Public Health, Democracy, and Transition: Global Evidence and Post-Communism.

Democracy is generally associated with governmental accountability, better public policy choices and public health. However, there is limited evidence about how political regime transition impacts public health. We use two samples of the states around the world to trace the impact of regime transition on public health: the first sample comprises 29 post-communist states, along with 20 consolidated democracies, for the period of 1970-2014; the second sample is a subsample of the same 29 post-communist states but only for the period of transition, 1990-2014. We find that the post-communist states experienced some decline in life expectancy in the first few years of transition (1990-1995). Yet, with a steady increase in the measure of democracy from 1995 onwards, life expectancy significantly improved and infant mortality decreased. Therefore, in the long run, democratization has had a positive impact on both the life expectancy and infant mortality of citizens of the post-communist states.

The process of deforestation in weak democracies and the role of Intelligence.

This article examines the interconnection between national intelligence, political institutions, and the mismanagement of public resources (deforestations). The paper examines the reasons for deforestation and investigates the factors accountable for it. The analysis builds on authors-compiled cross-national dataset on 185 countries over the time period of twenty years, from 1990 to 2010. We find that, first, nation's intelligence reduces significantly the level of deforestation in a state. Moreover, the nations' IQ seems to play an offsetting role in the natural resource conservation (forest management) in the countries with weak democratic institutions. The analysis also discovered the presence of the U-shaped relationship between democracy and deforestation. Intelligence sheds more light on this interconnection and explains the results. Our results are robust to various sample selection strategies and model specifications. The main implication from our study is that intelligence not only shapes formal rules and informal regulations such as social trust, norms and traditions but also it has the ability to reverse the paradoxical process known as "resource curse." The study contributes to better understanding of reasons of deforestation and shed light on the debated impact of political regime on forest management.

Trends and Patterns in Age Discrimination in Employment Act (ADEA) Charges.

The Age Discrimination in Employment Act (ADEA) protects individuals aged 40 years and over from discrimination throughout the employment process. Using data on ADEA charges from the Equal Employment Opportunity Commission from 1993 to 2010, we present labor force-adjusted charge rates demonstrating that the highest charge rates are among those in the preretirement age range, and only the rate of charges among those aged 65 years and older has not decreased. We examine characteristics of ADEA charges including the prevalence of different alleged discriminatory actions (or issues) and highlight the increasing proportion of age discrimination charges that are jointly filed with other antidiscrimination statutes. Through a regression analysis, we find that the likelihood of citing various issues differs by charging party characteristics, such as age, gender, and minority status, and on charges that cite only age discrimination as compared to those that are jointly filed. Implications of these findings for employers are discussed.

Differences by mother's education in the effect of childcare on child obesity.

Previous studies have found adverse effects of maternal employment on child obesity for higher educated mothers. Using a quasi-structural model, we find additionally a lower risk of obesity for children of less educated mothers with increased time in non-parental childcare.

Evaluation of bias in estimates of early childhood obesity from parent-reported heights and weights.

OBJECTIVES: We evaluated bias in estimated obesity prevalence owing to error in parental reporting. We also evaluated bias mitigation through application of Centers for Disease Control and Prevention's biologically implausible value (BIV) cutoffs. METHODS: We simulated obesity prevalence of children aged 2 to 5 years in 2 panel surveys after counterfactually substituting parameters estimated from 1999-2008 National Health and Nutrition Examination Survey data for prevalence of extreme height and weight and for proportions obese in extreme height or weight categories. RESULTS: Heights reported below the first and fifth height-for-age percentiles explained between one half and two thirds, respectively, of total bias in obesity prevalence. Bias was reduced by one tenth when excluding cases with height-for-age and weight-for-age BIVs and by one fifth when excluding cases with body mass-index-for-age BIVs. Applying BIVs, however, resulted in incorrect exclusion of nonnegligible proportions of obese children. CONCLUSIONS: Correcting the reporting of children's heights in the first percentile alone may reduce overestimation of early childhood obesity prevalence in surveys with parental reporting by one half to two thirds. Excluding BIVs has limited effectiveness in mitigating this bias.

Multiple Imputation For Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys.

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally-representative cohort surveys.

Parent-reported height and weight as sources of bias in survey estimates of childhood obesity.

Parental reporting of height and weight was evaluated for US children aged 2-13 years. The prevalence of obesity (defined as a body mass index value (calculated as weight (kg)/height (m)(2)) in the 95th percentile or higher) and its height and weight components were compared in child supplements of 2 nationally representative surveys: the 1996-2008 Children of the National Longitudinal Survey of Youth 1979 Cohort (NLSY79-Child) and the 1997 Child Development Supplement of the Panel Study of Income Dynamics (PSID-CDS). Sociodemographic differences in parent reporting error were analyzed. Error was largest for children aged 2-5 years. Underreporting of height, not overreporting of weight, generated a strong upward bias in obesity prevalence at those ages. Frequencies of parent-reported heights below the Centers for Disease Control and Prevention's (Atlanta, Georgia) first percentile were implausibly high at 16.5% (95% confidence interval (CI): 14.3, 19.0) in the NLSY79-Child and 20.6% (95% CI: 16.0, 26.3) in the PSID-CDS. They were highest among low-income children at 33.2% (95% CI: 22.4, 46.1) in the PSID-CDS and 26.2% (95% CI: 20.2, 33.2) in the NLSY79-Child. Bias in the reporting of obesity decreased with children's age and reversed direction at ages 12-13 years. Underreporting of weight increased with age, and underreporting of height decreased with age. We recommend caution to researchers who use parent-reported heights, especially for very young children, and offer practical solutions for survey data collection and research on child obesity.