Consumption Expenditure Research Articles

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

State Civil Apparatus Welfare of Public Elementary School Teachers in Bandar Lampung City

Teacher welfare is an important aspect that affects the standard, quality of life, and work of teachers. The components of welfare for the Indonesian State Civil Servant (ASN) are basic salary, allowances, work environment, security aspects in granting civil servant (PNS) status, awards, work-life balance between working hours and workload, and the development of certified teacher professionalism. The study population was ASN SDN teachers totaling 1466 people. Sampling using purposional sampling random method based on the lottery obtained a sample size of 155 people. The research objective was to determine the effect of consumption expenditure, education level, training, work experience, and teacher certification on the welfare of ASN SDN teachers in Bandar Lampung City. The estimation method used Ordinary Least Square (OLS). The findings are that the level of education, training, work experience, and teacher certification have a positive and significant effect on the welfare of ASN primary school teachers in Bandar Lampung City, while consumption expenditure has a negative and significant effect on the welfare of ASN primary school teachers in Bandar Lampung City. In the future, it is necessary to improve education, training, and experience, as well as teacher certification to improve welfare. In addition, it is necessary to maintain the purchasing power of teachers' income so that it is not eroded by inflation which reduces purchasing power.

Machine learning algorithms for energy efficiency: Mitigating carbon dioxide emissions and optimizing costs in a hospital infrastructure

Hospital buildings incur significant energy consumption due to their expensive operations, advanced medical equipment, strict sanitation protocols, and compliance with environmental standards in various weather conditions. For this study, focusing on two distinct climate regions in Turkey—Aksaray and Bursa provinces—a total of 1,440 unique scenarios were generated. These scenarios utilized materials from the Revit Building Information Modeling (BIM) software library, incorporating variations in thermal transmittance coefficients, solar heat gain coefficients, and building orientation angles. The research employed machine learning algorithms to predict energy consumption, carbon dioxide (CO2) emissions, total expenditures, and life cycle costs. While the Revit BIM software library provides numerous combinations of building materials, it’s important to note that only a limited subset undergoes practical testing during construction. The main objective is to streamline the estimation of energy consumption, CO2 emissions, overall costs, and life cycle expenditures in similar architectural settings and under comparable climate conditions, eliminating the need for drafting and energy calculation software. Across all machine learning algorithms, predictions generated by artificial neural networks closely aligned with actual values. Notably, the R-squared (R2) values, a critical evaluation metric, produced the following outcomes: both R2 values for energy were 0.95; total costs achieved 0.93 and 0.97; CO2 emissions attained 0.94 and 0.97; and life cycle costs garnered 0.95 and 0.94 for validation and test datasets, respectively. These results suggest that the successes observed in the two provinces can be extrapolated to data from all regions in Turkey. Moreover, our model is capable of predicting energy consumption, costs, and CO2 emissions based on thermal transmittance values complying with the Turkish standard TS 825 and building orientation angles.

Do Macroeconomic Factors Influence Household Consumption Expenditure in the Gambia? ARDL-Error Correction Regression Approach

Consumption constitutes a larger part of GDP and contributes immensely in enhancing economic growth and development of a country. This study examines the influence of macroeconomic factors on household consumption expenditure in the Gambia. To achieve our objective, we employ ARDL estimation method with Bounds tests to analyze our study using secondary times series data collected from World Bank’s World Development Indicators database from 2000 to 2020. The findings reveal that GDP, inflation, direct credit to private sector, remittances and population all have significant effect on household consumption expenditure in the short run because their p values are less than 0.05 at 5% level of significance. For the long run effect, the CointEq (-1)* is negative and statistically significant. It means that the short run is adjusted with high speed in the run approximatively 108%. Therefore, GDP, inflation, direct credit to private sector, inflation, remittances and population are all significantly affecting household consumption in the long run. Thus, the study recommends that Government of the Gambia to use a mix macroeconomic policy method to stimulate and increase household consumption expenditure in order to help in achieving economic growth and development. The study further recommends future researchers to include other macroeconomic variables like unemployment and interest rate.

A decompositional analysis of social group inequality in India

ABSTRACT Using the national representative Indian household surveys collected by the National Sample Survey Organization, we examine the welfare gaps across social groups for the entire distribution in 1983, 1993–94, 2004–05 and 2011–12. We use spatially adjusted per capita consumption expenditure as the measure of welfare and show that there exists significant welfare gap between Scheduled Tribes/Scheduled Castes (ST/SC) and General Category (GC) households at higher quantiles of the distribution, but the magnitude of the gap has declined over the years for the SC households. Using unconditional quantile regression decomposition, we find that the coefficient effect (unexplained effect) dominates the endowment effect (explained effect) in explaining the gap in all four years. Further decomposition shows that the difference in educational distribution between the SC/ST and GC households drives most of the explained differences between the social groups.

