The Effects of Assets and Debt on the Depression Trajectory of Low-Income Older Adults in South Korea

Evidence suggests that assets and debt are among the most significant factors affecting older adults’ mental health. This study focuses specifically on South Korea, where the poverty rate of older adults is the highest among all OECD countries. Given that low-income older adults with fewer assets and more debt may be at greater risk for depression, we investigated how assets and debt affected the depression trajectory of low-income older adults in South Korea. We used the six most recent waves of data from the Korean Welfare Panel Study (2014-2019) to estimate the longitudinal effects of assets and debt on depression in low-income older adults. Our sample was restricted to low-income Korean heads of household aged 55 and above (N=2,832). Using latent growth curve modeling, the unconditional model revealed decreasing trends in depression over time, while the conditional model, controlling for sociodemographic variables (i.e., age, gender, education, general health, marital status, employment status, income), suggested that assets and debt had contrasting impacts on depression. Specifically, although it did not impact the depression trajectory, debt did have a positive impact on depression at baseline. Most notably, assets negatively affected both depression at baseline (B=-1.911, SE=0.284, p<.001) and its trajectory (B=-0.235, SE=0.081, p<.01). These findings highlight the importance of holding assets over time as a protective factor against depression and thus the need for interventions such as savings programs and financial education for low-income older adults.

Multiscale Modeling of Meandering Fluvial Reservoir Architecture Based on Multiple-Point Geostatistics: A Case Study of the Minghuazhen Formation, Yangerzhuang Oilfield, Bohai Bay Basin, China

Meandering river reservoirs are essential targets for hydrocarbon exploration, although their characterization can be complex due to their multiscale heterogeneity. Multipoint geostatistics (MPS) has advantages in establishing reservoir architectural models. Training image (TI) stationarity is the main factor limiting the uptake of MPS modeling algorithms in subsurface modeling. A modeling workflow was designed to reproduce the distribution of heterogeneities at different scales in the Miocene Minghuazhen Formation of the Yangerzhuang Oilfield in the Bohai Bay Basin. Two TIs are established for different scales of architecture. An initial unconditional model generated with a process-based simulation method is used as the megascale TI. The mesoscale TI of the lateral accretion layers is characterized by an uneven spatial distribution of mudstone in length, thickness, frequency, and spacing. Models of different scales are combined by the probability cube obtained by lateral accretion azimuthal data as an auxiliary variable. Moreover, the permeability function sets are more suitable than the porosity model for collaboratively simulating the permeability model. Model verification suggests this workflow can accurately realize the multiscale stochastic simulation of channels, point bars, and lateral accretion layers of meandering fluvial reservoirs. The produced model conforms geologically realistically and enables the prediction of interwell permeability variation to enhance oil recovery.

Cognitive abilities and income: preliminary evidence from a developing country

Purpose Related literature finds that human capital proxied by cognitive abilities is an important antecedent of numerous specific life outcomes. The purpose of this study is to extend existing evidence by investigating the link between cognitive skills and income in Tajikistan. Tajikistan is a landlocked low-income country situated in Central Asia. Its population is 9.1 million people and gross domestic product per capita of US$822. According to the World Bank, Tajikistan has made significant progress in decreasing poverty levels from 83% in 2000 to 29.5% in 2017. Design/methodology/approach The data for this study comes from the 2013 Jobs, Skills and Migration Survey conducted by the World Bank and the German Society for International Cooperation. The main explanatory variable of the study is the cognitive abilities index of the respondents. The survey used item response theory (IRT) approach to estimate the ability of respondents. IRT is a method or a set of statistical frameworks, used to explore assessment item data, such as cognitive abilities assessment data. The wage function was estimated using the ordinary least squares method because the results are easier to interpret (Jencks, 1979; Bowles et al., 2001; Groves, 2006). Findings The baseline results are reported in Table 2. The results in Column 1 demonstrated the link between cognitive abilities and income without control variables (unconditional model). As expected, cognitive abilities are positively and significantly related to income (a1 = 0.0715, p < 0.01). The results from the unconditional model suggest that one standard deviation increase in cognitive abilities is associated with a nearly 17% increase in income. Research limitations/implications However, the study has a number of limitations. First, the dependent variable measures the overall income of the respondent, which includes the profit from other businesses. The survey does not provide data on monthly wages of respondents. Second, the sample may not perfectly represent the overall population of Tajikistan. To partially resolve this issue, this paper re-estimated out results for various sub-samples. Another important limitation of this study is the lack of respondent’s family background, which is an important correlate of human capital and income. Practical implications The results in the study offer preliminary evidence on the link between cognitive abilities and income in Tajikistan. However, the results of the study also suggest that both measures of human capital are positively related to income. Therefore, policymakers in Tajikistan should invest greater resources to health care, education and training programs as cognitive skills can be built in particular in the early stages of the life cycle. Indeed, Tajikistan has a significant potential for economic growth model driven by human capital. According to the World Bank, the adult literacy rate in Tajikistan is 100%, which is significantly above of what is observed in other developing countries. This may imply that the human potential in this country is considerable, and further investment in soft and hard skills would have a positive impact on economic growth. Originality/value This paper offers new evidence on the link between cognitive abilities and income, using data from Tajikistan. First, this paper finds that cognitive abilities are positively and significantly correlated with income. Second, this paper finds that this link remains robust even when this paper control for a large set of personal and job-related characteristics. The results from the unconditional model suggest that one standard deviation increase in cognitive abilities is associated with nearly a 17% increase in income.

