Income Estimates Research Articles

The Shadow Prices of Voluntary Caregiving: Using Well-Being Panel Data to Estimate the Cost of Informal Care

Abstract This article uses the well-being valuation (WV) approach to estimate and monetize the well-being impacts of informal care provision on caregivers. Using nationally representative longitudinal data from the UK, the British Household Panel Survey, we address two challenging methodological issues related to the economic valuation of informal care: (i) the anticipatory nature of informal care; and (ii) the sensitivity of income estimates used in valuation. We address the anticipatory issue by focusing on well-being impacts associated with caring for a relative who had recently suffered a serious accident. We use the fixed effects filtered (FEF) estimator to estimate a “time-invariant income” coefficient free from individual fixed effects bias, which helps to partially improve the quality of the income estimate as an alternative to using instrumental variables. This estimate is used in the calculation of shadow prices of informal care. Our estimates suggest that, focusing on the first year of unanticipated care provision, those experiencing the well-being losses from providing unanticipated informal care would be willing to pay approximately £13,167 on average to avoid it.

National Experimental Wellbeing Statistics (NEWS)

The NEWS project is improving and expanding estimates of income, poverty, and other measures of economic wellbeing by linking survey, administrative, and commercial data. The initial release addressed biases from unit nonresponse through improved weights, missing income information in survey and administrative data through improved imputation, and misreporting by combining or replacing survey responses with administrative data.

An environmental Kuznets curve for global forests: An application of the mi-lasso estimator

In this study, we employ a Moran's i based Lasso (Mi-Lasso) methodology to address the spatial dependence of an unspecified functional form, investigating the association between a country's economic growth and the rate of deforestation. Our aim is to explore the existence of a forestry environmental Kuznets curve (EKC). Our approach to handling spatial dependence overcomes limitations identified in existing EKC literature. We estimate a series of cross-sectional data models spanning the period from 1990 to 2020 for 146 countries. Our findings indicate a non-linear relationship, revealing a change peak rate of deforestation over time. Additionally, we observe that the income threshold at which the deforestation rate begins to decrease changes over time with differences observed between model specifications. Crucially, our results highlight that failing to account for spatial dependence leads to a significant absolute upward bias in ordinary least squares (OLS) estimates of income and worse model fit.

Multiliteracies meet new methods: The case for digital writing in English education case study in G-7 countries.

Recently, globe has tried to transform populace activities to digital platform, wherefrom all stakeholders can attain their basic information. However, education sector cannot be excluded from this debate. Since, the pandemic mostly most economies have utilized digital transformation in different aspects of life, but digital education becomes more prominent. Therefore, this empirical research focuses on digital writing specifically to English education in G-7 economies between the time frame spanning from January 2000 to April 2022. This study considers urbanization, income, renewable energy, information & communication technology, renewable energy, English education, and pandemic as the key environmental determinants. To address the challenges posed by panel data, this study utilized an advanced set of estimators such as AMG stands for Augmented Mean Group. The estimate of urbanization and income per capita is positively significant, confirming that urbanization and economic development cannot protect the green economy by minimizing environmental pollution. Likewise, the estimated coefficient of English Education and internet use is negative and significant, implying that English Education and ICT can protect the green economy by reducing emissions. Conversely, renewable energy consumption (REC) is an element that can increase economic activity and therefore decline the environmental damages to secure a green economy. Likewise, the growth in cases of Covid-19 also reduces the usage of resources like land, water, and forests and subsequently decreases carbon emissions, promoting the green economy. The results also indicate that interaction term of English Education and ICT have adverse impact on Carbon emission (CO2). The outcomes suggest that internet usage (ICT) in English Education (EE) can deal efficiently with environmental issues for the green economy; therefore, EE and ICT should be part of green policies across the international level.

