The intergenerational transmission of human capital: the role of skills and health

We provide new evidence on some of the mechanisms reflected in the intergenerational transmission of human capital. Applying both an adoption and a twin design to rich data from the Swedish military enlistment, we show that greater parental education increases sons’ cognitive and non-cognitive skills, as well as their health. The estimates are in many cases similar across research designs and suggest that a substantial part of the effect of parental education on their young adult children’s human capital works through improving their skills and health.     

Perception of own death risk

Individuals’ perception of their own road-traffic and overall mortality risks are examined in this paper. Perceived risk is compared with the objective risk of the respondents’ peers, i.e. their own gender and age group, and the results suggest that individuals’ risk perception of their own risk is biased. For road-traffic risk we obtain similar results to what have been found previously in the literature, overassessment and underassessment among low- and high-risk groups, respectively. For overall risk we find that all risk groups underestimate their risk. The results also indicate that men's risk bias is larger than women’s.

Body Size,Skills, and Income: Evidence From 150,000 Teenage Siblings

We provide new evidence on the long-run labor market penalty of teenage overweight and obesity using unique and large-scale data on 150,000 male siblings from the Swedish military enlistment. Our empirical analysis provides four important results. First, we provide the first evidence of a large adult male labor market penalty for being overweight or obese as a teenager. Second, we replicate this result using data from the United States and the United Kingdom. Third, we note a strikingly strong within-family relationship between body size and cognitive skills/noncognitive skills. Fourth, a large part of the estimated body-size penalty reflects lower skill acquisition among overweight and obese teenagers. Taken together, these results reinforce the importance of policy combating early-life obesity in order to reduce healthcare expenditures as well as poverty and inequalities later in life.     

Do They Know What They are Doing? Risk Perceptions and Smoking Behaviour Among Swedish Teenagers

Cross-sectional survey data on Swedish adolescents aged 12–18 was used to analyse perceived risks of smoking-related lung cancer, the determinants of these risk perceptions, and how these perceptions related to smoking behaviour. Three major conclusions were drawn: (1) that both smokers and non-smokers overestimated the risks of lung cancer, (2) that these risk perceptions fell substantially with age, but nevertheless implied risk overestimation, and (3) that individuals with higher perceived risks were less likely to be smokers but that risk beliefs had no effect on the number of cigarettes smoked.     

More Large Accidents Through Complexity

Many would tend to characterize modern society as steeped in complexity. This complexity is seen by a number of professional people to imply risk-taking--or risk-making--on a high level. "A single mistake may give consequences of quite different proportions from earlier times," as one critic puts it. This hypothesis of increasing risks on a high level--of worst things getting worse--has been tested on fires in Sweden. Fire has always been a powerfully upsetting agent to the social fabric. Today industrial fires dominate the picture economically. The cost of industrial fires is about three times the cost of residential fires in Sweden. It is of interest to note that the damage cost of the worst fires (as measured in insurance payments) does not form an increasing part of overall fire damage costs, as one would expect from the above hypothesis. Whether the hypothesis holds with regard to indirect costs due to production stand-still is more uncertain. We can conclude that the potential of complexity to create large abnormal occurrences may have been somewhat prematurely announced--at least with regard to fires in Sweden.     

Parameter estimation for partially observed hypoelliptic diffusions

Summary.  Hypoelliptic diffusion processes can be used to model a variety of phenomena in applications ranging from molecular dynamics to audio signal analysis. We study parameter estimation for such processes in situations where we observe some components of the solution at discrete times. Since exact likelihoods for the transition densities are typically not known, approximations are used that are expected to work well in the limit of small intersample times Δ t and large total observation times N  Δ t . Hypoellipticity together with partial observation leads to ill conditioning requiring a judicious combination of approximate likelihoods for the various parameters to be estimated. We combine these in a deterministic scan Gibbs sampler alternating between missing data in the unobserved solution components, and parameters. Numerical experiments illustrate asymptotic consistency of the method when applied to simulated data. The paper concludes with an application of the Gibbs sampler to molecular dynamics data.     

Boosted coefficient models

Regression methods typically construct a mapping from the covariates into the real numbers. Here, however, we consider regression problems where the task is to form a mapping from the covariates into a set of (univariate) real-valued functions. Examples are given by conditional density estimation, hazard regression and regression with a functional response. Our approach starts by modeling the function of interest using a sum of B-spline basis functions. To model dependence on the covariates, the coefficients of this expansion are each modeled as functions of the covariates. We propose to estimate these coefficient functions using boosted tree models. Algorithms are provided for the above three situations, and real data sets are used to investigate their performance. The results indicate that the proposed methodology performs well. In addition, it is both straightforward, and capable of handling a large number of covariates.