The Impact of Taking Parental Leave on Fathers’ Participation In Childcare And Relationships With Children: Lessons from Sweden

In Sweden, government-mandated paid parental leave has been available to both mothers and fathers since 1974. By 2006, each parent had two non-transferable leave months and nine additional months to share. From the beginning, parental leave was presented as a policy designed to promote gender equality, with women and men having equal opportunities and responsibilities to contribute economically to the family and care for children. Sweden thus provides a unique setting to explore whether social policy can be an important instrument for changing the gender contract. Analysing survey data from 356 fathers working in large private companies, we found that the amount of parental leave days taken had positive effects on several aspects of fathers’ participation in childcare and on their satisfaction with contact with children, controlling for other factors contributing to fathers’ participation in childcare. Our findings suggest that the full potential of Sweden's parental leave policy for degendering the division of labour for childcare will not likely be met until fathers are strongly encouraged by social policy to take a more equal portion of parental leave.     

The Sociology of Expertise: The Distribution of Social Fluency

Expert knowledge is an essential component of modern society. It is also a potentially difficult topic for sociology because of the importance sociology attaches to culture and context. The sociology of science has emphasised the continuity between scientific expertise and more traditional forms of knowledge. Whilst this suggests an increasing 'democratisation of expertise' is desirable, it also risks erasing the idea of expertise itself. This might be particularly detrimental for sociology as it restricts the role of sociological inquiry to examining how expert status is attributed rather than understanding what expertise is. This paper describes these developments and contrasts them with other approaches in which expertise appears less important. It concludes by setting out a new approach to expertise that respects the role of culture in generating knowledge but, by stressing the importance of socialisation and experience, argues for a more nuanced conception of expertise as both real and unequally distributed.     

An Experimental Evaluation of the Serial Cost Sharing Rule

This paper proposes an experimental test to evaluate the performance of the serial cost sharing rule, originally proposed by Shenker [Sigmetrics, 241–242 (1990)] and then analyzed by Moulin and Shenker [Econometrica 60, 1009–1037 (1992)]. We report measures of the performance and efficiency of the serial mechanism by comparing the choices and payoffs attained by the subjects to the expected equilibrium allocations. Experimental evidence shows that learning is needed for the subjects to converge to the equilibrium strategy. However, in terms of efficiency, the serial mechanism leads to almost efficient allocations.     

New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.