Estimation of Cost and Income of Onion Production: A Case Study of Ballia District of Eastern Uttar Pradesh, India

The system of India’s economy predominantly depends on agriculture. It is contains attractive good qualities of calories. The present research study on entitled “Estimation of Cost and Income of Onion Production: A Case Study of Ballia District of Eastern Uttar Pradesh This research article focused on the following specific objectives are (1) To work out the cropping intensity, costs-return and input-output relationship of onion cultivation in the district and second objectives (2) To find out the constraints in production of onion confronted by Farmers. Multistage stages sampling technique were used for selection of respondent. Primary data was collected by personal interview methods with the use of pre structured and pre listed schedule questionnaire. Finally 100 farmers were selected randomly from four selected villages. The data were related to the Rabi season in the agriculture cropping year 2021-22. Overall average costs of cultivation of respondent were found per hectare to be Rs. 111493.95. The higher costs of cultivation were observed on medium farms to Rs.118430.40 fallowed by small farms and marginal as Rs.109070.72 and Rs.106980.72 per hectare respectively. Per quintal cost of production and ratio of cost of Input –output analysis were calculated cost A1 toC1, C2 and C3 basis.

Analysis of Differences in Farmer Income Estimation Between Middlemen and Animeter For Bali Cattle in Jaro Subdistrict, Tabalong Regency, South Kalimantan

This study investigates differences in farmer income and body weight estimation for male and female Bali cattle in extensive farming in Jaro Subdistrict, Tabalong Regency, South Kalimantan. A random sampling approach was used, involving farmers from nine villages. Statistical analyses revealed a significant difference (p < 0.05) in the body weights of male Bali cattle measured by an animeter compared to those estimated by middlemen. In contrast, no significant difference (p > 0.05) was observed for female Bali cattle. These differences significantly impacted farmer income for male cattle because the income estimated by the animeter exceeded that estimated by the middlemen (p < 0.05). However, no significant difference (p > 0.05) was observed between the two estimation methods for female cattle. In addition, no significant relationship was observed between the estimates made by the middlemen and those made by the animeter for both sexes of Bali cattle. This study used the Monte Carlo simulation to predict demand for male and female Bali cattle in 2024. On average, farmers were able to supply between two and three male Bali cattle and between three to seven female Bali cattle per month. To ensure a steady income, farmers are advised to supply at least one male or female Bali cattle. Moreover, farmer groups are encouraged to compensate for cattle shortages or surpluses, thereby ensuring a stable supply for middlemen and consumers.

Socioeconomic inequity of measured indoor and outdoor exposure to PM2.5: 5 Years of data from 14,000 low-cost particle monitors

Multiple studies have considered socioeconomic or ethnic group inequities in outdoor fine particle (PM2.5) concentrations. Due to the lack of indoor measurements, these studies are forced to assume that indoor exposures are directly related to outdoor concentrations. In general, this assumption may be reasonable, but it is violated when indoor-generated fine particles form a substantial contribution to total potential indoor exposure. We now have for the first time access to long-term (months or years) indoor potential exposures, made possible by the development of low-cost optical particle counters. A large database of 120 million hourly PM2.5 indoor and outdoor concentrations measured by 10,000 outdoor and >4000 indoor PurpleAir monitors over a 5-year period (2017–2021) in three West Coast states (Washington, Oregon, California) has been used to compare with US Census 2021 estimates of median household income, educational attainment, housing characteristics, and ethnic groups. Clear evidence of inequities is found using indoor as well as outdoor PM2.5 concentrations.

Lifetime milk production of Holstein cattle in the humid tropics compared to Holstein-Gyr and Holstein-Brahman crosses.