Cross-species transcriptomics identifies obesity associated genes between human and mouse studies

BackgroundFundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined ‘health weight’. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions.MethodsThe study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty.ResultsPrincipal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues.ConclusionsThe present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

Demand and Supply Projections for Pulses in India

Background: The per capita net availability of pulses in India has been increased from 15.5 kg per year in 2018-19 to 19.6 kg in 2021-22. Efforts made to bridge the gap between demand and supply of pulses in the country has resulted in reducing the gap to some extent in recent years and still country is depended on import to meet the growing demand of the pulses such as pigeon pea, lentils and peas. Few studies predicted the demand and supply of pulses as a whole in the country and no studies analyzed the pulse wise demand supply gap in India. Hence, the present study was proposed to predict the demand and supply of major pulses such as chickpea (Gram/Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) in India for a period, 2024-2030. Methods: The present study has been used the crop data on area, production and yield of major pulses including chickpea (Gram/Chana), pigeon pea (Tur/Arhar), black gram (urd bean), mung bean (Green gram) and lentil (Masur) for a period of 29 years (1985-2024) collected from the Directorate of Economics and Statistics, Government of India, New Delhi. Household consumption expenditure data was collected from the National Sample Survey office for 2011-12 from the Government of India. The supply projection was estimated by using linear regression model and demand projections were done by using behaviouristic approach. Result: Decadal trend in area, production and yield of pulses from 1970-2010 showed mixed trends of increase and decrease, whereas in 2010-2020, chickpea, pigeon pea, mung bean and black gram showed positive trend in area, production and yield but lentil alone showed negative trend in production. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. Import dependency of the pulses in the total availability has reduced to 8.92 per cent in 2021-22 from 19.42 per cent in 2009-10. The availability of pulses has grown at a rate of 3.73 per cent from 2009 to 2021. Chick Pea, Tur, Mung bean, Urd bean and Lentils together contributed on an average 88.7 per cent to the total pulses production and 83.73 per cent of pulses availability from 2013 to 2021. Production of these five pulses has increased to 23.04 million tonnes in 2020-21 from 17.3 million tonnes from 2013-14. Gram is the single pulse contributed more than 40 per cent of the pulse production of the country in the above period. Demand supply projection showed a net surplus of 0.81-3.89 mt in the case of pulses and 5.64 -7.63 mt in the case of gram and 0.89 to 1.91 mt in Urd and 0.68 to 1.57 mt in Mung bean during 2024-2030. But in the case of pigeon pea and lentil there may be a net deficit in the range of 3-2.42 and 1.13-1.04 mt, respectively, during 2024-2030. Efforts should be made to bridge the gap in demand and supply of pigeon pea and lentil by the way of bringing new areas under pulses and developing the technologies suitable for the clusters.

Research on the relationship between global oil prices and economic indicators based on linear regression and ARIMAX models

This report aims to analyze the relationship between global oil prices and various economic indexes by using linear regression and ARIMAX models. This study will predict global oil prices accurately and establish a reasonable system for regulating oil prices. The research uses the statistical approach to predict oil prices based on historical data (including independent variables and dependent variable). The study uses monthly average data of WTI crude oil prices from January 2000 to March 2023 and contains the analysis of various economic indicators such as Consumer Price Index (CPI), Personal Consumption Expenditures (PCE), Employment, Population, and Oil Price. The findings indicate that the linear regression model can explain about 40.89% of the variation in log oil price, with significant negative effects of log_PCE, log_EMPLOYMENT, and log_POPULATION, and a significant positive effect of CPI on log_price. However, there exists the probability that some other factors have impact on oil prices. In this study, the author employ the ARIMAX model with ARIMA(4,1,1) errors, which can describe a relatively good fit and small errors in training set measures. Overall, while the linear regression model partially explains the variability in global oil prices, further analysis on residuals is necessary. The study concludes that the ARIMAX model provides a better approach to capture the time-series nature of the data.

