Future Spending Research Articles

Financing health in sub-Saharan Africa 1990-2050: Donor dependence and expected domestic health spending.

In 2021, global life expectancy at birth was 74 years whereas in sub-Saharan Africa it was 66 years. Yet in that same year, $92 per person was spent on health in sub- Saharan Africa, which is roughly one fifth of what the next lowest geographic region-North Africa and Middle East-spent ($379). The challenges to healthy lives in sub-Saharan Africa are many while health spending remains low. This study uses gross domestic product, government, and health spending data to give a more complete picture of the patterns of future health spending in sub-Saharan Africa. We analyzed trends in growth in gross domestic product, government health spending, development assistance for health and the prioritization of health in national spending to compare countries within sub-Saharan Africa and globally.We found that while gross domestic product was projected to increase through 2050 in sub-Saharan Africa, the share of gross domestic product that goes to health spending is only expected to increase moderately. Our exploration shows that this tepid growth is expected because the percent of overall government spending that is dedicated to health 7·2% (6·3-8·3) compared to average of 12·4% (11·7-13·2) in other regions) is expected to stay low. Even if the amount, of resources provided from donors climbs some, it is not expected to keep pace with growing economies in sub-Saharan Africa and may transition towards other global public health goods. Critically, development assistance for health provided to sub-Saharan Africa is expected to decrease in some countries, and the expected growth in government health spending might not be enough to cover this expected decline. Increases in spending with a concordant prioritization of health and the appropriate health system governance and structural reforms are critical to ensure that people who live in sub-Saharan Africa are not left behind.

Factors Affecting the Saving Behaviour of Individuals

Savings refer to the portion of income that people do not spend right away but instead set aside for investments, future spending, or unplanned expenses. A study was conducted to analyze the factors affecting the saving behavior of individuals living in Birendra Nagar Surkhet, Nepal. The study considered marital status, income status, and financial literacy as independent variables, and saving behavior as the dependent variable. The data was collected using a structural questionnaire based on the convenience sampling method, and 190 responses out of 225 questionnaires distributed were observed. A multivariate regression technique was used to analyze the collected data. The study found that marital status and financial literacy had a significant effect on saving behavior, while income status and lifestyle had an insignificant effect. The study emphasized the importance of raising individuals' financial literacy in Birendra Nagar Surkhet, as it could have a favorable impact on their saving behavior.Saving behaviour

Can Words Speak Louder Than Actions? Using Top Management Teams’ Language to Predict Myopic Marketing Spending

Myopic marketing spending—curtailing marketing and research and development expenses to boost earnings—damages firms’ long-term value. Despite this, top management teams are often myopic, and by the time investors or boards detect such short-termism, it is too late to react or intervene. This research introduces a novel prediction method by analyzing the language top management teams use in earnings calls, specifically focusing on marketing and earnings emphasis, to predict future instances of myopic marketing spending. Through linguistic dependency parsing of almost 11 million sentences extracted from nearly 25,000 quarterly earnings call transcripts of 1,197 firms between 2008 and 2019, the authors demonstrate that the proposed approach can predict myopic marketing spending at a quarterly frequency for up to one year in advance. They find that an increase of one standard deviation in earnings emphasis is associated with a 23.68% increase in the likelihood of future myopic marketing spending. Investments based on the proposed approach produce 1.61% additional annual abnormal returns compared with models that exclusively use known predictors of myopic marketing spending, while offering earlier foresight and more frequent opportunities for intervention. This reduces information asymmetry for investors and boards of directors.

National trends in prescription drug expenditures and projections for 2024.