Crossbred cattle are commonly used for milk production in the tropics, combining the potential benefits of pure breeds with the heterosis effects of the offspring. However, no comprehensive assessment of lifetime productivity for crossbred versus purebred cattle in low-altitude tropical environments has been carried out. The present study compares the lifetime productivity of purebred Holstein (HO, n = 17,269), Gyr (GY4, n = 435), and Brahman (BR4, n = 622) with crossbreds Gyr × Holstein (GY × HO, n = 5521) and Brahman×Holstein (BR × HO, n = 5429) cows from dairy farms located in low and medium altitude tropical regions in Costa Rica. The production traits of interest were age at first calving (AFC), days open (DO), milk production per lactation (TMP), lactation length (LLEN), age at culling (ACUL), and number of lactations (NLAC). Estimates of heterosis were also calculated. The AFC for GY × HO crosses (33-34 months) was not significantly different (p > .05) from HO (33.8 months). For BR × HO crosses, a significant (p < .05) decrease in AFC (BR3HO1 35.6 months, BR2HO2 34.5 months, and BR1H03 33.3 months) was observed as the fraction of HO breed increased. Estimates of heterosis for AFC were favourable for both crosses, of a magnitude close to 3%. The DO for F1 crosses (GY2HO2 94 days; BR2HO2 96 days) was significantly (p < .05) lower than HO (123 days). Estimates of heterosis for DO were also favourable and above 15% for both crosses. The TMP and LLEN were higher for HO (TMP = 5003 kg; LLEN = 324 days) compared with GY × HO (TMP = 4428 to 4773 kg; LLEN = 298 to 312 days) and BR × HO (TMP = 3950 to 4761 kg; LLEN = 273 to 313 days) crosses. Heterosis for TMP was favourable but low for both crosses, with a magnitude below 3.0%. The NLAC for HO (4.6 lactations) was significantly (p < .05) lower than F1 (GY2HO2, 5.8 lactations; BR2HO2, 5.4 lactations). Heterosis for NLAC was above 6.0% for both crosses. Overall, estimates of lifetime income over feed costs per cow on average were USD 2637 (30.3%) and USD 734 (8.4%) higher in F1 GY × HO and BR × HO, respectively, compared to HO. In conclusion, crossbred animals, specifically those with Gyr and Brahman genetics, extend the productive lifespan, increasing economic returns.

Socioeconomic inequalities and the role of sectoral foreign aid in developing countries

AbstractUsing a panel threshold regression approach, we consider multiple dimensions of socioeconomic inequalities and examine their heterogeneous relationships with sectoral foreign aid. Conditioned on the level of socioeconomic development, three types of inequality and four channels of sectoral foreign aid are considered. We use a comprehensive dataset covering 65 aid‐recipient countries from 1990 to 2017 and find that education aid effectively reduces inequalities but only when the level of institutional quality and other socioeconomic factors is above its 75th percentile value in our sample. These results are robust to alternative measures of income inequality, aid and estimation strategies.

Statistical Analysis of Ineqality and Poverty with Under-Reporting Incomes of Russian Households

The subject of the article is the study of inequality and poverty, taking into account under-reporting household income. The purpose of the research was to estimate the under-reporting incomes of households and their impact on the smoothing income inequality and poverty based on representative data of the All-Russian Survey of Consumer Finance (2013, 2015, 2018, 2020 and 2022). It is relevant to analyze changes in inequality for individual population groups, as well as the contribution to inequality of individual sources of income, including those hidden from statistical observation. The novelty of the study is the obtained estimates of under-reporting household income, as well as the results of an analysis of their impact on smoothing income inequality and poverty based on the specified dataThe methodology included the use of econometric modeling according to the Pissarides – Weber approach, as well as decomposition of the Gini elasticity by income sources based on the Lerman – Yitzhaki algorithm. The results of estimations differ from those previously obtained according to the data of the Russia Longitudinal Monitoring Survey – Higher School of Economics. The resulting share of under- reporting income in the disposable resources of households averaged about 20% for 2013 to 2022 and decreased by 2,3% on average per year. Decomposition of the Gini index by income sources led to the conclusion that the main contribution to inequality was made by the non-poor population. Calculation of Gini income elasticities by income sources for poor and non-poor households (taking into account the imputed component of under-reporting income) revealed their differently directed impact on inequality. Wages and social transfers increase inequality among the poor and do not increase inequality among non-poor households. Estimating under-reporting income was higher for the group of poor households. At the same time, this component increased inequality in the post-crisis year 2015 and in the pandemic year 2020 for the group of non-poor households. The obtained results will provide important information to social policy makers, taking into account the influence of under-reporting income` on the poverty reduction and equalization of the standard of living of the Russian population

