Income Estimates Research Articles

Disparities in access to healthcare services in a regional neonatal transport network

IntroductionThe disproportionate distribution of healthcare resources in the U.S. leads to an annual estimated 68,000 critically-ill newborns transported to regional centers for intensive care. As described in the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework, patients with limited access to healthcare experience multiple healthcare settings in their journey to specialty care. Little is known about community resources for transported neonatal patients in the Pacific Northwest region. MethodsTransport data from January 2014–December 2019 identified referral zip codes for transfers to a regional neonatal intensive care unit (NICU). Using the SEIPS 3.0 framework to explore the journey of families experiencing neonatal complications, we linked referral zip codes to data on healthcare and resource access including: Health Transportation Shortage Index (HTSI); US Census Small Area Income and Poverty Estimates; public transportation availability; Health Professional Shortage Area (HPSA) and number of Federally Qualified Health Centers. Results1834 neonatal transports from 75 zip codes were analyzed. Forty percent of patients were transferred from a HPSA, one-quarter from areas with limited public transit availability (28%) and household internet access (24%). Almost all infants transferred from a high HTSI area (96%) were transported by air. ConclusionMany patients were referred from communities with high poverty and limited services. Improving patient outcomes requires population and policy level recommendations and care coordination between healthcare organizations and community services. Considering the patient family perspective within the SEIPS 3.0 framework will ensure patient centered care and an inclusive experience for families transitioning through healthcare settings.

36 Disparities in General Mental Status Between Lower and Higher SES Ethnically Diverse Older Adults

Objective:To investigate differences in performance on a widely used cognitive screener between community-dwelling older adults from two disparate socioeconomic groups.Participants and Methods:Participants were part of a larger study of cognitive screening in healthy older adults. The total sample (N=79, 69.6% female, 19% White/Caucasian, 12.7% Asian, 43% Latino/a, 25.3% Black/African-American) consisted of community-dwelling adults (Mage=73.1 years [SD=7.2] and Meducation=14.3 years [SD=2.6]) who were initially recruited via social media, flyers, and general community announcements. A lack of ethnic minority participants resulted in a two-year commitment to reach communities of color via visits and provision of health literacy to local religious and community programs. Continuous contact with leaders/gatekeepers helped establish research study credibility and forge a stronger sense of trust among ethnically diverse participants in the greater Houston, TX, area. Testing was initially conducted at the clinical study site. Due to low participation rates among people of color, greater effort was placed on tailored strategies to overcome economic and time constraints (i.e., schedule/time conflicts, lack of transportation, inability to pay for parking). To fit the priorities and needs of the participants, testing was also conducted at their homes (25.3%) and nearby religious and community centers (22.8%). Participants identifying as Latino/a or Black were predominantly recruited and tested at their local community center (as requested by gatekeepers/participants) to increase access to the study, in contrast to Caucasian participants. Median income estimates were used to stratify participants by socioeconomic status (SES) based on zip codes into low SES (L-SES) or high SES (H-SES) groups.Results:Participants from the L-SES group had significantly lower total scores on the MoCA than their H-SES counterparts, t(77J=2.837, p=0.003, g=0.696. The average MoCA total score for participants from the L-SES group was 2.64 points lower. The observed differences in MoCA total score when stratifying by ethnicity may be attributable to differences in education level and SES, which are known risk factors for cognitive impairment and will be further examined upon recruitment completion.Conclusions:Studies have found that ethnically diverse older adults not only encounter more barriers to accessing quality health care but also experience disparities in brain health research. Communities of color comprise a sizeable portion of our older adults but have been traditionally underrepresented in clinical research, limiting the generalizability of research findings to clinical treatment. Socioeconomic deprivation has been identified as one of several barriers to research engagement for people of color, placing ethnic communities at increased risk for under- or misdiagnosis and limited access to medical intervention. Preliminary findings have implications for the recruitment of ethnically diverse groups in clinical research. Given the growing racial and ethnic diversity among the United States population, we must do our due diligence to increase understanding of participation and recruitment barriers for racial/ethnic individuals. Tailored community outreach and engagement strategies may be effective in improving the inclusion of ethnically diverse populations and facilitating recruitment and retention in clinical research studies.