To report historical patterns of pharmaceutical expenditures, to identify factors that may influence future spending, and to predict growth in drug spending in 2024 in the United States, with a focus on the nonfederal hospital and clinic sectors. Historical patterns were assessed by examining data on drug purchases from manufacturers using the IQVIA National Sales Perspectives database. Factors that may influence drug spending in hospitals and clinics in 2024 were reviewed-including new drug approvals, patent expirations, and potential new policies or legislation. Focused analyses were conducted for biosimilars, cancer drugs, endocrine drugs, generics, and specialty drugs. For nonfederal hospitals, clinics, and overall (all sectors), estimates of growth of pharmaceutical expenditures in 2024 were based on a combination of quantitative analyses and expert opinion. In 2023, overall pharmaceutical expenditures in the US grew 13.6% compared to 2022, for a total of $722.5 billion. Utilization (a 6.5% increase), new drugs (a 4.2% increase) and price (a 2.9% increase) drove this increase. Semaglutide was the top drug in 2023, followed by adalimumab and apixaban. Drug expenditures were $37.1 billion (a 1.1% decrease) and $135.7 billion (a 15.0% increase) in nonfederal hospitals and clinics, respectively. In clinics, increased utilization drove growth, with a small impact from price and new products. In nonfederal hospitals, a drop in utilization led the decrease in expenditures, with price and new drugs modestly contributing to growth in spending. Several new drugs that will influence spending are expected to be approved in 2024. Specialty, endocrine, and cancer drugs will continue to drive expenditures. For 2024, we expect overall prescription drug spending to rise by 10.0% to 12.0%, whereas in clinics and hospitals we anticipate an 11.0% to 13.0% increase and a 0% to 2.0% increase, respectively, compared to 2023. These national estimates of future pharmaceutical expenditure growth may not be representative of any health system because of the myriad of local factors that influence actual spending.

Identifying synergies between VLBI and STIX imaging

Context. Reconstructing an image from noisy, sparsely sampled Fourier data is an ill-posed inverse problem that occurs in a variety of subjects within science, including data analysis for Very Long Baseline Interferometry (VLBI) and the Spectrometer/Telescope for Imaging X-rays (STIX) with respect to solar observations. The need for high-resolution, high-fidelity imaging fosters the active development of a range of novel imaging algorithms in a variety of different algorithmic settings. However, despite these ongoing, parallel developments, such synergies remain unexplored. Aims. We study, for the first time, the synergies between the data analysis for the STIX instrument and VLBI. In particular, we compare the methodologies that have been developed in both fields and evaluate their potential. In this way, we identify key trends in the performance of several algorithmic ideas and draw recommendations for the future spending of resources in the study and implementation of novel imaging algorithms. Methods. To this end, we organized a semi-blind imaging challenge with data sets and source structures that are typical for sparse VLBI, specifically in the context of the Event Horizon Telescope (EHT) as well as STIX observations. We used 17 different algorithms from both communities, from six different imaging frameworks, in the challenge, making this work the largest scale code comparison for STIX and VLBI to date. Results. We identified strong synergies between the two communities, as proven by the success of the imaging methods proposed for STIX in imaging VLBI data sets and vice versa. Novel imaging methods outperform the standard CLEAN algorithm significantly in every test case. Improvements over the performance of CLEAN offer deeper updates to the inverse modeling pipeline necessary or, consequently, the possibility to replace inverse modeling with forward modeling. Entropy-based methods and Bayesian methods perform best on STIX data. The more complex imaging algorithms utilizing multiple regularization terms (recently proposed for VLBI) add little to no additional improvements for STIX. However, they do outperform the other methods on EHT data, which correspond to a larger number of angular scales. Conclusions. This work demonstrates the great synergy between the STIX and VLBI imaging efforts and the great potential for common developments. The comparison identifies key trends on the efficacy of specific algorithmic ideas for the VLBI and the STIX setting that may evolve into a roadmap for future developments.

Canary Categories

Past customer spending in a category is generally a positive signal of future customer spending. Analyses of historical data at two retailers demonstrate that there exist “canary categories” for which the reverse is true. Purchases in these categories are a signal that customers are less likely to return to that retailer. The authors propose an explanation for the existence of canary categories and then develop a stylized model that illustrates four contributing factors: the probability that a customer finds their favorite brand, customers’ willingness to substitute brands, the cost and attractiveness of visiting other stores, and expectations about future brand availability. The analysis uses both field data and experiments to investigate these factors. The findings suggest that canary categories exist (at least in part) because store assortments are not completely adjusted to local preferences. An implication is that canary categories are endogenous to each retailer; the same category may be a canary category at one retailer and a destination category at a competing retailer.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information

BackgroundDespite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness.MethodsWe study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated– though imperfect– risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a ‘high risk pool’ using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers’ incentives for risk selection and cost control, and compare this to the situation without HRP.ResultsThe results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending.ConclusionOur main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Assessing the Relationship between Plant Types Purchased and Consideration of Future Consequences to Generate Marketing Messages for Ornamental Plants1