Sustainable Income, Employment, and Income Distribution in Indonesia

Production and consumption activities in any economy have a direct impact on the environment. Although increased economic activity and population growth in developing countries continue to exert enormous pressure on their natural environments, the role of the environment is neglected in the estimation of national income. Such neglect at the macroeconomic level is at least in part, an important cause of environmental degradation in developing countries. Since the United Nations Conference on Environment and Development in 1992 at Rio and even as early as middle of the 1980s, a substantial literature had developed on methods to integrate the environment into the economic development process. The main assertion in this literature is that natural resources represent a form of capital that is analogous to the stock of manufactured capital. Sustainable income can be determined by allocating a portion of income to allow for the deprecation of natural capital [Ahmed, El Serafy, and Lutz (1989) and Solow (1992)]. Indonesia had average real GDP growth rates of more than five percent per year up to the early 1990s [World Bank (1994)]. But income inequality (measured by the Gini coefficient) has been high. Although inequality continues to be quite high, especially between rural and urban populations, Indonesia has been successful in poverty alleviation up to mid 1990s. In 1976 almost 40 percent of its population was below the poverty line, which in 1993 decreased to less than 14 percent [Todaro (1994)]. Income distributional consequences of economic growth would continue to be one of the main policy issues in Indonesia. This is due to its large population size, presence of different ethnic and religious groups, large diversity between rural and urban groups, variety of natural resources scattered over the country, huge distances and the effects of a far-flung archipelago [Akita, Lukman, and Yamada (1999)].

Maddison‐style estimates of the evolution of the world economy: A new 2023 update

AbstractThis paper surveys the literature on historical national accounting, discusses the importance of relative income benchmarks for, in particular, historical income estimates, and presents an update of long run global economic development with a new version of the Maddison Project Database (MPD). As benchmarks are central to methodologies for global income comparisons over time, and therefore vital to MPD, we analyze the consequences and biases of three benchmarks, the 1990 benchmark, the 2011 benchmark and the multiple benchmark method following the recent Penn World Tables (PWT) methodology, for pre‐1940 income estimates. We develop a methodology to determine which benchmark in combination with time series produces the best anchor for the historical income estimates in the MPD. We conclude that the best way forward for the Maddison Project is to stick to the original 1990 benchmark, yet with two important changes. First, we integrate the 2011 benchmark for the post‐1990 period, and second, we fine tune the dataset for the pre‐1940 period by integrating a new historical benchmark for the US/UK comparison in 1909. By integrating more benchmarks, the MPD moves closer to a multiple benchmark approach as developed by the PWT.

Harmonized disposable income dataset for Europe at subnational level

In recent decades, detailed country-level estimates of income and wealth have become widely available and inform us about the evolution of inequality between and within countries. But a substantial portion of these available datasets lack sub-national geographical information, precluding the exploration of the spatial distribution and evolution of inequalities within countries. We present here a new dataset of disposable income for Europe at the subnational level. It has been compiled from existing income data (gross income, gross earnings, equivalised income, etc.) published by national statistical institutes at different geographical levels. We used linear regressions and numerical operations to estimate disposable income from other available socio-economic statistics (e.g. household size, tax rates). We developed a harmonization and adjustment procedures to ensure of the consistency of statistical units, income indicators, costs of living and inflation. The dataset covers 42 European countries distributed over more than 120,000 geographical entities on the 1995 to 2021 period (most of the data being available for the 2010–2020 decade). This new dataset opens avenues for investigating the links between income inequality and other socio-economic or ecological processes.

Cost and Income Estimation of Burn Intensive Care Units A Case Study in the Armed Forces Hospital

