Household Consumption Research Articles

Association of expenditure on ultra-processed foods and beverages and anthropometric indicators in Mexican children: A longitudinal study.

The prevalence of obesity in Mexico has been rising dramatically from school age onward. The high consumption of ultra-processed food has been identified as a contributing factor. We explored the longitudinal association between household expenditure on ultra-processed foods and beverages (UPF) and changes in anthropometric indicators of obesity among Mexican children aged 5 to 10 years in 2002. We used data from the Mexican Family Life Survey (MxFLS), a longitudinal, probabilistic, multipurpose, and representative survey of the Mexican population conducted in 2002, which reports household expenditure on the main food and beverage groups, as well as anthropometric indicators and sociodemographic characteristics of household members, across three rounds surveyed between 2002 and 2012 (n = 2,677). The exposure variable was UPF expenditure, categorized into tertiles, and the outcomes studied were BMI z-score for age, waist circumference, and waist-to-height ratio. We estimated random effects models and generalized estimating equation models for longitudinal data. Using an interaction term between tertiles of UPF expenditure and survey rounds, we found that household membership in the middle and upper tertiles of UPF expenditure in 2002 was associated with an increase in waist circumference and waist-to-height ratio, particularly after three years of follow-up. For instance, the middle tertile of UPF expenditure was associated with an increase of 4.43 centimeters in waist circumference compared to the low tertile of UPF expenditure after three years of follow-up (p < 0.01). Our findings suggest that higher UPF expenditure in households with children aged 5-10 years drives abdominal obesity in the short and medium term, underscoring the need for comprehensive policies to limit the purchase and consumption of UPF from an early age.

Technical implications of light greywater production and quality for decentralized treatment and reuse: A case study in Bucaramanga, Colombia.

Decentralized light greywater (LGW) treatment and reuse can help mitigate urban water scarcity, yet data on its characteristics at the household level in Latin America remain scarce, limiting system design and implementation. This study assessed LGW quantity and quality in a representative household in Bucaramanga, Colombia, and analyzed its implications for decentralized treatment and reuse. Potable water consumption and LGW production from showers and hand basins were monitored over 98 and 124days, respectively, with 27 LGW samples collected for quality analysis. Results showed that LGW production was 33.83 ± 4.10 (L/person)/day, accounting for 21% of household potable water consumption, with no significant differences across days of the week. Showers contributed 94% of LGW, while hand basins accounted for 6%. The volume of LGW was sufficient to meet toilet flushing demands (10% of potable water consumption) and could also support other accepted uses, such as floor cleaning and garden irrigation. LGW quality exhibited high variability, low nutrient content (6.37 ± 1.84 mgN/L and 0.74 ± 0.33 mgP/L), high organic matter concentrations (COD: 879.68 ± 163.51mg/L; BOD5: 387.92 ± 92.08mg/L), and fluctuating fecal coliform levels (1.87 × 105 ± 4.03 × 105CFU/100mL), influenced by personal care product use and hygiene practices. Effective treatment systems must accommodate water quality fluctuations and incorporate processes to remove suspended solids, turbidity, dissolved organic matter, oil and grease, and pathogens. This study identified three key technical implications of LGW production and quality for household-level treatment and reuse systems in decentralized settings.

Inside Household Debt: Disentangling Mortgages and Consumer Credit, and Household and Bank Factors. Evidence from Italy

Inside Household Debt: Disentangling Mortgages and Consumer Credit, and Household and Bank Factors. Evidence from Italy