Abstract Plants are an integral part of the environment and human health can be improved through people interacting with plants. But do “plant people” care about the environment more than others? Plant purchases may be tied to perceptions of future personal and environmental health. The interface of concern for the environment, personal mental health, and plant purchases has yet to be explored and may hold informative suggestions for marketing strategies. Here, an online survey elicited behavior toward plant purchases, mental health, consideration of future consequences (CFC), plant spending and demographic information. A total of 860 U.S. consumers participated in the study. A probit model assessed the relationship between plant spending, demographics and participants' CFC. Results indicate positive correlations between all of the plant types purchased (e.g., annuals, perennials) and plant spending in 2021 and 2020. Mental health ratings (from 20 statements in the PANAS-X Scales Manual for Positive and Negative Affect Schedule) and high mean scores for future consequences (from a CFC scale) were positively correlated. Purchasing indoor foliage plants improved the probability of being in the high CFC group, while negatively impacting the probability of being in the low CFC group. Purchasing annual plants significantly improved participants’ mental health ratings.

Energetic Modelling of Ánibal Solar Vehicle for Murcia Solar Race Competition.

With the energetic modelling, we search to make a tool with which to predict a future spending of energy in different races and optimize this energy. The energetic modelling of Aníbal vehicle is very important for our objectives because it provides a tool that is very useful for our designs. Thanks to modelling we can save time and money because we can make the test of our ideas and designs before spend our resource. Also the modelling is very useful for our pilots due to before the race they can test different ways of driving for to save the maximum energy possible. Results are very good, in the different graphics we can see that the current, power and energy in the time is very similar to real graphics.

Enhanced Financial Reporting for Unfunded Obligations in the US Economy

This article looks at the critical need for better financial reporting on unfunded obligations in the federal government of the United States’ budgeting and accounting processes. A more comprehensive view is offered by financial records like the yearly Financial Report of the United States Government (FRUSG) published by the U.S. Treasury, which includes incurred debts and undeclared promises. Actions to alleviate the long-term financial discrepancy can be prompted by including these estimates into the regular budget decision-making procedures. The paper highlights the need of openness in future spending by examining particular cases of undisclosed liabilities and contrasting federal cash-based bookkeeping with accrual accounting methods used by public companies. Therefore, the article proposes an improved reporting framework that promotes responsible financial management, urging decision-makers to make informed decisions about the nation’s financial health by carefully examining crucial components and consulting experts on government accounting principles.

Прогноз изменения военных расходов Великобритании

The new era of great power competition against the backdrop of challenging situation in the global economy raises the importance of military expenditures analysis and forecast. The United Kingdom is one of the leading military powers, one of the key NATO members and a country actively involved in the competition and confrontation with Russia and China. Therefore, the UK’s defense spending is worth studying to develop and assess new methods, approaches and factors of military expenditures analysis and forecast as well as an insight for further research of the country’s national security strategy, defense and foreign policy. Among key factors affecting the evaluation of defense spending are: an uncertainty of future technological developments given rapid progress of advanced technologies; a possibility of a technological gap among great powers; the convergence between economy and national security, civil and military technologies, between different warfare domains. Another issue is the broader introduction of the Wholeof-Government Approach to the national security and interstate confrontation. Analysis of past military expenditures and military burden allows to better understand patterns, trends, most probable limits and dynamics of future changes. The article contains a forecast of Great Britain’s future long-term defense spending in four scenarios. It is based on the evaluation of past expenditures, current discussions in the British government and parliament, budgetary plans, and on the analysis of the UK’s attitude to NATO’s Defense Investment Pledge. All scenarios assume that the United Kingdom’s future military expenditures will increase but vary in the degree of the average annual real growth. The article also provides a list and brief description of factors which might significantly affect the UK’s defense spending in the future.

