F-LAN is a geospatial data set that documents hundreds of coastal lights that guided ships around France from medieval times to 1929. F-LAN provides visibility range for individual lights. The authors collected all data from scholarly literature, historical coastal navigational charts, and official lighthouse surveys. F-LAN allows users to track the provision of coastal lighting over time. It complements the existing LAN dataset for England and Wales. Thanks to F-LAN it is now possible to map and analyse light coverage for France over two centuries. It can be used to analyse coastal routing, infrastructure investment and the relationship between accidents and the provision of light in any given area.

Foundational debates about public service provision originate with the study of private lighthouses in England and Wales. We provide a new empirical assessment of cost and technical efficiency of competing lighthouse organizations in the early 1800s. Those with more private control charged ships higher fees and had greater operating costs. Lights with more local representation and funding provided lights of more local use and were most cheaply maintained. Our results help explain why government promoted nonprofit organizations to run lighthouses over private operators. We provide new insights into the role of private enterprise and nonprofit organizations in public service provision.

Michael Jacobs and Andrew Hindmoor’s analysis of ‘Labour, left and right’ is a salutary corrective to ‘electoral-ideological’ accounts of party strategy in Britain, and rightly urges scholars to pay more attention to substantive ‘policy reasoning’. Jacobs and Hindmoor’s account of Labour policy is only partly convincing, however, because it is based on a sharp distinction between left-wing ‘structural reform’ and moderate ‘redistributive’ strategies which is difficult to justify historically, and understates the importance of social policy commitments to Labour’s positioning. This article argues that Labour’s policy trajectory since the 1980s is better understood through a fiscal lens, which reflects the importance of costings debates in UK general election campaigns and of Shadow Chancellors in opposition policy-making. Shifts in Labour’s positioning can thus be explained by looking at the interplay between the party’s economic thought, the leadership’s perception of its electoral needs, and the changing budgetary context.

This paper draws parallels between the principal components analysis of factorless high-dimensional nonstationary data and the classical spurious regression. We show that a few of the principal components of such data absorb nearly all the data variation. The corresponding scree plot suggests that the data contain a few factors, which is corroborated by the standard panel information criteria. Furthermore, the Dickey–Fuller tests of the unit root hypothesis applied to the estimated “idiosyncratic terms” often reject, creating an impression that a few factors are responsible for most of the nonstationarity in the data. We warn empirical researchers of these peculiar effects and suggest to always compare the analysis in levels with that in differences.

A test for time-varying correlation is developed within the framework of a dynamic conditional score (DCS) model for both Gaussian and Student t-distributions. The test may be interpreted as a Lagrange multiplier test and modified to allow for the estimation of models for time-varying volatility in the individual series. Unlike standard moment-based tests, the score-based test statistic includes information on the level of correlation under the null hypothesis and local power arguments indicate the benefits of doing so. A simulation study shows that the performance of the score-based test is strong relative to existing tests across a range of data generating processes. An application to the Hong Kong and South Korean equity markets shows that the new test reveals changes in correlation that are not detected by the standard moment-based test.

Recently, the Dynamic Conditional Score (DCS) or Generalized Autoregressive Score (GAS) time series models have attracted considerable attention. This motivates the need for a software package to estimate and evaluate these new models. A straightforward to operate program called the Dynamic Score (DySco) package is introduced for estimating models for positive variables, in which the location/scale evolves over time. Its capabilities are demonstrated using a financial application.

We build a dynamic model where the economy is vulnerable to belief-driven slow-moving debt crises at intermediate debt levels, and rollover crises at both low and high debt levels. Vis-à-vis the threat of slow-moving crises, countercyclical deficits generally welfare-dominate debt reduction policies. In a recession, optimizing governments only deleverage if debt is close to the threshold below which belief-driven slow-moving crises can no longer occur. The welfare benefits from deleveraging instead dominate if governments are concerned with losing market access even at low debt levels. Long bond maturities may fully eliminate belief-driven rollover crises but not slow-moving ones.

Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses, update their own guess, and so forth. We study learning dynamics in three large-scale networks capturing features of real-world social networks: Erdös–Rényi, Stochastic Block (reflecting network homophily), and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These patterns are consistent with the predictions of DeGroot updating. It lends support to the notion that the use of simple heuristics in information aggregation is prevalent in large and complex networks.

The ongoing global race for bigger and better artificial intelligence (AI) systems is expected to have a profound societal and environmental impact by altering job markets, disrupting business models, and enabling new governance and societal welfare structures that can affect global consensus for climate action pathways. However, the current AI systems are trained on biased datasets that could destabilize political agencies impacting climate change mitigation and adaptation decisions and compromise social stability, potentially leading to societal tipping events. Thus, the appropriate design of a less biased AI system that reflects both direct and indirect effects on societies and planetary challenges is a question of paramount importance. In this paper, we tackle the question of data-centric knowledge generation for climate action in ways that minimize biased AI. We argue for the need to co-align a less biased AI with an epistemic web on planetary health challenges for more trustworthy decision-making. A human-in-the-loop AI can be designed to align with three goals. First, it can contribute to a planetary epistemic web that supports climate action. Second, it can directly enable mitigation and adaptation interventions through knowledge of social tipping elements. Finally, it can reduce the data injustices associated with AI pretraining datasets.

Due to the unprecedented burdens on public health, agriculture, and other socio-economic and cultural systems, climate change-induced heatwaves in India can hinder or reverse the country’s progress in fulfilling the sustainable development goals (SDGs). Moreover, the Indian government’s reliance on its Climate Vulnerability Index (CVI), which may underestimate the impact of heatwaves on the country’s developmental efforts. An analytical evaluation of heat index (HI) with CVI shows that more than 90% of the country is at extremely cautious or dangerous levels of adversely impacting adaptive livelihood capacity, food grains yield, vector-borne disease spread and urban sustainability. The results also show by examining Delhi’s urban heat risk that heatwaves will critically hamper SDG progress at the urban scale. Linking HI with CVI identifies more of India’s vulnerability and provides an opportunity to rethink India’s climate adaptation policies through international cooperation in designing holistic vulnerability assessment methodologies. The conclusion emphasizes the urgent need to improve extreme weather impact assessment by combining multiple layers of information within the existing climate vulnerability measurement frameworks that can account for the co-occurrence and collision of climate change events and non-climate structural SDG interventions.