Genome-Wide Conditional Association Study Reveals the Influences of Lifestyle Cofactors on Genetic Regulation of Body Surface Area in MESA Population

Backgrounds Body surface area (BSA) is an important trait used for many clinical purposes and is associated with a variety of diseases including cardiovascular diseases and cancer. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). Genome-wide association study of BSA was conducted on 5,336 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from MESA (The Multi-Ethnic Study of Atherocloris) data using unconditional and conditional full genetic models for analyzing genetic effects of additive, dominance, epistasis, and genetic by ethnicity interactions.Results Conditional association analyses revealed that lifestyle cofactors could affect the genetic effects of genes that regulate BSA. Moreover, impacts of the lifestyle cofactors on BSA could depend on the genotypes of several SNPs, and ethnicity of individuals. In this study, fifteen SNPs were identified with highly significant (Experiment-wise PEW < 1×10–5) genetic effects using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. Estimated heritabily was 72.88% using unconditional model and 74.85 ~ 79.87% using lifestyle cofactor-conditional models. It was revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes. From gene ontology analysis, it was observed that several genes are related to the metabolic pathway of calcium compounds, a main compound in several diseases related to obesity, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer.Conclusions In summary, our study provides novel insights into the genetic mechanism of BSA in MESA population, and influences of different lifestyle cofactors on the genetic effects of BSA associated loci.

Trajectories of Dynamic Risk Factors During Forensic Treatment: Growth Trajectory of Clinical Risk Factors in a Sample of Dutch Forensic Patients

In this study, growth trajectories (from admission until unconditional release) of crime-related dynamic risk factors were investigated in a sample of Dutch forensic patients ( N = 317), using latent growth curve modeling. After testing the unconditional model, three predictors were added: first-time offender versus recidivist, age, and treatment duration. Postanalyses were chi-square difference tests, t tests, and analyses of variance (ANOVAs) to assess differences in trajectories. Overall, on scale level, a decrease of risk factors over time was found. The predictors showed no significant slope differences although age and treatment duration differed significantly at some time points. The oldest age group performed worse, especially at later time points. Treatment duration effects were found at the second time point. Our results that forensic patients show a decrease in crime-related risk factors may indicate that treatment is effective. This study also found differences in growth rates, indicating the effect of individual differences

Alpha Beta Risk and Stock Returns—A Decomposition Analysis of Idiosyncratic Volatility with Conditional Models

The variance of stock returns is decomposed based on a conditional Fama–French three-factor model instead of its unconditional counterpart. Using time-varying alpha and betas in this model, it is evident that four additional risk terms must be considered. They include the variance of alpha, the variance of the interaction between the time-varying component of beta and factors, and two covariance terms. These additional risk terms are components that are included in the idiosyncratic risk estimate using an unconditional model. By investigating the relation between the risk terms and stock returns, we find that only the variance of the time-varying alpha is negatively associated with stock returns. Further tests show that stock returns are not affected by the variance of time-varying beta. These results are consistent with the findings in the literature identifying return predictability from time-varying alpha rather than betas.