Association between Prevalence of Talaromycosis and Economic Status in Asia

Previous studies have shown that geographic niche was a significant risk factor for developing T. marneffei (Tm) infection, and to date there are still lacking data of impact of economic status on the prevalence of Tm. Thus, we conducted this study, aiming to examine the correlation between prevalence of talaromycosis among HIV/AIDS-infected adult population and Asian country income, reported by World Bank data. A comprehensive medical literature review was performed to estimate the prevalence of T. marneffei infection among HIV/AIDS-infected adult population in Tm endemic and non-endemic countries and country income in Asia. Linear regression analyses were used to study the associations between Tm prevalence and Asian country income. Both unadjusted and adjusted models with Tm geographic distribution were analysed. A total of 48 Asian countries were included in the statistical analysis. Geographic distribution of talaromycosis was divided into three main regions: (i) Region 1: Regions of highest incidence of talaromycosis (>100 Tm cases reported in literature); (ii) Region 2: Endemic Tm countries and (iii) Region 3: Countries of imported or travel-associated Tm cases. Estimated Tm prevalence in the three regions were 3.9%, 2% and 0.02%, respectively. There was no statistical difference in GDP among three regions (P = 0.70). The linear regression analyses showed no associations between prevalence estimates of talaromycosis and Asian country income in both unadjusted and adjusted for geographic distribution of T. marneffei (P = 0.83). Most markedly, country geography is still a significant risk factor for T. marneffei infection (P < 0.001). Keywords: Economic status, Invasive fungal infections, Prevalence, Talaromyces marneffei, Vietnam.

The role of peer influence in rooftop solar adoption inequity in the United States

Individual demand for emerging technologies can be influenced by the demand of other individuals within defined peer groups. These so-called peer effects have been demonstrated in emerging clean energy technologies such as rooftop solar. To date, peer effects have disproportionately driven solar adoption among relatively affluent households. Here, we use household-level income estimates of rooftop solar adopters to explore how peer effects drive adoption for low-income households. We find evidence of peer effects for both high- and low-income households and find that peer effects are generally stronger within than across income groups. Our results indicate that peer effects translate to adoption less frequently among low-income households. These results suggest that low-income peer effects are mitigated by barriers to low-income adoption. Heterogeneous peer influence is another demand shifter that explains the inequitable adoption of emerging technologies.

The Effect of Kacang Ratu BW on Ration to Production and Business Income of Layer Chickens

The research was conducted to determine the effect of Alfalfa (Medicago sativa L.) substitution for laying hen performance. 108 Hy-Line strains on 26 weeks were put in battery cage and divided randomly into three treatments, (with six replications and every replication consisted six hens). The treatments were used 100% basal diet for control (A0), 99% basal diet + 1% Alfalfa (A1), and 97% basal diet + 3% Alfalfa (A2). Data recorded every day on three periods, with twenty-eight days per period. Feed and drink were given by ad libitum. The data collected every day consist of egg production, egg weight, while laying hen performance counted every week consist of Feed Conversion Ratio (FCR), Hen Day Average (HDA). In addition, the estimation of Income Over Feed Cost (IOFC) counted at the end of this period. Data were analyzed by factorial of classification of variance analysis (ANOVA) between Alfalfa and period. of treatment followed by testing significant means Duncan’s Multiple Range Test (DMRT). According to this research, it can be concluded that Alfalfa can be used for substitution on laying hen’s diet due to decrease the feed cost (increase feed efficiency) moreover without declined its production.

Predicting Time to Evaluation After Pediatric Concussion: Factors Affecting Specialty Concussion Care.

The timing of clinical evaluation after pediatric concussion represents an important and potentially modifiable clinical milestone for diagnosis, selection of appropriate treatment pathways, and recovery prognosis. Patient demographics, socioeconomic status, or medical history may affect the time to the initial evaluation and subsequently influence recovery outcomes. The purpose of this investigation was to evaluate the association of patient characteristics with the time to specialty evaluation after a concussion. It was hypothesized that patients with a history of concussion, a preexisting relationship with our specialty concussion program, or a higher ZIP code-based income estimate would present for care more quickly after a concussion than patients without these characteristics. Cross-sectional study; Level of evidence, 3. Included were patients with a concussion between 6 and 18 years old who were seen for care at a single sports medicine center between January 1 and December 31, 2019. Patient demographic, socioeconomic, injury, and clinical characteristics were collected through a retrospective review of the medical records. The primary outcome was the number of days between the date of the concussion and the patient's initial specialty evaluation. Overall, 220 patients (mean age, 14.4 ± 2.5 years; 46% female) were seen for care at a mean of 9.7 ± 5.6 days (range, 1-21 days) after concussion. A shorter time to specialty evaluation was associated with a history of concussion (β = -1.72 [95% CI, -3.24 to -0.20]; P = .03) and a prior clinical relationship with the treating clinical department (β = -1.85 [95% CI, -3.52 to -0.19]; P = .03). Referral by a primary care provider was associated with a longer time to evaluation (β = 3.86 [95% CI, 2.39-5.33]; P < .0001). A history of concussion and having a preexisting clinical relationship with the deparment were associated with a shorter time to evaluation after concussion. Referral from a primary care physician was associated with a longer time to evaluation. Issues may exist in the propensity to access care after an injury, resulting in delays for initiating early treatment.