Deficit financing and consumption: a study of Indian private consumption expenditure

The study reinvestigates the 'Ricardian Equivalence hypothesis in India by taking private consumption as the dependent variable. The study aims to empirically study the impact of debt financing versus tax of fiscal deficit in the Indian context, as Indian policymakers primarily rely on fiscal policy as a tool for economic stability and growth. ARDL, FMOLS and DOLS approach was used by taking annual time series data from 1988 to 2023. The study reinvestigates the 'Ricardian Equivalence hypothesis in India by taking private consumption as the dependent variable. Government expenditure, public debt, tax, domestic income, and liquidity constrain as independent variables. The estimates confirm a significant symmetric as well as symmetric long-run and short-run relationship between the variables; the results reject the Ricardian Equivalence and propound the Keynesian approach that financing the fiscal deficit (debt vs tax) does matter to the private consumption expenditure. The positive and significant coefficient shows that the increase in liquidity will lead increase in private consumption expenditure. Over-reliance on debt financing strategies has a significant influence on domestic private consumption. The liquidity constraints, fiscal policies will be able to reallocate resources from the future to the present. Since debt financing of deficits and liquidity substantially influence India's consumer spending, expansionary fiscal policies should be carefully devised and supported. This study contributes to the existing literature on deficit financing and 'Ricardian Equivalence' by giving new evidence on designing sustainable fiscal policy by spending wisely without imperilling the country's consumption expenditure.

Bank lending channel and household consumption expenditure in Nigeria

Bank lending channel and household consumption expenditure in Nigeria

Impact of the total expenditure shocks on food security: VAR model

Purpose. This study examines the causal relationship between total expenditures and food availability and identifies their shocks in food availability in Saudi Arabia. Methodology / approach. The study uses a multivariate modeling technique of the Vector Autoregression (VAR) and its environment, the Granger Causality Test, Forecast Error Variance Decomposition (FEVD), and Impulse Response Function (IRF) for the observation period of 2000–2020 in Saudi Arabia. Results. The results of the Granger causality show that investment expenditure has a significant impact on food availability in Saudi Arabia. However, consumption and government expenditures do affect food availability in Saudi Arabia, but have an indirect effect. The Impulse Response Functions show that the shocks of the selected variables require a long period to reach the long-run equilibrium level and the greatest response of the food availability variable is explained by its own shock and investment expenditure shocks. Originality / scientific novelty. The novelty of this study is related to the investigation of a new model and focus on a new perspective. While traditional food security research has mostly concentrated on agricultural production, availability, and accessibility of food, as well as nutrition and health outcomes factors, this research conveys a new dimension by highlighting the link between total expenditure and food security. Their contribution expands the scope of food security research and highlights the impact of recognising the role of total expenditure in implementing and supporting food security at the household level. Practical value / implications. It is important to design strategies and develop a budgeting plan to allocate a reasonable portion of total consumption and government expenditures on food items. Adding, regularly reviewing, and adapting the budgeting plan based on new challenges, and evolving priorities are essential to address the dynamic nature of food security.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

Consumption Expenditure Patterns and Food Security Dynamics in Nigeria

Food insecurity remains a critical issue globally, as highlighted by the Sustainable Development Goals (SDG 2). In 2023, nearly 25 million Nigerians faced food insecurity due to factors like insurgency affecting agricultural activities. This, therefore, worsens the food insecurity in the country. Thus, this study investigates the dynamics between household consumption expenditure patterns and food security in Nigeria over a decade (2010-2019). This was done by analysing four waves of secondary panel data of 4,000 nationally representative households of the Nigeria General Household Survey GHS - Panel collected by the National Bureau of Statistics in four waves over ten years from 2010 to 2019. Analytical tools used were descriptive statistics and the Food Security Index. The results of the consumption patterns of the respondents showed that the majority of the respondents were in the lowest band of per capita total expenditure and sectoral expenditures. The ratio of food secure to food insecure averages around 30% to 70% with a food security (surplus) index of 1.26 and food insecurity (shortfall) index of 0.53. Nigeria was food insecure during the period. The analysis concludes that boosting agricultural productivity, improving infrastructure, and addressing corruption are essential to enhance food security in Nigeria. The government is recommended to prioritize long-term strategies for food production, support vulnerable households, and leverage technological advancements to increase agricultural productivity. This study underscores the need for comprehensive policies to address the multifaceted challenges of food insecurity in Nigeria.