Analysis of Stock Mutual Fund in Indonesian Economy

Mutual Fund is one of developing industries. Since it was launched in 1996, mutual fund industry has repidly grown. This fact was indicated by more and more mutual funds that are operated. This condition provides more choices for by investors. Beside considering of benefit and value which will be given by mutual fund, investors must also consider performance of mutual fund. The objective of this research is to evaluate performance of mutual fund in Indonesia and what factors which influence the performance of mutual fund. Measure the performance of the mutual fund, this research used model a<br />which was developed by Alpha Jensen. Using Jensen model caused an unconditional model, which performance of mutual fund can be compared without focusing on the differentiation of the portfolio diversification level. This model measures performance by intercept of regression between excess portfolio<br />return as dependent variable and excess market return as independent variable. The result of this research that is only two of six mutual fund has outperform toward market perform as a benchmark performance. Variable of excess market retum level was consistent influenced portfolio return with positive significance. The bad mutual fund performance more caused by stocks election ability of portfolio managers in selecting<br />accurate stock to porlfolio. Beside uncapability of portfolio managers in selecting accurate stock, the different characteristic of mutual fund caused bad observed mutual fund.

Operational Ideology and Party Identification

Conventional wisdom suggests that partisanship is the “unmoved mover” in the minds of American voters. Revisionist theories hold that party updating is conditional on individual/contextual factors. By delimiting the scope conditions of the Michigan model, revisionist models do not fundamentally challenge the classic view. This paper proffers an unconditional model of party revision. We theorize that beliefs about government activism—operational ideology—are widely available and heuristically efficacious, and easily map onto party labels. Hence, operational ideology should drive party updating. Using data from seven panel studies covering 1990–2012, we demonstrate that (1) party shapes operational ideology, (2) operational ideology shapes party, (3) the ideology-to-party effects are as large as the party-to-ideology effects, and (4) neither sophistication nor education or elite polarization condition these relationships. These results push the revisionist model of party farther than it has gone before and suggest that operational ideology is a core predisposition in mass belief systems.

The Influence of Neighborhood Poverty on Blood Glucose Levels: Findings from the Community Initiative to Eliminate Stroke (CITIES) program

Objectives: To examine the relationship between both individual and neighborhood level characteristics and non-fasting blood glucose levels.Study design: This study used a cross sectional design using data from the Community Initiative to Eliminate Stroke Program in NC (2004-2008). A total of 12,809 adults nested within 550 census block groups from two adjacent urban counties were included in the analysis.Methods: Participants completed a cardiovascular risk factor assessment with self-reported demographics, stroke-risk behaviors, and biometric measurements. Neighborhood level characteristics were based upon census data. Three multilevel models were constructed for data analysis.Results: Mean blood glucose level of this sample population was 103.61mg/dL. The unconditional model 1 suggested a variation in mean blood glucose levels among the neighborhoods (τ00 = 13.39; P < .001). Both models 2 and 3 suggested that the neighborhood composite deprivation index had a significant prediction on each neighborhood’s mean blood glucose level (¡01= .69; P < 0.001,¡01= .36; P = .004). Model 3 also suggested that across all the neighborhoods, on average, after controlling for individual level risk factors, deprivation remained a significant predictor of blood glucose levels.Conclusions: The findings provide evidence that neighborhood disadvantage is a significant predictor of neighborhood and individual level blood glucose levels. One approach to diabetes prevention could be for policymakers to address the problems associated with environmental determinants of health.

A New Parameterization Method for Large-Scale Reservoir History

A novel and simple parameterization method using an ensemble of unconditional model realizations is applied to decrease the dimension of the misfit objective function in large-scale history matching problems. The major advantage of this parameterization method is that the singular value decomposition (SVD) calculation is completely avoided, which saves time and cost for huge matrix decomposition and the eigenvectors computations in parameterization process. After objective function transforms from a higher dimension to a lower dimension by parameterization, a Monte Carlo approach is introduced to evaluate the gradient information in the lower domain. Unlike the adjoint-gradient algorithms, the gradient in our method is estimated by Monte Carlo stochastic method, which can be easily coupled with different numerical simulator and avoid complicated adjoint code. When the estimated gradient information is obtained, any gradient-based algorithm can be implemented for optimizing the objective function. The Monte Carlo algorithm combined with the parameterization method is applied to Brugge reservoir field. The result shows that our present method gives a good estimation of reservoir properties and decreases the geological uncertainty without SVD but with a lower final objective function value, which provides a more efficient and useful way for history matching in large scale field.