Pengaruh Penekanan Anggaran, Asimetri Informasi, Dan Kejelasan Sasaran Anggaran Terhadap Kesenjangan Anggaran

A budget is an estimate of achieving performance goals by recording estimates of income and expenses measured in monetary units over a specified period of time. Budget estimates use historical data to calculate your budget for the previous year. This indicates that the budget set is not being used optimally. We see that his APBD in Gianyar Regency government is under budget in this province. The purpose of this study was to determine the impact of budget concentration, information asymmetry, and clarity of budget goals on budget coverage. The theory used in this study is agency theory. The data used in this study are primary data from recruitment questionnaires. This survey used a sample of 148 respondents. The analytical technique used is multiple linear regression. This study shows that budget orientation and information asymmetry can have a positive and significant impact on budget slack, whereas budget target clarity does not affect budget slack

Using schooling decisions to estimate the elasticity of marginal utility of consumption

This paper uses schooling decisions to estimate curvature in utility of consumption in developing countries. We show that the elasticity of marginal utility of consumption (EMUC) can be recovered from three sufficient statistics: (1) the price effect of schooling; (2) the income effect of schooling; and (3) the consumption cost of sending a child to school. We estimate the consumption cost of schooling using variation from the randomized roll-out of Progresa, a conditional cash transfer program in rural Mexico. Using estimates of income and price effects in the Progresa setting from Bergstrom and Dodds (2021), we estimate the EMUC at 1.6.

Retirement and cognitive aging in a racially diverse sample of older Americans.

Retirement represents a crucial transitional period for many adults with possible consequences for cognitive aging. We examined trajectories of cognitive change before and after retirement in Black and White adults. Longitudinal examination of up to 10 years (mean = 7.1 ± 2.2 years) using data from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study-a national, longitudinal study of Black and White adults ≥45 years of age. Data were from 2226 members of the REGARDS study who retired around the time whenan occupational ancillary survey was administered. Cognitive function was an average of z-scores for tests of verbal fluency, memory, and global function. Cognitive functioning was stable before retirement (Estimate = 0.05, p = 0.322), followed by a significant decline after retirement (Estimate = -0.15, p < 0.001). The decline was particularly pronounced in White (Estimate = -0.19, p < 0.001) compared with Black (Estimate = -0.07, p = 0.077) participants, twice as large in men (Estimate = -0.20, p < 0.001) compared with women (Estimate = -0.11, p < 0.001), highest among White men (Estimate = -0.22, p < 0.001) and lowest in Black women (Estimate = -0.04, p = 0.457). Greater post-retirement cognitive decline was also observed among participants who attended college (Estimate = -0.14, p = 0.016). While greater work complexity (Estimate = 0.92, p < 0.05) and higher income (Estimate = 1.03, p < 0.05) were related to better cognitive function at retirement, neither was significantly related to cognitive change after retirement. Cognitive functioning may decline at an accelerated rate immediately post-retirement, more so in White adults and men than Black adults and women. Lifelong structural inequalities including occupational segregation and other social determinants of cognitive health may obscure the role of retirement in cognitive aging.

Is older age more unequal than we think? Estimates from the Survey of Income and Program Participation linked to administrative records

Despite increased scholarly and policy interest in income inequality, our knowledge of inequality in later life is relatively limited. One challenge to studying inequality in older age groups is that income sources that are important in later life, including pensions and retirement accounts, may be poorly captured by household income surveys. In this study, we investigate income inequality among the older population in the United States, focusing on how measurement error affects retirement income estimates and its impact on summary measures of inequality. We use a rich dataset that links respondents in the Survey of Income and Program Participation with restricted-use administrative records from the Social Security Administration and Internal Revenue Service. We find systematic, nonclassical measurement error in survey-reported retirement income, particularly from pensions and retirement accounts. Due to these patterns of measurement error, income inequality estimates are considerably higher using administrative records instead of survey-reported data. Additional analyses show the effects of measurement error when estimating between- and within-group inequality across racial-ethnic and education groups. The downward bias associated with measuring income inequality among older Americans could worsen in the future given the shift from defined-benefit to defined-contribution retirement plans.