Effect of digital financial inclusion on banking for the poor in African emerging economies

PurposeThis paper studies the effect of digital financial inclusion in banking on the poor and deprived populations of African emerging economies from 1997 to 2023.Design/methodology/approachAutomated teller machines, mobile payments and mobile money transactions measure digital financial inclusion. Household consumption expenditure proxies poverty reduction. The autoregressive distributed lag analyzes the study.FindingsThe results indicate that automated teller machines, mobile money transactions and financial deepening positively affect poverty reduction, while mobile payments negatively affect poverty reduction. Digital financial inclusion decreases poverty via increased investment and empowerment.Research limitations/implicationsDigital financial products and services should be expanded to all population segments in the economies. The governments should improve the quality and quantity of institutions that guarantee the operation of digital financial activities through the enforcement of law and order. The quality and quantity of mobile money transactions and financial deepening should be increased. The costs and charges involved in using automated teller machines and mobile payments should be regulated to relieve the burden on the population. The government should facilitate access to digital financial services via power supply, transport and telecommunication networks. Banks and telecommunications service providers should improve the payment system network to ensure cost-effective, convenient and secure financial service delivery. The digital infrastructure and financial services markets should be enhanced to fully capture the gains of financial inclusion and reduce poverty.Originality/valueA literature review provides studies with conflicting findings on the effect of digital financial inclusion on poverty reduction. This study supports that digital financial inclusion decreases poverty.

Public hospital reform, family health consumption and health inequality: evidence from China Family Panel Studies.

In 2017, China launched a comprehensive reform of public hospitals and eliminated drug markups, aiming to solve the problem of expensive medical treatment and allow poor and low-income people to enjoy basic health opportunities. This study attempts to evaluate the policy impact of public hospital reform on the health inequality of Chinese residents and analyze its micro-level mechanism from the perspective of household consumption structure. Studying the inherent causal connection between public hospital reform and health inequality is of paramount significance for strengthening China's healthcare policies, system design, raising the average health level of Chinese residents, and achieving the goal of ensuring a healthy life for individuals of all age groups. Based on the five waves of data from the China Family Panel Studies (CFPS) conducted in 2012-2020, We incorporates macro-level statistical indicators such as the time of public hospital reforms, health insurance surplus, and aging, generating 121,447 unbalanced panel data covering 27 provinces in China for five periods. This data was used to explore the impact of public hospital reform on health inequality. Logical and empirical tests were conducted to determine whether the reform, by altering family medical care and healthy leisure consumption expenditures, affects the micro-pathways of health inequality improvement. We constructed a two-way fixed model based on the re-centralized influence function (RIF_CI_OLS) and a chained mediation effects model to verify the hypotheses mentioned above. Public hospital reform can effectively improve the health inequality situation among Chinese residents. The reform significantly reduces household medical expenses, increases healthy leisure consumption, promotes the upgrading of family health consumption structure, and lowers the health inequality index. In terms of indirect effects, the contribution of the increase in healthy leisure consumption is relatively greater. Public hospital reform significantly alleviates health inequality in China, with household health consumption serving as an effective intermediary pathway in the aforementioned impact. In the dual context of global digitization and exacerbated population aging, enhancing higher education levels and vigorously developing the health industry may be two key factors contributing to this effect.

The impact of government expenditure, renewable energy consumption, and CO2 emissions on Lebanese economic sustainability: ARDL approach

Most of the recent environmental and economic studies focus on the influence of renewable energy consumption and effective government expenditure respecting global climate change in leading sustainable economic growth. The empirical studies showed variation in the relationship between these variables. Based on the Keynesian economic growth framework, this study aims to investigate the impact of government expenditure, renewable energy consumption, and carbon dioxide emissions on the sustainable economic growth of Lebanon. The study used the ordinary least square method to test the short- and long-run relationship between the model variables by employing the Autoregressive Distributed Lag Stationarity estimation. The research data are gathered from the World Development Indicators annually from 1990 to 2022. The empirical findings showed that all variables are stationary at first difference except for carbon dioxide emissions. A long-term relationship between the dependent and independent variables was shown by the model test simulation employing the bound test. The model test for model residuals showed no heteroscedasticity based on the White test. The residuals are normally distributed by applying the Shapiro-Wilk test, and the model is stable with no structural break at the period. According to the study results, government spending has a robust reverse relation with sustainable economic growth and positive significant results for both renewable energy consumption and carbon dioxide emissions. The study findings are consistent with some literature sources and raise attention to monitoring the nature of government spending and boosting green energy sources in an economy.