Illegal Hunting and Bushmeat Trade around Save Valley Conservancy

Abstract Globally, illegal bushmeat hunting has contributed to the decline of over 300 species listed as threatened on the International Union for Conservation of Nature (IUCN) Red List and accounts for an estimated 5 million tons of wildlife harvested annually, particularly in tropical regions, placing immense pressure on biodiversity. Despite its recognized conservation threat, limited information exists on the bushmeat trade dynamics in sub-Saharan African savannas. This study conducted interviews with 133 illegal hunters and 40 anti-poaching field rangers in Southeastern Zimbabwe, using semi-structured questionnaires. We explored the characteristics, motivations, methods, species targeted, and perceptions of wildlife law enforcement in Save Valley Conservancy (SVC). Overall, illegal bushmeat hunting in SVC is mainly done by less educated and unemployed young to middle aged men (15–40 years old). The motives behind illegal bushmeat hunting mainly included household consumption (96%), the desire to raise income (96%), unemployment (78%), retaliation for wildlife induced losses (62%), culture (29%) and poor benefit sharing (8%). The common hunting methods reported were hunting with dogs (87%), and snaring (65%). Targeted animal species included impala (96%), wildebeest (53%), eland (53%), African buffalo (51%) among other 12 animal species. Illegal bushmeat hunting was generally conducted all year round. The law enforcement penalties were considered less deterrent, and most of the hunters intended to continue with illegal hunting. Measures suggested to minimize illegal bushmeat hunting in the SVC included investing and strengthening wildlife law enforcement, provision of community conservation-based incentives and enhancing environmental education and awareness.

Study on the Relationship Between Digital Inclusion, the Level of the Digital Economy and Rural Household Consumption

It is crucial to expand domestic demand and enhance the driving effect of consumption on economic development in order to achieve sustainable economic growth in China. There is considerable scope for improvement in the consumption level of rural households when compared with that of urban households. This paper employs data from 2104 rural Chinese households to investigate the impact of digital inclusion on rural households’ consumption expenditures and to consider the moderating effect of the development of the digital economy. The findings are intended to provide useful guidance for the promotion of digital inclusion and the growth of rural households’ consumption. The study finds that digital inclusion significantly promotes rural households’ consumption expenditures, particularly for enjoyable consumption. Furthermore, the level of regional digital economic development exerts a positive moderating influence on the impact of digital inclusion on rural households’ consumption. Additionally, there are notable differences in the impact of digital inclusion on the consumption of households with varying levels of assets. Therefore, boosting rural residents’ acceptance and utilization of digital technology, refining the digital financial service system and infrastructure, and providing policy support for the development of digital inclusion can promote sustained growth in rural household consumption in China.

Low access to animal protein, oils and snack caused stunted among 25-59 months old children in Jakarta urban slum during pandemic

Children aged 23-59 months were disproportionately affected by stunting during the COVID-19 pandemic due to economic hardship. This study aimed to investigate the association between food expenditure and the type of food source with stunting among under-five children in Jakarta slums. A comparative cross-sectional design was employed to analyze data from 42 pairs of under-five children and their mothers. Information on household expenditure and food consumption was collected through questionnaires. Statistical comparisons between stunted and non-stunted groups were conducted using independent t-tests and the Mann-Whitney U test. The study revealed a significantly lower overall food expenditure among stunted children compared to their non-stunted counterparts. Notably, spending on animal-based foods and snacks was considerably lower in the stunted group. These findings were confirmed by child nutrient intake, which showed significant differences in energy and macronutrient consumption from animal sources between the two groups. The results highlight the critical role of food expenditure and dietary quality in preventing stunting, particularly in vulnerable populations. In challenging economic conditions, prioritizing allocations for nutrient-dense foods, such as those derived from animal sources, is essential.

Цифровой двойник территориально-производственного комплекса полного цикла: шаблон проектирования при принятии решений

The paper proposes a conceptual approach to creating digital twins of full-cycle territorial production complexes for Agriculture 5.0. The cycle completeness lies in the closure (full or partial) of the product and service flows within the complex itself, considering the final consumption of households that provide the production part of the complex with a labour resource. At the same time, individual production stages are usually distributed among different enterprises within the latter. The problem of obtaining a sufficient amount of uniformly structured data to manage the complex as a whole can be solved by using the object relations method, which is the basis of the DATA-CI platform. This platform does not replace the IT systems (APCS, MES, ERP, BI) successfully operating at the enterprises of the complex, but integrates them, providing the ability to manage in real time according to the Continuous Intelligence (CI) concept. The paper gives an example of implementing a full-cycle economy model in the agro-industrial complex on this platform for four production stages (including households). The work shows the possibilities of visualizing the system state in real time on the platform mnemonic diagram and the principle of collecting and storing data on all events and processes in the system that affect the production of specific units (accounting units).