Важный вклад в освещение статистики национального дохода и экономической истории дореволюционной России и СССР

The author of this paper analyses the book by A. Markevich and M. Harrison, “Great War, Civil War, and Recovery: Russia’s National Income, 1913 to 1928”. He highlights the fact that the authors’ (A. Markevich and M. Harrison) calculations introduced in this article significantly expand and clarify the economic situation in Russia and the USSR in 1913-1928 from an economics point of view. For the first time, M. Harrison and A. Markevich evaluate the dynamics of national income and production of individual economic sectors, including the service industry, and compare the economies of Russia and the USSR with other countries in the same period. The calculations show a more successful development of the economy of Russia than other warring countries during the First World War. In addition, the article analyzes the decline in Russia’s share in the world economy in 1928 compared to 1913. Thereby, the authors make a reasonable conclusion about the decrease in the efficiency of the USSR economy in 1928 compared to the pre-revolutionary period. Estimates of the national income, the personal consumption fund, and the population allowed evaluating the entire economic development of pre-revolutionary Russia, the USSR, and post-Soviet Russia. Finally, the authors reveal the consequences of the economic crises of the 20th century in Russia. Drawing on personal and other researchers’ calculations, the authors criticize the opinion about the national income growth in 1928 in comparison with 1913 as exaggerated. Its significant drop during the civil war was caused by war communism as well as by the rupture of economic ties.

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data.

A Data Valuation Model to Estimate the Investment Value of Platform Companies: Based on Discounted Cash Flow

As both investment attraction and mergers and acquisitions targeting information technology and platform companies are becoming more important in the digital-centric economic environment, interest in valuing corporate data assets is increasing. Accordingly, among the income approaches used in business valuation, this study presents a data valuation model based on discounted cash flow. This model is expected to be useful for corporate investment decision-making. The assumptions used in this study for the estimation of data income include intangible asset value, exclude net asset value, and data attribution is centered on technology, human resources, and market factors. In particular, data attribution accounts comprise ordinary data research and development, data labor costs, and data advertising expenses. Data costs were divided into those incurred during collection, storage, curation, analysis, and utilization. Financial statements and related data from a real estate information platform operator over three years were collected and used to simulate the data valuation model. The simulation reveals that the operator possesses KRW 472.6 billion in data assets. Ultimately, the data valuation model developed in this study can contribute to strengthening platform operators’ investment attraction, guaranteeing financial sustainability, and transparency and data assetization.

Estimation of income and price elasticities of Indian electricity demand

This study is the first to analyse Indian electricity demand at both the aggregate and sectoral levels between 1981 and 2020. We use a novel autoregressive distributed lag (ADL) cointegration test in the presence of unknown structural breaks. While the income elasticity is close to one in the residential and industrial sectors, it is greater than one in the aggregate and commercial sectors. These results reveal that economy-wide electricity intensity rises with economic expansion in India. The price elasticity is negative and smaller than one in absolute value in the aggregate sector (−0.66), the residential sector (−0.83), and the industrial sector (−0.63), but it is statistically insignificant in the commercial sector. These estimates allow us to provide sector-specific information for policy design and implementation in the power market.

K-means clustering analysis and multiple linear regression model on household income in Malaysia

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.

Income Guarantee Policy Design: Implications for Poverty, Income Distribution, and Tax Rates

A guaranteed income could greatly reduce poverty, yet the impact and feasibility depend on several key policy design choices. We empirically compare guarantee designs financed by income taxation and estimate potential labor supply responses to transfer benefits as well as financing or targeting mechanisms. The analysis features a fundamental tax reform converting personal deductions and credits into an income guarantee along with higher marginal tax rates. Additional results consider policies with a flat surtax, benefit phaseout, or welfare reform. We use microsimulation analysis based on the Current Population Survey, and we adjust for underreported income.

Catastrophic health expenditure in nonneurological injury due to motor vehicle crash.