Optimizing community green roof spaces in high-density cities: a K-modes clustering algorithm based analysis of resident preferences and spatial configuration

ABSTRACT Green roofs are an important part of the ecological system in high-density cities and a crucial component of community public green spaces. Analyzing preferences of residents during spatial planning can help improve their satisfaction and efficiency in using community rooftop spaces. This study uses K-modes algorithm to perform cluster analysis on questionnaires from 699 residents, and summarizes user profiles with significant characteristics by combing the comprehensive ratings of residents regarding different roof space functions. The study shows that differences in preferences among people are not only related to demographic characteristics but also to their interests and environmental perceptions. For example, low-to-middle-income groups, being price-sensitive, tend to reduce energy consumption expenditure. The highly educated population, driven by social needs, shows a clear preference for activity spaces and sports facilities. The elderly population, emphasizing healthy eating and rural memories, gives higher ratings to agricultural spaces. Finally, this study explores the correlation between certain resident characteristics and their preferences for green roof space functions, and proposes a “1+X” spatial configuration strategy dominated by Landscape Leisure Space, with Ecological Low-carbon Space, Agricultural Production Space, and Activity Gathering Space as options, in order to optimize community green roof spaces guided by resident needs.

Pricing the priceless childcare: Early childhood education for babies and toddlers.

This article connects Viviana Zelizer's theory of the social meaning of money to family studies, using the case of American parents' spending on children. We investigate how money spent on the youngest children-babies and toddlers-reflects the growing expert emphasis on the importance of parental investment in the critical early period for child development. First, we review literature on expert knowledge to trace the shift in increasing emphasis on the importance of building children's cognitive skills through formal education beginning in infancy, offered in center-based care, moving from spaces of "childcare" to "early childhood education" centers. Second, we use quantitative data from the Consumer Expenditure Survey (1995-2017) to show that parents have increasingly spent money, and an increasing share of their income, on center-based care for babies and toddlers but not on otherchild items. Additionally, lower income families have been spending a greater share of their income on center-based care for their infants than other families. We interpret our findings using Zelizer's theory about the cultural influences on the meaning of money, showing how these can be expert-led and persist even when families are faced with structural economic constraints.

Poverty dynamics for Ghana during 2005/06–2016/17: An investigation using synthetic panels

Ghana has consistently kept its poverty rate lower than the regional average over the past 25 years, but this positive trend has slowed down recently. Without actual panel data, it would be next to impossible to probe into the dynamic patterns beneath this trend. We offer the first study that examines the dynamics of overall, moderate, and extreme poverty in Ghana during 2005/06–2016/17, addressing the lack of actual panel data by constructing synthetic panel data from three nationally representative cross-sectional rounds of the Ghana Living Standards Surveys (GLSSs).We present a well-defined decomposition framework, which allows us to examine a rich profile of the dynamics among different population groups. Beyond poverty analysis, we further investigate the dynamics of the full consumption expenditure distribution. Although we find considerable conditional chronic (extreme) poverty rates hovering around 50–60%, there was more upward mobility than downward mobility during this period. Poor households were also more likely to have stronger consumption expenditure growth. The findings suggest that factors such as educational attainment, female household headship, urban residence, and nonagricultural work are positively correlated with poverty reduction. Compared to all other correlates, educational attainment appears to be the most effective in pushing households out of poverty and keeping them from falling into poverty. These results contribute to the growing literature on poverty dynamics in developing countries in general and provide new and relevant inputs for policy advice in Ghana in particular, especially since policies that address chronic poverty typically differ from those that tackle transient poverty.

Changes in food consumption expenditure during the COVID-19 pandemic in Indonesia

PurposeThis study aims to explore the structural changes in food consumption expenditure in Indonesia before and during the COVID-19 pandemic using data from the 2020 and 2021 National Socioeconomic Survey by Statistics Indonesia.Design/methodology/approachThe quadratic almost-ideal demand system analysis model is used to estimate changes in the share of food consumption and the demand and price elasticity of food commodities in Indonesia. A total of 15 food items are analyzed to determine changes in food consumption expenditure during the COVID-19 pandemic.FindingsThe results of this study show that during the COVID-19 pandemic, there was an increase in the proportion of household expenditure related to the consumption of home-cooked food. Simultaneously, the proportion of expenditure on prepared food significantly decreased.Practical implicationsThe pandemic has changed household food consumption in Indonesia. This study recommends that the government ensure the availability of supplies and stability of food prices and provide financial subsidies to maintain food consumption, especially for lower-income communities.Originality/valueThere has yet to be a study on the changes in household food consumption during the COVID-19 pandemic in Indonesia. Therefore, this research provides empirical evidence that there were changes in household food expenditure during the pandemic.