The association between commuting and cardiovascular disease: A biomarker-based analysis of cross-sectional cohort data from the UK Biobank.

This study used cross-sectional UK Biobank data to estimate the influence of active and passive commuting modes and commuting distance on cardiovascular disease (CVD) -related biomarkers as measures of health outcomes. The analysis applied logistic regression to assess the risk of exhibiting individual biomarker values outside a predefined reference interval and standard linear regression to estimate the relation between commuting practices and a composite CVD index. The study sample comprised 208,893 UK Biobank baseline survey participants aged 40 to 69 who use various modes of transport to commute to work at least once a week. Participants were recruited and interviewed between 2006 and 2010 at 22 centers geographically dispersed across England, Scotland, and Wales. The data set included these participants' sociodemographic and health-related information, including lifestyle indicators and biological measures. The primary outcome was a shift from low to high-risk blood serum levels in eight cardiovascular biomarkers: total cholesterol, low density lipoprotein, high density lipoprotein, triglycerides, apolipoprotein A and B, C-reactive protein, and lipoprotein (a). Our results indicated a small negative association between the composite risk index for CVD biomarkers and weekly commuting distance. Although estimates for active commuting modes (cycling, walking) may admittedly be sensitive to different covariate adjustments, our specifications show them to be positively associated with select CVD biomarkers. Commuting long distances by car is negatively associated with CVD-related biomarkers, while cycling and walking might be positively associated. This biomarker-based evidence, although limited, is less susceptible to residual confounding than that from distant outcomes like CVD mortality.

The effect of macroscopic herd inputs on individual investment behaviour.

Decisions are rarely made in isolation and the role of others' decisions in guiding our own has been observed in a diversity of contexts. This influence is often argued to result from an information cascade, where decisions in a sequential setting are influenced by the early decisions of others. However, the degree to which individuals modify behaviour through the integration of social information (i.e., other people's decisions) varies considerably. While significant literature has been dedicated to understanding individual determinants for this variation, we propose that we should not ignore the aggregate characteristics of the herd itself. Specifically, we examine whether the scale and longevity of the herd itself at the time when an individual decides, defined as macroscopic herd inputs, influence whether individuals integrate social information. By employing data from a social trading platform, we find that macroscopic herd inputs exert a strong influence on individual investment decisions, showing that the influence of others' behaviour on our own is in part dependent on the nature of the herd itself.

Using machine learning to uncover the relation between age and life satisfaction.

This study applies a machine learning (ML) approach to around 400,000 observations from the German Socio-Economic Panel to assess the relation between life satisfaction and age. We show that with our ML-based approach it is possible to isolate the effect of age on life satisfaction across the lifecycle without explicitly parameterizing the complex relationship between age and other covariates-this complex relation is taken into account by a feedforward neural network. Our results show a clear U-shape relation between age and life satisfaction across the lifespan, with a minimum at around 50 years of age.

Why are Chinese workers so unhappy? A comparative cross-national analysis of job satisfaction, job expectations, and job attributes.

Using data from the 2015 International Social Survey Program (ISSP), this study conducts a multinational comparison of job satisfaction determinants and their drivers in 36 countries and regions, with particular attention to the reasons for relatively low job satisfaction among Chinese workers. Based on our results from a Blinder-Oaxaca decomposition analysis, we attribute a substantial portion of the job satisfaction differences between China and the other countries to different job attributes and expectations; in particular, to unmet job expectations for interesting work, high pay, and opportunities for advancement. We also note that, contrary to common belief, Chinese workers value similar attributes as Western workers but perceive their work conditions as very different from those in the West.

The effectiveness of a population-based skin cancer screening program: evidence from Germany.

In this paper, we analyze how a nationwide population-based skin cancer screening program (SCS) implemented in Germany in 2008 has impacted the number of hospital discharges following malignant skin neoplasm diagnosis and the malignant melanoma mortality rate per 100,000 inhabitants. Our panel data, drawn from the Eurostat database, cover subregions in 22 European countries, measured at the lowest nomenclature of territorial units for statistics (NUTS) level for 2000-2013. Applying fixed effects methods, we find a significantly positive and robust effect of the German SCS on the number of patients diagnosed with malignant skin neoplasm. However, the program does not significantly influence the melanoma mortality rate. This finding conflicts with the decreased melanoma mortality rate found for the pilot SCS program in northern Germany. Our results indicate that Germany's nationwide SCS program is effective in terms of a higher diagnosis rate for malignant skin neoplasms and thus may contribute to an improvement in the early detection of skin cancer.

Smoking and local unemployment: Evidence from Germany.

In this paper, we use data from the German Socio-Economic Panel to investigate the effect of macro-economic conditions (in the form of local unemployment rates) on smoking behavior. The results from our panel data models, several of which control for selection bias, indicate that the propensity to become a smoker increases significantly during an economic downturn, with an approximately 0.7 percentage point increase for each percentage point rise in the unemployment rate. Conversely, conditional on the individual being a smoker, cigarette consumption decreases with rising unemployment rates, with a one percentage point increase in the regional unemployment rate leading to a decrease in consumption up to 0.8 percent.