The Role of Poverty and Gender in Shaping Households’ Energy Consumption Patterns in Selected European Countries

In the context of Sustainable Development Goals, declining poverty (Goal 1), achieving gender equality (Goal 5), and ensuring access to clean and affordable energy (SDG7) are still behind track, and the gaps are not yet ready to be rapidly fulfilled. As part of Goal 7, energy consumption-related targets still lack the potential to be targeted. Considering these elements, this study aims to determine the impact of poverty and gender equality on the energy consumption of households in several European countries. Using data from 2010 to 2022 and the moments’ quantile regression method combined with a pooled OLS based on Driskoll-Kraay estimators, we found statistically significant results regarding the impact of poverty and gender on household energy consumption. These findings’ significance will direct policy design towards those meaningful tools that will increase energy efficiency, address energy poverty, and ensure energy just transition, leaving no one behind. Based on the main findings, the policymakers can understand that a mix of policies is significantly more efficient. In such circumstances, social and economic inequalities will not successfully address development issues without including key environmental priorities, such as emissions mitigation and energy consumption patterns.

Remittances and financial development: catalysts for poverty reduction in transition economies

ABSTRACT This study examines the impact of remittances and financial development on poverty reduction in 16 transition economies from 1997 to 2020 using a PMG-ARDL model. Results show that remittances significantly lower poverty by boosting household consumption and health outcomes, particularly infant mortality rates. Additionally, financial development is strongly linked to poverty reduction, enabling investment in education and health. Findings suggest that a robust financial sector enhances the positive effects of remittances. Policy recommendations include enhancing remittance channels, financial literacy, and institutional strength, providing insights for sustainable poverty reduction in transition economies.

Can financial literacy enhance household financial risk asset allocation? Based financial planning and the mediating role of Taoist culture

PurposeThis study examines the impact of enhanced financial literacy on the allocation of risky assets within household financial portfolios, while also considering the mediating role of Taoist cultural influences.Design/methodology/approachThe authors applied ordered probit models and ordinary least squares estimation to analyze a sample of 19,015 data collected from the 2019 Chinese Urban Household Consumer Finance Survey.FindingsThe results demonstrate that an improvement in financial literacy significantly contributes to an increase in residents’ allocation of risky assets. This enhancement in financial literacy, achieved through financial planning and a reduction in the influence of Taoist culture, effectively bolsters the quantity of risky financial assets held. Moreover, the impact of financial literacy on the allocation of risky financial assets is particularly pronounced among residents aged 45–55 and those aged 35–45 and older.Practical implicationsThe practical implications suggest that policymakers and relevant departments may consider strategies to encourage greater participation in the financial market by promoting Taoist culture in an appropriate and reasonable manner while widely carrying out financial education for residents and improving their financial literacy.Originality/valueWhile previous studies have not focused on the impact of traditional culture on financial decision-making allocations, this study investigates the significant impact of financial literacy and Taoist culture on risky asset allocations, highlighting in particular the significant impact on middle-aged residents. This provides a nuanced understanding of how financial literacy affects investment behavior and offers insights for policymakers to effectively tailor financial education programs.

Key drivers of household food security and consumption patterns in the post-Covid-19 pandemic: Lessons from Umzimvubu and Ntabankulu Local Municipalities, Eastern Cape Province, South Africa

