ABSTRACT Stillwagon (this issue) tests extensively for structural breaks in a dynamic Taylor rule for US data and finds no breaks over 2001–2025, whether using Bai–Perron tests or multiplicative indicator saturation. The current paper complements and builds on Stillwagon by focusing on features specific to three Fed chairs (Bernanke, Yellen, and Powell) during 2006–2025, and on the zero lower bound. The Taylor rule is chair‐specific; and the zero lower bound affects the Taylor rule for Yellen's and Powell's tenures. These economically significant structural breaks are detectable by multiplicative indicator saturation that focuses on those features, whereas the broad‐spectrum multiplicative indicator saturation in Stillwagon misses these breaks. Such structural breaks limit the interpretability and usefulness of the Taylor rule for monetary policy.
We measure teacher effects by their ability to raise test scores in high-stakes examinations. We present estimates for value-added teacher effects from public high schools, while prior work in developing countries has focused on primary and middle schools or private high schools. Our data come from 22 schools and represent the cohort of public-school students in Delhi who took their end of grade 12 examinations in 2016. We estimate teacher effects using within-student and across-subject variation in test scores, and additionally account for contemporaneous student inputs. We find substantial variation in teacher quality that contradicts the perception that most public-school teachers are ineffective: over the course of two years, namely grades 11 and 12, being assigned a one standard deviation better than average teacher from the same school moves a student at the median to the 63rd percentile of their cohort's test score distribution. Furthermore, being female is the only teacher attribute that positively predicts teacher effectiveness; training, experience, and personality traits are uncorrelated with teacher effectiveness. Lastly, we find low correlation between value-added teacher effects and principals' ratings of teachers implying that principals' ratings cannot function as a proxy for value-added estimates when deciding teacher compensation.
This article presents a semiparametric stochastic frontier model that accounts for unobserved spatial heterogeneity in inefficiency by incorporating a stationary Gaussian random field based on geographic coordinates into the inefficiency scaling function. This approach captures spatial patterns in inefficiency at a fine scale. The proposed model is validated using simulated data and applied, within a Bayesian framework, to a panel dataset of Dutch dairy farms with precise geographic coordinates. For comparison, several alternative specifications are also estimated, excluding the spatial component, the inefficiency covariates, or both. The results confirm the presence of spatial heterogeneity and indicate that spatial dependence in inefficiency persists over relatively long distances. The estimated spatial effects reveal variation across locations, including areas with relatively higher and lower inefficiency. Model comparisons further show that omitting the spatial component and/or the scaling covariates affects the distribution of inefficiency across observations and leads to a worse model fit.
We analyse growth vulnerability in the four largest Euro Area (EA) economies, measured as a lower quantile of the growth distribution conditional on EA-wide and country-specific macroeconomic/financial factors. Growth densities are obtained under a normal activity scenario and under stressed conditions. We find that vulnerability is higher in Germany, more exposed to EA-wide conditions, and in Spain, with large country-specific dynamics. Under stress, financial factors amplify adverse macroeconomic conditions, and even severe individual shocks fail to fully explain the vulnerability observed under systemic stress. Our results underscore the importance of monitoring local and EA-wide macro-financial conditions to mitigate vulnerability.
The 2016 EU referendum result was widely perceived as a statement against immigration. This paper investigates whether information about majority voting outcomes influences attitudes towards immigrants. Using a survey experiment conducted in England in 2017-2018, we randomly exposed participants to truthful information indicating that local constituencies had predominantly voted either to Leave or to Remain in the 2016 EU referendum. The intervention substantially shifted beliefs about local Brexit vote shares. Although we find no statistically significant effects on most individual survey questions, exposure to a Leave-majority signal leads to more restrictive immigration policy preferences relative to exposure to a Remain-majority signal. Effects are concentrated among participants for whom the information was politically dissonant or unexpected. Overall, the pattern of results is consistent with a conformity mechanism operating through perceived majority views, suggesting that referenda may continue to shape expressed policy preferences beyond their immediate aftermath.