PROJECTION OF MEDICAID SPENDING ON PEOPLE WITH ALZHEIMER’S DISEASE AND OTHER RELATED DEMENTIAS

Abstract As the US population is aging, the number of people with Alzheimer’s Disease and Other Related Dementias (ADRD) is increasing. People with ADRD have high expenditures on medical care and long-term services and supports (LTSS). Medicaid is the largest single payor for LTSS, however, the literature on the demographic trends and epidemiology of people with ADRD does not offer projections of Medicaid spending. The goal of this study was to develop an estimate for future Medicaid spending for people with ADRD for the state of Pennsylvania. The approach taken was to develop a demographic projection of the size of the older population, then estimate the fraction of people with ADRD who are eligible for Medicaid. Next, we used estimates of current spending trends to project future spending through 2025. Nearly all Medicaid participants with ADRD are dually eligible for both Medicaid and Medicare, however, Medicare managed care encounter data were not available for analysis. Thus, the prevalence of ADRD was constructed using Medicaid and Medicare fee-for-service (FFS) claims data. Two alternate estimates of the prevalence of ADRD among Medicare managed care enrollees were used (same as Medicare FFS, or slightly lower). Estimates were generated under several growth rates in per person spending on home and community-based services (HCBS). The results shows that growth in HCBS spending is the largest factor in predicting spending, leading to estimates ranging from $2.8 billion to $3.5 billion. Future research will extend the model to additional years.

Future disease-specific health spending and burden of disease in Norway, 2019 to 2040

The expected increase in the proportion of elderly, with increasing rates of chronic diseases, presents a challenge to the Norwegian healthcare system. In this study, we project the future burden of disease and health spending by health conditions from 2019 to 2040; and explore the importance of 1) population growth, 2) population composition, and 3) future epidemiological development for these projections. We find that total, and per capita, health spending is projected to increase in three scenarios (reference, better and worse health) from 2020 to 2040 for communicable diseases, non-communicable diseases, and injuries. The increased proportion of elderly drives the increase in health spending. When keeping the age composition constant (and by this account for the increased proportion of elderly), we find that per capita health spending decreases in the reference and better health scenario but not in the worse-health scenario. If Norway aims to provide care at current levels in the future, substantial reductions in the cost of care is needed. If not, increased health spending is inevitable, due to chronic conditions in old age.

Healthy aging and future health spending

This article examines the extent to which differences in life-expectancy are associated with shifts in average hospital costs for different age groups. The effect of increases in life expectancy on the cost curves is identified by comparing two countries with different life expectancies, but which are very similar on other variables like culture, technology and health systems (Norway and Denmark). Using data from the National Patient Registries the paper compares the ratio of average spending on individuals who die and individuals who survive in different age groups in these two countries. The best fit between the age related cost curves is achieved when the cost curve in the country with a two year longer life expectancy is also shifted by two years.

The Effect of Avatar Self-Integration on Consumers’ Behavioral Intention in the Metaverse

This study explores the relationship between users and avatars, focusing on the degree of facial resemblance and the realism of fashion items as the main factors of avatar self-integration that affect consumers’ behavioral intentions. Through a contactless survey, this study empirically identified how avatar–user resemblance and fashion item realism influence consumers’ future spending intention on virtual fashion items and the continuous usage intention of the metaverse by mediating psychological identification. We also demonstrate the moderating role of users’ self-monitoring of the effect of avatar–user resemblance on psychological identification. We found a positive mediation role of psychological identification between avatar–user resemblance, fashion item realism, and users’ consumption intentions. Additionally, a higher self-monitoring tendency strengthens the positive effect of avatar–user resemblance on psychological identification. The study establishes an academic basis for metaverse retailing by uncovering connections between predictors contributing to users’ consumption intentions in the metaverse.

RFM Analysis for Customer Lifetime Value with PARETO/NBD Model in Online Retail Dataset

In recent years, there has been a growing interest in analyzing Customer Lifetime Value (CLV) due to its ability to provide valuable insights into customer profitability and worth. CLV analysis predicts the net profit attributed to the entire future relationship with a customer. This analysis involves calculating the present value of a customer's expected future spending with the company, facilitating an understanding of the economic value of long-term customer relationships. CLV analysis empowers businesses to identify their most profitable customers and develop strategies for retaining them, ultimately maximizing long-term profitability.
 CLV analysis relies on various models and techniques, including the RFM analysis categorizes customers based on recency, frequency, and monetary value, helping to segment customers and predict future behavior. Then, The Pareto/NBD model combines probability distributions to estimate CLV and is commonly used for customer base analysis.
 This research article explores the application of RFM analysis for estimating customer lifetime value using the Pareto/NBD model in an online retail dataset. This metric is crucial for businesses as it assists in identifying valuable customers and formulating retention strategies to maximize long-term profitability.

