Circadian Rhythms,Sleep, and Cognitive Skills: Evidence From an Unsleeping Giant

This study analyzes the effects of sleep duration on cognitive skills and depression symptoms of older workers in urban China. Cognitive skills and mental health have been associated with sleep duration and are known to be strongly related to economic behavior and performance. However, causal evidence is lacking, and little is known about sleep deprivation in developing countries. We exploit the relationship between circadian rhythms and bedtime to identify the effects of sleep using sunset time as an instrument. Using the Chinese Health and Retirement Longitudinal Study, we show that a later sunset time significantly reduces sleep duration and that sleep duration increases cognitive skills and eases depression symptoms of workers aged 45 years and older. The results are driven by employed individuals living in urban areas, who are more likely to be constrained by rigid work schedules. We find no evidence of significant effects on the self-employed, non-employed, or farmers.     

A Bootstrap Likelihood Approach to Bayesian Computation

There is an increasing amount of literature focused on Bayesian computational methods to address problems with intractable likelihood. One approach is a set of algorithms known as Approximate Bayesian Computational (ABC) methods. One of the problems with these algorithms is that their performance depends on the appropriate choice of summary statistics, distance measure and tolerance level. To circumvent this problem, an alternative method based on the empirical likelihood has been introduced. This method can be easily implemented when a set of constraints, related to the moments of the distribution, is specified. However, the choice of the constraints is sometimes challenging. To overcome this difficulty, we propose an alternative method based on a bootstrap likelihood approach. The method is easy to implement and in some cases is actually faster than the other approaches considered. We illustrate the performance of our algorithm with examples from population genetics, time series and stochastic differential equations. We also test the method on a real dataset.     

Soccer Scores,Short-Term Mood and Fertility

European Journal of Population - Previous research has shown that seemingly irrelevant events such as unexpected outcomes in sporting events can affect mood and have relevant consequences for...     

Stochastic Evolution of Rules for Playing Finite Normal Form Games

The evolution of boundedly rational rules for playing normal form games is studied within stationary environments of stochastically changing games. Rules are viewed as algorithms prescribing strategies for the different normal form games that arise. It is shown that many of the “folk results” of evolutionary game theory, typically obtained with a fixed game and fixed strategies, carry over to the present environments. The results are also related to some recent experiments on rules and games.      

THE POLITICAL COLOR OF FISCAL RESPONSIBILITY

We propose a dynamic general equilibrium model that yields testable implications about the fiscal policy run by governments of different political color. Successive generations of voters choose taxation, expenditure, and government debt through repeated elections. Voters are heterogeneous by age and by the intensity of their preferences for public good provision. The political equilibrium switches stochastically between left‐ (pro‐public goods) and right‐leaning (pro‐private consumption) governments. A shift to the left (right) is associated with a fall (increase) in government debt, an increase (fall) in taxation, and an increase (fall) in government expenditures. However, left‐leaning governments engage in more debt accumulation during recessions. These predictions are shown to be consistent with the time‐series evidence for the United States in the postwar period, and also with the evidence for a panel of OECD countries. (JEL: D72, E62, H41, H62, H63)     

Local labour markets delineation: an approach based on evolutionary algorithms and classification methods

In this paper a methodology for the delineation of local labour markets (LLMs) using evolutionary algorithms is proposed. This procedure, based on that in Flórez-Revuelta et al. [13 F. Flórez-Revuelta, J.M. Casado-Díaz, and L. Martínez-Bernabeu, An evolutionary approach to the delineation of functional areas base on travel-to-work flows, Int. J. Autom. Comput. 5(1) (2008), pp. 10–21. doi: 10.1007/s11633-008-0010-6[Crossref] , [Google Scholar],14 F. Flórez-Revuelta, J.M. Casado-Díaz, L. Martínez-Bernabeu, and R. Gómez-Hernández, A memetic algorithm for the delineation of local labour markets, in Parallel Problem Solving from Nature X, Vol. 5199, Lecture Notes in Computer Science, G. Rudolph, T.H. Jansen, S.M. Lucas, C. Poloni, and N. Beume, eds., Springer, Berlin, 2008, pp. 1011–1020. [Google Scholar]], introduces three modifications. First, initial groups of municipalities with a minimum size requirement are built using the travel time between them. Second, a not fully random initiation algorithm is proposed. And third, as a final stage of the procedure, a contiguity step is implemented. These modifications significantly decrease the computational times of the algorithm (up to a 99%) without any deterioration of the quality of the solutions. The optimization algorithm may give a set of potential solutions with very similar values with respect to the objective function what would lead to different partitions, both in terms of number of markets and their composition. In order to capture their common aspects an algorithm based on a cluster partitioning of k-means type is presented. This stage of the procedure also provides a ranking of LLMs foci useful for planners and administrations in decision-making processes on issues related to labour activities. Finally, to evaluate the performance of the algorithm a toy example with artificial data is analysed. The full methodology is illustrated through a real commuting data set of the region of Aragón (Spain).     

Semi-parametric Bayesian Analysis of the Proportional Hazard Rate Model An Application to the Effect of Training Programs on Graduate Unemployment

In this paper, we introduce a semi-parametric Bayesian methodology based on the proportional hazard model that assumes that the baseline hazard function is constant over segments but, by contrast to what is usually assumed in the literature, with the periods at which the function changes not being specified in advance. The methodology is applied to explore the impact of Vocational Training courses offered by the University of Zaragoza (Spain) on the duration of the initial periods of unemployment experienced by graduate leavers. The framework is very flexible and allows us, in particular, to capture the presence of seasonality in the job insertion of graduates.     

Concentration function and sensitivity to the prior

Summary In robust bayesian analysis, ranges of quantities of interest (e. g. posterior means) are usually considered when the prior probability measure varies in a class Γ. Such quantities describe the variation of just one aspect of the posterior measure. The concentration function describes changes in the posterior probability measure more globally, detecting differences in probability concentration and providing, simultaneously, bounds on the posterior probability of all measurable subsets. In this paper, we present a novel use of the concentration function, and two concentration indices, to study such posterior changes for a general class Γ, restricting then our attention to some ∈-contamination classes of priors.     

Optimal scale in the Italian gas distribution industry using data envelopment analysis

The Italian gas distribution industry presents a high degree of fragmentation. However, the tendency between 1970 and 1998 was a concentration process. Available evidence supports the thesis that local distributors have undertaken a process of scale enlargement. This raises the question of characteristics of returns to scale for such operators as well as the optimal scale at which they should operate. Returns to scale are analysed by data envelopment analysis (DEA) methodology. The results show that the output space in which DMUs attain a high level of scale efficiency is widespread, thus indicating an unexpected return to scale characterisation. Technology shows increasing returns only for the smallest units, but such an effect is rapidly exhausted in favour of a regime of constant returns to scale. The main managerial conclusion is that an improvement of productivity may be reached via an intensification of the merging process involving local distributors operating at a small scale. Moreover, the concentration process appears as an “attainable” objective since the critical dimension, which permits the exploitation of positive returns to scale, is quite small.