Background: As one of the most expensive sections of health system, hospitals are of special importance among healthcare service providers, and the management of financial resources in this sector is essential for continuous provision of services. The high cost of the burn department and post-treatment care in the armed forces, which is one of the most needed units of this organization, has led to the financial analysis of the burn department in Chamran Hospital. The main goal of this study is for the policy-makers and managers of the country's health sector to adopt a suitable policy in the debate between quality and cost-effectiveness of establishing a burn unit in the field of health and treatment, so that, in addition to reducing the costs of this sector, they focus on positive external effects, including reducing the psychological pressure on the affected people.&#x0D; Methods: This cross-sectional and descriptive study analyzed burn department in one of the hospitals affiliated with the armed forces in 2020 and 2021. The required data was collected through the review of all the hospital financial statements and patient payment documents during the period under study, and the income, cost and profitability indicators were calculated and analyzed using Excel software.&#x0D; Results: Based on the results, the calculated costs in the burn department were more than 62 billion tomans, and the income from hospitalization of patients was about 61 billion tomans. The index of pharmaceutical income to total income was calculated as 0.35, pharmaceutical income to pharmaceutical cost was 0.10, and the ratio of hoteling income to total income was 0.37. Among the cost indicators, the ratio of the cost of medicine and medical supplies to the total costs was 0.31, the energy cost to the total cost was 0.002, the percentage of personnel cost to the total resources was 0.56. Moreover, the profitability index was calculated as 0.97.&#x0D; &#x0D; Conclusion: According to the results, although the burn department, especially in the armed forces, is not cost-efficient from an economic point of view, from a social point of view and due to the health and treatment mission of the armed forces, which is the efficient support of the armed forces and meeting their needs. Establishing this sector is considered a strategic matter and the optimal use of resources and reduction of unnecessary costs is necessary for cost efficiency. Therefore, the establishment of a systematic system for evaluating financial performance, outsourcing services, providing subsidies, and raising burn tariffs in order to improve the performance of the burn department in government hospitals are the methods proposed in this study to improve financial indicators, productivity, and excellent management in providing services to the patients in the burn department.

Access to Otolaryngologic Telemedicine Care Across the COVID-19 Pandemic at an Urban Tertiary Hospital System.

Objective: To describe associations between patients' demographic characteristics and access to telemedicine services in an urban tertiary academic medical system across the COVID-19 pandemic, and to identify potential barriers to access. Methods: This was a retrospective cohort study conducted at a single-center tertiary academic medical center. The study included adult patients undergoing outpatient otolaryngologic care in person or via telemedicine during 8 week timeframes: before the pandemic, at the onset of the pandemic, and during later parts of the pandemic. Patients were characterized by age, sex, race, insurance type, primary language, portal activation status, income estimate, and visit type. Where appropriate, chi-squared tests, Wilcoxon signed-rank tests, and logistic regression were used to compare demographic factors between the cohorts. Results: A total of 14,240 unique patients [median age, 58 years (range, 18-107 years); 56.5% were female] resulting in a total of 29,457 visits (94.8% in-person and 5.2% telemedicine) were analyzed. Patients seen in person were older than those using telemedicine. Telemedicine visits included a higher proportion of patients with private insurance, and fewer patients with government or no insurance compared to in-person visits. Race, income, and English as primary language were not found to have a significant effect on telemedicine use. Conclusion: In an urban tertiary medical center, we found significant differences in sociodemographic characteristics between patients who accessed otolaryngologic care in person versus via telemedicine through different phases of the COVID pandemic, reflecting possible barriers to care associated with telemedicine. Further studies are needed to develop interventions to improve access.

Poor and satisfied? A review of the monetary poverty indicator in the EU

This article seeks to contribute to the generation of more accurate poverty indicators in the EU, by providing some further evidence of potential bias when joint income-wealth perspective on poverty measurement is not considered. Using the 2018 EU-SILC, we compare the individuals’ financial satisfaction and his/her household classification as at risk of poverty (AROP). We detect a significant group of people whose households are classified as poor but who are satisfied with their economic situation. The explanations for this mismatch lie both in errors in the income estimation and in the presence of household wealth. Through an exploratory analysis with certain limitations, we find that those in this group have different characteristics from the rest of the poor and are more similar to those who are neither poor nor dissatisfied when we analyse economic stress and proxy wealth variables. The article supports the recommendation to revise the AROP indicator based on the joint income-wealth distribution.