Motor vehicle crashes (MVCs) are a leading cause of nonfatal injury in the United States and impose a high financial cost to the patient and the economy. For many patients, this cost may be financially devastating and contribute to worsening health outcomes after injury. We aimed to describe the population level risk of catastrophic health expenditure (CHE) and determine factors associated with risk of CHE. We performed a retrospective review using the 2014-2017 Nationwide Inpatient Sample. The study population consisted of uninsured and privately insured adults aged 26 to 64 years who were hospitalized for nonneurologic traumatic injury due to MVCs. Our measure of financial hardship was CHE, which was defined as hospital charges ≥40% of postsubsistence income. Income estimates were derived from zip-code level data using Γ distribution modeling. Our sample included 189,000 patients, of which 149,705 had private insurance and 39,375 were uninsured. The median estimated income for the study cohort was $66,118 (interquartile range, $65,353-$66,884). The median cost of hospitalization was $53,467 (interquartile range, $29,854-$99,914). In addition, 91.5% of uninsured patients suffering from MVC are at risk for CHE, and 10.1% of privately insured patients are at risk for CHE. Among the insured, Black, Hispanic, and low income were associated with CHE. Nine of 10 uninsured patients are at risk for CHE after hospitalization for MVC. Despite having insurance, 10% of patients are still at risk of CHE. Black, Hispanic, and low-income communities are at highest risk of having private insurance and still experiencing CHE. This is the first population level analysis after the implementation of the Affordable Care Act that assesses the financial burden of no insurance and underinsurance. These data are important to understand the effectiveness of insurance coverage and guide hospital and policy level interventions to prevent CHE. Prognostic and Epidemiological; Level III.

Progress on Poverty in Africa: The Importance of Growth and Inequality

AbstractEmploying World Bank data, this paper, first, historically examines Africa's record on poverty incidence, spread and severity, as compared with other regions of the world, at the US$1.90 and US$3.20 per day (2011 PPP) poverty standards. Second, it evaluates country-specific progress on growth, poverty and inequality, and compares the ‘poverty transformation efficiency vector’ (PTEV) among African countries. Third, the study analyses the relative roles of income growth and inequality changes in explaining African countries' poverty records, through a decomposition of poverty changes using ‘optimal’ income and inequality elasticity estimates from the ‘identity’ model. The study finds that following the dismal record on poverty during the 1980s, progress on poverty has been appreciable since Africa's growth resurgence starting in the mid-1990s, and that this progress was driven mainly by income growth, consistent with the global evidence. Nonetheless, inequality often played a complementary role in most of the countries and, in a small number of cases, it was the primary driver of changes in poverty. Thus, the present study sheds light on country-specific differences in the relative roles of growth and inequality in poverty reduction on the continent, based on both qualitative and quantitative evidence. The study should, therefore, provide a useful compass to those who seek to understand country-specific situations within the African context.

ZERO-BASED BUDGET IN RESTRICTIVE CONTEXTS: WORK METHODOLOGY APPLYING FUZZY LOGIC FOR SMES

Purpose: The objective of the research is proposed a methodology to prepare a Zero-Based Budget (ZBB) for Small and Medium-sized Enterprises (SMEs) in Ecuador, applying fuzzy logic. Design/methodology/approach: A quantitative approach is assumed to show findings derived from the work carried out in these Ecuadorian business units, belonging to non-essential sectors such as wood, textiles and footwear. Fuzzy logic, the technique of expertise, and Trapezoidal Fuzzy Numbers (TpFN) are used to capture true budget levels. Findings: The results recommend that optimal budget levels can be obtained for SMEs in restrictive and health emergency contexts. Originality/value: As a result of COVID-19 pandemic, markets and demand are contracting causing variations in income and demanding greater rationalization at the level of expenditures. For SMEs is essential prepared income and disbursements estimates. Based on the methodology proposed, predictions are made to achieve the objectives of SMEs. Directors will be able to make more successful decisions for the benefit of their companies, to streamline operations, direct the achievement of objectives, rationalize expenses (costs and expenses), and to project better scenarios in the future before carrying out cost-benefit analysis.

Loss expectation and income shifting

This study examines the effects of expected losses on the income-shifting strategies of multinational corporations (MNCs). Using a set of worldwide MNC affiliates, this study first finds empirical evidence for ex ante adjustments of income-shifting strategies according to reverse incentives for potential losses. The results also support the existence of limited flexibility introduced by Hopland et al. (2018, 2021). Second, the estimates of income shifting measured using expected tax rate differences reveal that the traditional methodology of using statutory tax rate differences is subject to an estimation bias. This estimation bias varies depending on the loss expectation status and tax-rate levels of the affiliates.