Most studies assessing animal decision-making under risk rely on probabilities that are typically larger than 10%. To study Decision-Making in uncertain conditions, we explore a novel experimental and modelling approach that aims at measuring the extent to which rats are sensitive - and how they respond - to outcomes that are both rare (probabilities smaller than 1%) and extreme in their consequences (deviations larger than 10 times the standard error). In a four-armed bandit task, stochastic gains (sugar pellets) and losses (time-out punishments) are such that extremely large - but rare - outcomes materialize or not depending on the chosen options. All rats feature both limited diversification, mixing two options out of four, and sensitivity to rare and extreme outcomes despite their infrequent occurrence, by combining options with avoidance of extreme losses (Black Swans) and exposure to extreme gains (Jackpots). Notably, this sensitivity turns out to be one-sided for the main phenotype in our sample: it features a quasi-complete avoidance of Black Swans, so as to escape extreme losses almost completely, which contrasts with an exposure to Jackpots that is partial only. The flip side of observed choices is that they entail smaller gains and larger losses in the frequent domain compared to alternatives. We have introduced sensitivity to Black Swans and Jackpots in a new class of augmented Reinforcement Learning models and we have estimated their parameters using observed choices and outcomes for each rat. Adding such specific sensitivity results in a good fit of the selected model - and simulated behaviors that are close - to behavioral observations, whereas a standard Q-Learning model without sensitivity is rejected for almost all rats. This model reproducing the main phenotype suggests that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.

BACKGROUND:It is commonly believed that Africa largely evaded the worst of the COVID-19 pandemic, with fewer cases than other continents. However, regional comparisons that ignore differences in testing intensity may misrepresent dynamics. Studying the spread and case-fatality relationship during COVID-19 across WHO regions requires explicitly adjusting for time-varying test volumes. METHODS:We build a weekly panel dataset spanning May 2020 to December 2021 for the WHO regions: Africa, Eastern Mediterranean, South-East Asia, the Americas, Western Pacific, and Europe. Data on tests, confirmed cases, and COVID-19-attributed deaths were sourced from Our World in Data. We apply a novel metric that corrects for fluctuating test volumes to quantify week-to-week acceleration in infections and in mortality. We then compare the frequency, magnitude, and timing of these acceleration episodes across regions. RESULTS:Accounting for testing dynamics, we show that Africa exhibits multiple infection-acceleration episodes whose magnitude and frequency match those in other regions. Mortality accelerations in Africa closely follow infection surges, with an average lag of ten weeks. A positive correlation between infection acceleration in Africa and the Americas further indicates synchrony. These findings hold when using a larger secondary dataset of 140 countries. CONCLUSIONS:Contrary to prevailing assumptions, Africa was not spared from the pandemic's severe dynamics. Infection surges were on par with those elsewhere and were followed by mortality accelerations. These results underscore that accounting for testing variability is essential to accurately assess pandemic progression, and they highlight the urgent need to strengthen surveillance and healthcare capacity across all regions.

We provide a novel way to correct the effective reproduction number for the time-varying amount of tests, using the acceleration index (Baunez et al., 2021) as a simple measure of viral spread dynamics. Not correcting results in the reproduction number being a biased estimate of viral acceleration and we provide a formal decomposition of the resulting bias, involving the useful notions of test and infectivity intensities. When applied to French data for the COVID-19 pandemic (May 13, 2020-October 26, 2022), our decomposition shows that the reproduction number, when considered alone, characteristically underestimates the resurgence of the pandemic, compared to the acceleration index which accounts for the time-varying volume of tests. Because the acceleration index aggregates all relevant information and captures in real time the sizable time variation featured by viral circulation, it is a more parsimonious indicator to track the dynamics of an infectious disease outbreak in real time, compared to the equivalent alternative which would combine the reproduction number with the test and infectivity intensities.

The interest rate at which US firms borrow funds has two features: (i) it moves in a countercycli-cal fashion and (ii) it is an inverted leading indicator of real economic activity: low interest rates today forecast future booms in GDP, consumption, investment, and employment. We show that a Kiyotaki-Moore model accounts for both properties when interest-rate movements are driven, in a significant way, by self-fulfilling belief shocks that redistribute income away from lenders and to borrowers during booms. The credit-based nature of such self-fulfilling equilibria is shown to be essential: the dynamic correlation between current loanable funds rate and future aggregate economic activity depends critically on the property that the interest rate is state-contingent. Bayesian estimation of our benchmark DSGE model on US data shows that the model driven by redistribution shocks results in a better fit to the data than both standard RBC models and Kiyotaki-Moore type models with unique equilibrium.