Estimation of Income, Expenditure and Resource use Pattern of Groundnut in Thandrampattu Block of Tiruvannamalai District, Tamil Nadu, India

The core objective of the research estimation of income, expenditure and resource use pattern of groundnut in Thandrampattu block of Tiruvannamalai district is to evaluate the performance of groundnut in terms of cost and returns and resource use efficiency. The sample of 30 farmers growing groundnut were selected at random from the Thandrampet, Veppur, Thanipadi and Vanapuram villages of Thandrampattu Block of Tiruvannamalai District. Average and percentage analyses were used to examine cost of cultivation, labour hours used, quantity of materials used and machine hours used by farmers in each crop production. Regression analysis was used to assess the impact of selected explanatory variables on independent variables. Joint families account for around 33% of total sample homes, with nuclear families accounting for 67 percent. Average size of holding is 2.84 acres. Total cost of cultivation for groundnut is Rs.16371.68/acre. Gross income from groundnut is Rs.54000/ac. Benefit Cost Ratio (BCR) of groundnut cultivation is 3.30. Farmers sell the produce at regulated market, mill owners and local traders. The human labors were used for sowing and transplanting, FYM, fertilizer, chemicals, intercultural operations. FYM, chemicals, fertilizer and seed are the major material used in crop production. Harvester, rotavator and bund former are the machine used for crop production. About 69 percent of variation in dependent variables explained by the selected independent variables such as labor hours for inters cultural operation, land holding size and quantity of FYM.

A quasi-Bayesian Approach to Small Area Estimation Using Spatial Models

The empirical best linear unbiased prediction (EBLUP) method has been the dominant model-based approach in small area estimation. As an alternative to this frequentist method, the observed best prediction (OBP) method, also frequentist, was proposed by Jiang et al.[ 11 ] where the parameters of the model are estimated by minimizing an objective function which is implied by the total mean squared prediction error. In a recent article, Datta et al.[ 6 ] followed a general Bayesian approach, proposed recently by Bissiri et al.[ 2 ], to develop a quasi-Bayesian method by appropriately calibrating the objective function for the OBP method for the Fay-Herriot model. In a different article, Chung and Datta[ 4 ] demonstrated that in the absence of covariates with good predictive power the small area estimates from the standard Fay-Herriot model can be improved by using spatially dependent random effects. In this article, we develop a quasi-Bayesian small area estimation method using several spatial alternatives to the independent Fay-Herriot random effects model. Evaluation of the proposed method based on an application to estimation of four-person family median incomes for the U.S. states shows its usefulness. Limited but related simulation studies for the median incomes application reinforce our conclusion. AMS Subject Classification: 62F 15, 62D99

Income estimation based on human mobility patterns and machine learning models

Sustainable and inclusive urban development requires a thorough understanding of income distribution and poverty. Recent related research has extensively explored the use of automatically generated sensor data to proxy economic activities. Notably, human mobility patterns have been found to exhibit strong associations with socioeconomic attributes and great potential for income estimation. However, the representation of complex human mobility patterns and their effectiveness in income estimation needs further investigation. To address this, we propose three representations of human mobility: mobility indicators, activity footprints, and travel graphs. These representations feed into various models, including XGBoost, a traditional machine learning model, a convolutional neural network (CNN), and a time-series graph neural network (GCRN). By leveraging public transit data from Shenzhen, our study demonstrates that graph-based representations and deep learning models outperform other approaches in income estimation. They excel in minimising information loss and handling complex data structures. Spatial contextual attributes, such as transport accessibility, are the most influential factors, while indicators related to activity extent, temporal rhythm, and intensity contribute comparatively less. In summary, this study highlights the potential of cutting-edge artificial intelligence tools and emerging human mobility data as an alternative approach to estimating income distribution and addressing poverty-related concerns.

Impact of investment and R&amp;D expenditures on company performance in the industry 4.0 era

AbstractThis paper aims to evaluate the influence of investment and R&amp;D expenditures on the performance of large companies that are headquartered in the Europe using regression analysis of the generalized method of moments. The independent control variables are GDP per capita and the interest rate represented by the yield of 10-year state bonds. The sample period is 2010–2018. Our result shows that the growth of R&amp;D expenditures and investments negatively affected the profitability ratios of the firms in the year of implementation. While concentrating on the macroeconomic control variables, the results support an assumption about the positive effects of GDP on the majority of the net income, ROA and ROE estimates. On the other hand, negative effects of interest rates were found.