Public Funding of Research into Ethnological Activities in Andalusia (Spain): Boosting the Academic Career of Researchers

The advent of democracy in Spain and the establishment of the different autonomous communities marked the beginning of a process to transfer political, economic and other competences over Culture and Cultural Heritage. Following its creation in 1984, the Ministry of Culture of the Andalusian Autonomous Government incorporated a Directorate-General for Cultural Assets into its organisational structure and embarked on an ambitious programme of actions to support Andalusian historical heritage, including creation of a management structure, enactment of a specific heritage law and budget allocations for protection tasks. From the outset, a type of heritage little known until then emerged: ethnological heritage. Dynamic actions were also promoted to fund research into this area, including grants for ethnological activities, financing for publications and funding for ethnological symposiums. This paper analyses the different ethnological activities carried out and their funding, and assesses the extent to which this investment favoured the professional development of teaching staff in the field of Social Anthropology in Andalusia, specifying the marginal effects and differentiating them according to gender and university size using binary choice models (Logit).

A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

ITERATIONS OF DEPENDENT RANDOM MAPS AND EXOGENEITY IN NONLINEAR DYNAMICS

We discuss the existence and uniqueness of stationary and ergodic nonlinear autoregressive processes when exogenous regressors are incorporated into the dynamic. To this end, we consider the convergence of the backward iterations of dependent random maps. In particular, we give a new result when the classical condition of contraction on average is replaced with a contraction in conditional expectation. Under some conditions, we also discuss the dependence properties of these processes using the functional dependence measure of Wu (2005, Proceedings of the National Academy of Sciences 102, 14150–14154) that delivers a central limit theorem giving a wide range of applications. Our results are illustrated with conditional heteroscedastic autoregressive nonlinear models, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes, count time series, binary choice models, and categorical time series for which we provide many extensions of existing results.

Dilemma Zone: Modeling Drivers’ Decision at Signalized Intersections against Aggressiveness and Other Factors Using UAV Technology

Intersection safety and drivers’ behavior are strongly interrelated, especially when the latter are located in dilemma zone. This paper explores, among others, the main factors affecting driver behavior, such as distance to stop line, approaching speed and acceleration/deceleration, and two additional factors, namely, driver’s aggressiveness and driver’s relative position at the onset of the yellow signal. Field data were collected using unmanned aerial vehicle (UAV) technology. Two binary choice models were developed, the first relying on observed data and the latter enriched by the latent factor drivers’ aggressiveness and the vehicles’ relative position. Drivers were classified to aggressive and non-aggressive ones using a latent class model that combined approaching speed and acceleration/deceleration data. Drivers were further grouped according to their expected reaction/decision to stop or cross the intersection in relation to their relative position. Both models equally explain drivers’ decisions adequately, but the second one offers additional explanatory power attributed to aggressiveness. Being able to identify the level of aggressiveness among the drivers enables the calculation of the probability that drivers will cross the intersection even if caught in a dilemma zone or in a zone in which the obvious decision is to stop. Such findings can be valuable when designing a signalized intersection and the traffic time settings, as well as the posted speed limit.

The Empirical Content of Binary Choice Models

An important goal of empirical demand analysis is choice and welfare prediction on counterfactual budget sets arising from potential policy interventions. Such predictions are more credible when made without arbitrary functional‐form/distributional assumptions, and instead based solely on economic rationality, that is, that choice is consistent with utility maximization by a heterogeneous population. This paper investigates nonparametric economic rationality in the empirically important context of binary choice. We show that under general unobserved heterogeneity, economic rationality is equivalent to a pair of Slutsky‐like shape restrictions on choice‐probability functions. The forms of these restrictions differ from Slutsky inequalities for continuous goods. Unlike McFadden–Richter's stochastic revealed preference, our shape restrictions (a) are global, that is, their forms do not depend on which and how many budget sets are observed, (b) are closed form, hence easy to impose on parametric/semi/nonparametric models in practical applications, and (c) provide computationally simple, theory‐consistent bounds on demand and welfare predictions on counterfactual budge sets.

Feeling the Heat

Climate change is an existential threat to the world economy like no other, with complex, evolving and nonlinear dynamics that remain a source of great uncertainty. There is a bourgeoning literature on the economic impact of climate change, but research on how climate change affects sovereign risks is limited. Building on our previous research focusing on the impact of climate change on sovereign risks, this paper empirically investigates how climate change may affect sovereign credit ratings. By means of binary-choice models, we find that climate change vulnerability has adverse effects on sovereign credit ratings, after controlling for conventional macroeconomic determinants of credit worthiness. On the other hand, with regards to climate change resilience, we find that countries with greater climate change resilience benefit from higher (better) credit ratings. These findings, robust to a battery of sensitivity checks, also show that impact of climate change is disproportionately greater in developing countries due largely to weaker capacity to adapt to and mitigate the consequences of climate change.