Understanding consumer confidence evolution in response to adverse shocks and how marketing contributes to business s success in economic downturns

Purpose- Firstly, this paper examines how current measurements of consumer confidence might be used to forecast future household spending by using economic variable (i.e. unemployment rate, national adjusted income per capita) data in China, with the help of statistic tools (i.e. Python and Pandas) for the job of data cleaning and analyzing. Secondly, I will focus on the ways in which businesses alter their marketing mix in the face of economic hardship to survive by understanding the importance of utilizing effective marketing strategies during economic downturns. Methodology- This paper examines the predictive performance of consumer confidence index on change in consumption growth by constructing three different OLS regression models and by integrating several existing proposals for effective marketing strategies for businesses in times of low consumer confidence to help business managers to make wise and effective response to economic downturns. Findings- It is shown both 1 year lagged consumer confidence and 3 years lagged consumer confidence are good predictors for current change in consumption patterns, whereas 2 years lagged consumer confidence shows negative correlation with consumption change, and consumer confidence have their own predictive power regardless of the macroeconomic variables. Conclusion- Therefore, this paper contributes to the existing literature by providing empirical findings that consumer confidence cannot always fulfill the role of anticipating consumption change and by further providing concrete policy recommendations for consumer confidence’ revival during recessions in an effort to fill the gap. Keywords: Consumer confidence, marketing, consumption, consumer behavior, forecasting. JEL Codes: B16, B22, E21, E27, M21, M31

Prediction of Future Medical Costs by Modifiable Measures of Health.

Strategies to mitigate rising health-care costs are a priority for patients, employers, and health insurers. Yet gaps currently exist in whether health risk assessment can forecast medical claims costs. This study examined the ability of a health quotient (HQ) based on modifiable risk factors, age, sex, and chronic conditions to predict future medical claims spending. The study included 18,695 employees and adult dependents who participated in health assessments and were enrolled in an employer-sponsored health plan. Linear mixed effect models stratified by chronic conditions and adjusted for age and sex were utilized to evaluate the relationship between the health quotient (score of 0-100) and future medical claims spending. Lower baseline health quotient was associated with higher medical claims cost over 2 years of follow up. For participants with chronic condition(s), costs were $3628 higher for those with a low health quotient (<73; N = 2673) compared to those with high health quotient (>85; N = 1045), after adjustment for age and sex (P value = 0.004). Each one-unit increase in health quotient was associated with a decrease of $154 (95% CI: 87.4, 220.3) in average yearly medical claims costs during follow up. This study used a large employee population with 2 years of follow-up data, which provides insights that are applicable to other large employers. Results of this analysis contribute to our ability to predict health-care costs using modifiable aspects of health, objective laboratory testing and chronic condition status.

The impact of policy uncertainty on entrepreneurial activity: a cross-country analysis

ABSTRACT This study examines the impact of Economic Policy Uncertainty (EPU), capturing the extent to which a national economy is characterized by uncertainty about future tax codes, monetary policy, and government spending on the rate of entrepreneurial activity, including necessity entrepreneurship and opportunity entrepreneurship. Applying the system Generalized Method of Moments methodology on panel data covering 26 countries over 19 years, we show that higher EPU is associated with increased rates of necessity entrepreneurship. The results are significant even after controlling for other macro-level indicators and alternate EPU specifications. The results also suggest a lower rate of necessity entrepreneurship in developed economies, which reflects the role of safety nets as a shield against sudden loss of livelihood. We find no significant relationship between EPU and opportunity entrepreneurship, suggesting that opportunity entrepreneurship is governed by a more complex combination of factors. Our study contributes to the literature on determinants of entrepreneurship and offers important recommendations for entrepreneurs and policymakers.

Estimated Annual Spending on Lecanemab and Its Ancillary Costs in the US Medicare Program

This cross-sectional cost analysis uses data from the 2018 Health and Retirement Study to estimate the potential future Medicare spending and beneficiary costs for lecanemab.