We estimate a dynamic model of schooling on two cohorts of the National Longitudinal Survey of Youth and find that, contrary to conventional wisdom, the effects of real (as opposed to relative) family income on education have practically vanished between the early 1980s and the early 2000s. After conditioning on a cognitive ability measure (AFQT), family background variables and unobserved heterogeneity (allowed to be correlated with observed characteristics), income effects vary substantially with age and have lost between 30% and 80% of their importance on age-specific grade progression probabilities. After conditioning on observed and unobserved characteristics, a $300,000 differential in family income generated more than 2 years of education in the early 1980s, but only 1 year in the early 2000s. Put differently, a $70,000 differential raised college participation by 10 percentage points in the early 1980s. In the early 2000s, a $330,000 income differential had the same impact. The effects of AFQT scores have lost about 50% of their magnitude but did not vanish. Over the same period, the relative importance of unobserved heterogeneity has expanded significantly, thereby pointing toward the emergence of a new form of educational selectivity reserving an increasing role to noncognitive abilities and/or preferences and a lesser role to cognitive ability and family income.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Income is a primary determinant of social mobility, career progression, and personal happiness. It has been shown to vary with demographic variables like age and education, with more oblique variables such as height, and with behaviors such as delay discounting, i.e., the propensity to devalue future rewards. However, the relative contribution of each these salary-linked variables to income is not known. Further, much of past research has often been underpowered, drawn from populations of convenience, and produced findings that have not always been replicated. Here we tested a large (n = 2,564), heterogeneous sample, and employed a novel analytic approach: using three machine learning algorithms to model the relationship between income and age, gender, height, race, zip code, education, occupation, and discounting. We found that delay discounting is more predictive of income than age, ethnicity, or height. We then used a holdout data set to test the robustness of our findings. We discuss the benefits of our methodological approach, as well as possible explanations and implications for the prominent relationship between delay discounting and income.

Data from the National Health and Nutrition Examination Survey (NHANES) in 2005-2010, including 6,662 adults aged 20 or older, were utilized for this cross-sectional study. The baseline data was used to display the distribution of each characteristic visually. Multiple linear regression and smooth curve fitting were used to study the linear and non-linear correlations between PIR and nocturia. Subgroup analysis and interaction tests were conducted to examine the stability of intergroup relationships., We obtained data from the NHANES database website for the three cycles of 2005-2006, 2007-2008, and 2009-2010. Data analysis, including baseline characteristic distribution, logistic regression analysis, RCS curves, and subgroup analysis, was conducted using StataMP17.0 and R language 4.2.2., , # Association between family income to poverty ratio and nocturia

The “Data†originates from the NHANES database and represents the data obtained after our screening process. “RCS†includes the code for conducting restricted cubic spline regression on the data after applying weights. “subgroup†contains the code for performing subgroup analysis on the weighted dataset. “1.4svyscitb5†is utilized to weigh the dataset during subgroup analysis. Through “RCS,†“subgroup,†and “1.4svyscitb5,†we analyzed “Data†and identified a significant nonlinear relationship between PIR and nocturia. We also listed the correlations between various subgroups and their associations with PIR and nocturia.

Description of the data and file structure

Data

Seqn: The patient sequence number corresponding to the NHANES database.

Gender: The gender of the participants.

Age: The age range of the participants.

Race: The race of the participants.

Education: The education level of the participants.

Ma...

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

Because of their restricted access to financial resources, couples undergoing economic distress are more likely to live in disadvantaged neighborhoods than are financially well-off couples. The link between individual economic distress and community-level economic disadvantage raises the possibility that these two conditions may combine or interact in important ways to influence the risk of intimate violence against women. This study examined whether the effect of economic distress on intimate violence was stronger in disadvantaged or advantaged neighborhoods or was unaffected by neighborhood conditions. This project was a secondary analysis of data drawn from Waves 1 and 2 of the National Survey of Families and Households (NSFH) and from the 1990 United States Census. From the NSFH, the researchers abstracted data on conflict and violence among couples, as well as data on their economic resources and well-being, the composition of the household in which the couple lived, and a large number of socio-demographic characteristics of the sample respondents. From the 1990 Census, the researchers abstracted tract-level data on the characteristics of the census tracts in which the NSFH respondents lived. Demographic information contains each respondent's race, sex, age, education, income, relationship status at Wave 1, marital status at Wave 1, cohabitation status, and number of children under 18. Using variables abstracted from both Wave 1 and Wave 2 of the NSFH and the 1990 Census, the researchers constructed new variables, including degree of financial worry and satisfaction for males and females, number of job strains, number of debts, changes in debts between Wave 1 and Wave 2, changes in income between Wave 1 and Wave 2, if there were drinking and drug problems in the household, if the female was injured, number of times the female was victimized, the seriousness of the violence, if the respondent at Wave 2 was still at the Wave 1 address, and levels of community disadvantage.