Most studies assessing animal decision-making under risk rely on probabilities that are typically larger than 10%. To study Decision-Making in uncertain conditions, we explore a novel experimental and modelling approach that aims at measuring the extent to which rats are sensitive - and how they respond - to outcomes that are both rare (probabilities smaller than 1%) and extreme in their consequences (deviations larger than 10 times the standard error). In a four-armed bandit task, stochastic gains (sugar pellets) and losses (time-out punishments) are such that extremely large - but rare - outcomes materialize or not depending on the chosen options. All rats feature both limited diversification, mixing two options out of four, and sensitivity to rare and extreme outcomes despite their infrequent occurrence, by combining options with avoidance of extreme losses (Black Swans) and exposure to extreme gains (Jackpots). Notably, this sensitivity turns out to be one-sided for the main phenotype in our sample: it features a quasi-complete avoidance of Black Swans, so as to escape extreme losses almost completely, which contrasts with an exposure to Jackpots that is partial only. The flip side of observed choices is that they entail smaller gains and larger losses in the frequent domain compared to alternatives. We have introduced sensitivity to Black Swans and Jackpots in a new class of augmented Reinforcement Learning models and we have estimated their parameters using observed choices and outcomes for each rat. Adding such specific sensitivity results in a good fit of the selected model - and simulated behaviors that are close - to behavioral observations, whereas a standard Q-Learning model without sensitivity is rejected for almost all rats. This model reproducing the main phenotype suggests that frequent outcomes are treated separately from rare and extreme ones through different weights in Decision-Making.

The paper studies the implication of initial beliefs and associated confidence on the system’s dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics and the evolution of endogenous variables in a small DSGE model with credit-constrained agents, in which rational expectations are replaced by constant-gain adaptive learning. We then examine how discretionary experimenting with new macroeconomic policies is affected by expectations that agents have in relation to these policies. More specifically, we show that a newly introduced macro-prudential policy that aims at making leverage counter-cyclical can lead to substantial increase in fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect information about the policy experiment. This is in the stark contrast to the effects of such policy under rational expectations.

The paper studies the implication of initial beliefs and associated confidence on the system’s dynamics under adaptive learning. We first illustrate how prior beliefs determine learning dynamics and the evolution of endogenous variables in a small DSGE model with credit-constrained agents, in which rational expectations are replaced by constant-gain adaptive learning. We then examine how discretionary experimenting with new macroeconomic policies is affected by expectations that agents have in relation to these policies. More specifically, we show that a newly introduced macro-prudential policy that aims at making leverage counter-cyclical can lead to substantial increase in fluctuations under learning, when the economy is hit by financial shocks, if beliefs reflect imperfect information about the policy experiment. This is in the stark contrast to the effects of such policy under rational expectations.

Even though much has been learned about the new pathogen SARS-CoV-2 since the beginning of the COVID-19 pandemic, a lot of uncertainty remains. In this paper we argue that what is important to know under uncertainty is whether harm accelerates and whether health policies achieve deceleration of harm. For this, we need to see cases in relation to diagnostic effort and not to look at indicators based on cases only, such as a number of widely used epidemiological indicators, including the reproduction number, do. To do so overlooks a crucial dimension, namely the fact that the best we can know about cases will depend on some welldefined strategy of diagnostic effort, such as testing in the case of COVID-19. We will present a newly developed indicator to observe harm, the acceleration index, which is essentially an elasticity of cases in relation to tests. We will discuss what efficiency of testing means and propose that the corresponding health policy goal should be to find ever fewer cases with an ever-greater diagnostic effort. Easy and low-threshold testing will also be a means to give back people’s sovereignty to lead their life in an “open” as opposed to “locked-down” society.

An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs-May 13 to October 25, 2020-our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59-68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19-28] age group was the lowest and is about half that of the [69-78]. In addition, we propose an algorithm to allocate tests among French "départements" (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.

An in-depth econometric analysis of US state-level data on an annual frequency, from 1976 to 2008, sheds new light on a plausible causal link between infrastructure investments, namely public spending on highways, and income inequality. This causal relationship is drawn out by using the number of seats in the US House of Representatives Committee on Appropriations (HRCA) as an instrument to identify quasi-random variations in state-level spending on highways. An exogenous pattern which emerges when a state gains an additional member to the HRCA is that it is allocated with new federal grants. This increase in federal transfers for infrastructure financing results in slashing of expenditures on highways and a crowding-out effect of federal funding for state investments on highways. Spending cuts on highways produced by a new HRCA member being attained by a state can unwittingly cause income inequality to rise over a short two-year time horizon. Similar challenges with decentralized development to finance infrastructure via federal transfers to state and sub-national governments may be encountered by other industrially advanced, emerging and low-income developing economies. US data over the mentioned period reveal a strong positive correlation with state spending on highways and wages paid for construction jobs. Suggestive evidence indicates that the construction sector also plays an important role in the transmission channel from a rise in state spending on highways to lowering income inequality, albeit during specific intervals, as opposed to on a long-term basis.