Cardiovascular Disease Prevalence in Travis County (2014-2018)

Assessing Risk Factors in an Urban Community

By City of Austin [source]

About this dataset

This dataset provides invaluable insight into the prevalence of cardiovascular disease in Travis County, Texas between 2014 and 2018. By utilizing data from the Behavioral Risk Factor Surveillance System (BRFSS), this dataset offers a comprehensive look at the health of the adult population in Travis County. Are your heart health concerns growing or declining? This dataset has the answer. Through its detailed analysis, you can quickly identify any changes in cardiovascular disease over time as well as understand how disability and other factors such as age may be connected to heart-related diagnosis rates. Investigate how diabetes, lifestyle habits and other factors are affecting residents of Travis County with this insightful strategic measure!

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How to use the dataset

This dataset provides valuable insight into the prevalence of cardiovascular disease among adults in Travis County from 2014 to 2018. The data includes a Date_Time variable, which is the date and time of the survey, as well as a Year variable and Percent variable detailing prevalence within that year. This data can be used for further research into cardiovascular health outcomes in Travis County over time.

The first step in using this dataset is understanding its contents. This data contains information on each year’s percent of residents with cardiovascular disease and was collected during annual surveys by Behavioral Risk Factor Surveillance System (BRFSS). With this information, users can compare yearly changes in cardiovascular health across different cohorts. They can also use it to identify particular areas with higher or lower prevalence of cardiovascular disease throughout Travis County.

Now that you understand what’s included and what it describes, you can start exploring deeper insights within your analysis. Try examining demographic factors such as age group or sex to uncover potential trends underlying the increase or decrease in overall percentage over time . Additionally, look for other data sources relevant to your research topic and explore how prevalence differs across different factors within Travis County like specific counties or cities within it or types of geographies like rural versus urban settings . By overlaying additional datasets such as these , you will learn more about any correlations between them and this BRFSS-surveyed measure overtime .

Finally remember that any findings related to this dataset should always be interpreted carefully given their scale relative to our broader population . Yet by digging deep into the changes taking place , we are able to answer important questions about howCV risk factors might vary from county-to-county across Texas while also providing insight on where public health funding should be directed towards next !

Research Ideas

• Evaluating the correlation between cardiovascular disease prevalence and socio-economic factors such as income, education, and occupation in Travis County over time.
• Building an interactive data visualization tool to help healthcare practitioners easily understand the current trends in cardiovascular disease prevalence for adults in Travis County.
• Developing a predictive model to forecast the future prevalence of cardiovascular disease for adults in Travis County over time given relevant socio-economic factors

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: strategic-measure-percentage-of-residents-with-cardiovascular-disease-1.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------| | Date_Time | Date and time of the survey. (DateTime) | | Year | Year of the survey. (Integer) | | Percent | Percentage of adults in Travis County with cardiovascular disease. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Austin.

This paper relaxes some restrictions of previous twin-based estimates of the effects of education on earnings. First, it estimates the earnings premiums associated with different educational levels. Second, it estimates a piecewise linear relationship between the natural logarithm of annual earnings and years of schooling. Third, the measurement error corrections are based on a less restrictive, non-classical, measurement error model. The estimation strategy implies that ability bias can be investigated separately in different parts of the educational distribution. The linear relationship between the logarithm of annual earnings and years of schooling is rejected. Furthermore, the results in the sample of identical (MZ) twins indicated both that the ability bias could be of different signs and of different magnitudes in different parts of the educational distribution. The twin-based estimates in the sample of fraternal (DZ) twins did not display any marked differences as compared to the cross-sectional estimates. Finally, the results indicated that the error-corrected twin-based estimates of the average return to years of schooling that rely on a classical measurement error model are upwards biased by approximately 30%.

In an era when education is supposed to be a means for good jobs and linked to extrinsic values such as fame and money, students are losing interest in school education. The goal of this research is to see if there is any correlation between the students' views of school worthiness and schoolwork, and demographic variables. Our hypothesis is that several key demographics involving diversity, income, and education will affect how students view school and its importance. Results show that there is no correlation between the demographic variables we analyzed and the student perception of school worthiness.

Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

Sociodemographic and economic characteristics of respondents knowledge of the highest conception probability period (n = 235,575/4).

Research of the mutual relation between labour, mobility, income and education. Detailed descriptions of r's school career and occupational career / kinds of work, distances to residence, taking training courses, courses organized by company, training facilities, plans to make use of training facilities, costs of courses / legal position with respect to employer / working overtime / how did respondent get his job / periods of unemployment / looking for ( other ) job or additional job, motives, efforts made / preferred number of working hours / impediments to start in a new job within a month / relation between education, skill and work / respondent's level of education compared with colleagues / expectations in respect of function of higher level / willingness to work in relation to financial necessity, willingness to have shorter working hours / occupational level of respondent's father when respondent was 12 years old / doing voluntary work / valuation of different levels of income / detailed data on components of family income. Background variables: basic characteristics/ residence/ housing situation/ household characteristics/ place of work/ occupation/employment/ income/capital assets/ education

Multilevel logistic regression analysis of determinant factors of knowledge of the highest conception probability period among women of reproductive age in Low-Income African countries (n = 235,574).

Values and childbearing attitudes: pearson correlation coefficients.

MR leave-one-out analysis of the causal effect of years of schooling on income.

Asymmetric ARDL model short-term coefficient elasticities results.

Female entrepreneurs have irreplaceable status and essential significance in entrepreneurship research. Improving females’ entrepreneurial intentions is an important topic in this area. Accordingly, this study, based on the theory of planned behavior, investigates the factors that affect female students’ entrepreneurial intention at China’s vocational colleges and whether household income moderates the relationship between entrepreneurial education, attitude, competence, self-efficacy and entrepreneurial intention. 2149 females from vocational colleges in Guangdong Province, Zhejiang Province, and Jiangxi Province were randomly chosen to participate in the study. They had taken part in entrepreneurial courses throughout 2021–2022. In addition, data were analyzed by structural equation modeling partial least squares. The results demonstrate that entrepreneurial education did not directly affect female students’ intentions. Entrepreneurial competence, self-efficacy, and attitude positively affect entrepreneurial intention. It is further concluded that household income significantly moderates the relationship between entrepreneurial education, attitude, competence, and intention. However, there is no significant difference in the relationship between self-efficacy and entrepreneurial intention between high and low-household-income students. While females continue to confront sexism in the workplace, it is crucial that we conduct empirical research into the factors influencing female entrepreneurial intention to boost economic growth and gender parity. This research helps bridge a gap in the prior literature and adds substantial value to encouraging female entrepreneurs.

Adoption of clean electric energy depends not only on administrative regulations, but also on public support, in particular, the public is willing to pay for environmental improvements. However, the increase of solar photovoltaic power generation willingness to pay (WTP) associated with higher education attainment and the identification of their causality has been missing. Present paper used the enactment of the Compulsory Schooling Law as an instrumental variable to solve the causal relationship between education and willingness to pay for photovoltaic power generation. The results are as follows:Heckman two-stage model and instrumental variable both confirmed that higher education has a positive impact on WTP for solar photovoltaic power generation. For each level of public education in the east, the WTP of photovoltaic power generation will increase by 7.540 CNY, 8.343 CNY and 8.343 CNY respectively, the central public will increase by 9.637 CNY, 10.775 CNY and 11.758 CNY, and the western public will increase by 12.723 CNY, 15.740 CNY and 17.993 CNY respectively. The positive influence of education level is smaller among the people who know the ladder price better, but it is bigger among the people who are male, older than 45 years old, healthier, higher income and stronger awareness of safe electricity use. The total socio-economic value of photovoltaic power generation is significantly different in eastern, central and western region China.

Hypertension is a widespread and life-threatening condition affecting one-third of adults globally. In low- and middle-income countries, like Malawi, the burden of hypertension is escalating due to inadequate healthcare resources and lifestyle changes. Family members often become primary caregivers, playing a crucial role in managing hypertension through support and adherence to treatment. This study examined caregivers’ knowledge retention by evaluating their pre- and post-health education knowledge levels. This was a prospective cross-sectional study in Neno, Malawi, a rural setting. 422 caregivers were enrolled from the Integrated Chronic Care Clinic (IC3). A structured questionnaire was used to collect baseline, post-health education, and week six data. Using SPSS V 22.0, comparison of knowledge, attitude, and practices (KAP) scores, correlation between KAP and between KAP and social demographic characteristics were done using Wilcoxon signed-rank test, Pearson correlation, and independent t-test respectively. Among the 422 caregivers who participated in the study, 267 (63.2%) were females and mean age was 44.94 years. The baseline mean knowledge level score was 9.5 (38.0%) and rose to 21.08 (84.3%) p = 0.000 immediate post-health education and a 2.1% decrease 20.54 (82.2%) p