Objectives: This note provides an assessment of COVID-19 acceleration among groups with different vaccine status in France. Methods: We assess viral acceleration using a novel indicator introduced in Baunez et al. (2021). The acceleration index relates the percentage change of tests that have been performed on a given day to the percentage change in the associated positive cases that same day. We compare viral acceleration among vaccinated and unvaccinated individuals in France over the period May 31st-August 29, 2021. Results: Once the state of the epidemic within each groups is accounted for, it turns out that viral acceleration has since mid-July converged to similar levels among vaccinated and unvaccinated individuals in France, even though viral speed is larger for the latter group compared to the former. Conclusion: Our results call for an increasing testing effort for both vaccinated and unvaccinated individuals, in view of the fact that viral circulation is currently accelerating at similar levels for both groups in France.

The paper studies the quantitative implications of priors beliefs and confidence in the model with learning. We introduce a constant-gain adaptive learning into a medium scale DSGE model with credit-constrained agents. We estimate the model both under rational expectations and adaptive learning using Bayesian techniques and study to what extent prior beliefs determine the evolution of expectations and endogenous variables. We analyze how the introduction of new macroeconomic policies is affected by priors, their precision, and expectations related to these policies.

Tests are crucial to know about the number of people who have fallen ill with COVID-19 and to understand in real-time whether the dynamics of the pandemic is accelerating or decelerating. But tests are a scarce resource in many countries. The key but still open question is thus how to allocate tests across sub-national levels. We provide a data-driven and operational criterion to allocate tests efficiently across regions or provinces, with the view to maximize detection of people who have been infected. We apply our criterion to Italian regions and compute the shares of tests that should go to each region, which are shown to differ significantly from the actual distribution.

This paper investigates how different monetary policy designs alter the effect of carry trades on a host small open economy. Capital inflows are expansionary, leading the central bank to raise the interest rate, increasing carry trades' returns, and generating further capital inflows (carry trades' vicious circle). This paper shows how monetary authorities can mitigate or suppress this vicious circle, when agents do not have full information about the central bank's objectives. The best way to deal with the destabilizing effect of carry trades is to target both inflation and capital inflows.

This paper stresses a new channel through which global financial linkages contribute to the co-movement in economic activity across countries. We show in a two-country setting with borrowing constraints that international credit markets are subject to self-fulfilling variations in the world real interest rate. Those expectation-driven changes in the borrowing cost in turn act as global shocks that induce strong cross-country co-movements in both financial and real variables (such as asset prices, GDP, consumption, investment and employment). When firms around the world benefit from unexpectedly low debt repayments, they borrow and invest more, which leads to excessive supply of collateral and of loanable funds at a low interest rate, thus fueling a boom in both home and abroad. As a consequence, business cycles are synchronized internationally. Such a stylized model thus offers one way to rationalize both the existence of a world business-cycle component, documented by recent empirical studies through dynamic factor analysis, and the factor’s intimate link to global financial markets.

The radical uncertainty around the current COVID19 pandemics requires that governments around the world should be able to track in real time not only how the virus spreads but, most importantly, what policies are effective in keeping the spread of the disease under check. To improve the quality of health decision-making, we argue that it is necessary to monitor and compare acceleration/deceleration of confirmed cases over health policy responses, across countries. To do so, we provide a simple mathematical tool to estimate the convexity/concavity of trends in epidemiological surveillance data. Had it been applied at the onset of the crisis, it would have offered more opportunities to measure the impact of the policies undertaken in different Asian countries, and to allow European and North-American governments to draw quicker lessons from these Asian experiences when making policy decisions. Our tool can be especially useful as the epidemic is currently extending to lower-income African and South American countries, some of which have weaker health systems.

This note provides an early assessment of the reinforced measures to curb the COVID-19 pandemic in France, which include a curfew of selected areas and culminate in a second COVID-19-related lock-down that started on October 30, 2020 and is still ongoing. We analyse the change in virus propagation across age groups and across departements using an acceleration index introduced in Baunez et al. (2020). We find that while the pandemic is still in the acceleration regime, acceleration decreased notably with curfew measures and this more rapidly so for the more vulnerable population group, that is, for people older than 60. Acceleration continued to decline under lock-down, but more so for the active population under 60 than for those above 60. For the youngest population aged 0 to 19, curfew measures did not reduce acceleration but lock-down does. This suggests that if health policies aim at protecting the elderly population generally more at risk to suffer severe consequences from COVID-19, curfew measures may be effective enough. However, looking at the departmental map of France, we find that curfews have not necessarily been imposed in departements where acceleration was the largest.

We show that the acceleration index, a novel indicator that measures acceleration and deceleration of viral spread (Baunez et al. 2020a,b), is essentially a test-controlled version of the reproduction number. As such it is a more accurate indicator to track the dynamics of an infectious disease outbreak in real time. We indicate a discrepancy between the acceleration index and the reproduction number, based on the infectivity and test rates and we provide a formal decomposition of this difference. When applied to French data for the ongoing COVID-19 pandemic, our decomposition shows that the reproduction number consistently underestimates the resurgence of the pandemic since the summer of 2020, compared to the acceleration index which accounts for the time-varying volume of tests. From the comparison that we present here follows that the acceleration index is a sufficient statistic to track the pandemic's propagation, as it captures in real time the sizeable time variation featured by viral circulation.