Achieving food security and ensuring the consumption of healthy, nutritious food, and/or balanced diets for most rural households continues to be a challenge. This poses a significant barrier to achieving Sustainable Development Goals (SDGs) 1 (No Poverty) and 2 (Zero Hunger), disproportionately affecting rural communities and leaving fundamental human needs, such as reliable food access, unfulfilled. The current study, therefore, sought to assess the key drivers of household food security and consumption patterns in the post-COVID-19 pandemic. Primary data was collected from 165 randomly selected rural households to address the aim of the study. Descriptive analysis, Household Food Insecurity Experience Scale, and Multivariate binary logistic regression using Stata 15 software were employed to analyze the data. Descriptive statistics revealed variability in the consumption of various food groups, with starchy food (main staples and pulses) being the most consumed, followed by oil, meat or fish, sugar, vegetables, fruit, and milk (products). The results further indicated that households in these communities lack dietary diversification and balanced diets, with limited choice and reliance on affordable starchy foods. Notably, this monotonous diet regime has significant implications for micronutrient deficiencies, increased risk of chronic diseases, and compromised overall health and well-being, underscoring the need for targeted nutrition interventions to address the pervasive hunger and malnutrition in these communities. The main drivers of food insecurity in Umzimvubu and Ntabankulu municipalities are household income, monthly food expenditure, access to financial credit and the number of employed and unemployed household members. These factors, combined, significantly condition household food security status and consumption patterns across these rural areas. The study emphasizes the urgent need for targeted interventions to address the lack of formal qualifications, high unemployment and the high rate of food insecurity across these rural communities. Skill development, economic empowerment and financial inclusion for rural households are the most important strategic elements that should be considered when developing strategies to eradicate poverty and hunger. Households in rural areas should be enlightened about the importance of having and consuming balanced diets and the benefits of participating in farming. Key words: Food security, post-COVID-19, binary logistic regression, household food consumption

Factors Affecting CO2 Emissions from Direct Energy Household Consumption in Vietnam

The energy sector is pivotal in Vietnam’s commitment to achieving net-zero emissions by 2050. This study employs a combination of Structural Decomposition Analysis (SDA) and decoupling approaches based on data from Vietnam’s energy statistics and the Vietnam Living Standards Survey (VHLSS) for 2016, 2018, and 2020. The primary aim is to elucidate the effects of direct energy consumption by household groups on CO2 emissions, examine factors affecting emissions, and clarify the relationship between CO2 emissions from household energy consumption and economic growth in Vietnam. Research results underscore that household groups make considerable use of electricity and Liquefied Petroleum Gas (LPG), simultaneously reducing the proportion of firewood, rice husk, sawdust, agricultural by-products and other fuels. The decrease in energy intensity emerges as the primary factor in lowering household emissions, while population growth and economic efficiency exert the opposite effect. Additionally, the research reveals disparities in emissions between urban and rural areas, similarly among household groups within the given location. Despite maintaining a robust decoupling status between emissions from household consumption and economic growth, unsustainable risks persist, particularly with the increase in electricity demand. The study also highlights the uneven impact of the COVID-19 epidemic on CO2 emissions across household groups. Drawing upon these findings, several recommendations are proposed to control CO2 emissions from direct energy household consumption to facilitate the most effective household decarbonisation process while ensuring sustainable economic growth in Vietnam.

Foreign direct investment and poverty in Uganda: what is the relationship?

PurposeThis paper investigates the relationship between foreign direct investment (FDI) and poverty in Uganda.Design/methodology/approachUsing annual time series data from 1983 to 2021, we use the autoregressive distributed lag bounds testing method to cointegration for test the above relationship. Considering the multidimensionality of poverty, we proxy poverty by three proxies: household consumption expenditure, infant mortality rate and life expectancy.FindingsThe findings reveal that FDI is important for poverty reduction in both the long- and short-run when using household consumption and infant mortality rate, holding other factors constant. This relationship is robust to alternative specifications and estimation methods. This paper establishes that FDI does matter for poverty reduction in both the short and long run. However, when life expectancy is used to proxy poverty, results show a positive relationship between FDI and poverty.Originality/valueThe long-run relationship between FDI and poverty largely suggests that tackling poverty may require reforming the economic environment by addressing bottlenecks that hinder economic growth, which is a key component in poverty reduction. Thus, it is important to ensure that government expenditure is directed to the productive sectors of the economy, such as education and infrastructure, among others that are paramount in expanding the productive capacity of the economy, which in turn is crucial for poverty reduction.

Can credit promote rural transformation? Evidence from district-level data in Bangladesh

In this paper, we assess the impact of credit on rural transformation in Bangladesh. District-level rural data from three waves of the Bangladesh Household Income and Expenditure Survey (HIES) were analyzed using ordinary least squares and fixed-effects panel regression analysis. The findings indicate that increasing household credit significantly stimulated the production of high-value agricultural commodities and expanded opportunities for non-farm employment in rural areas. Robustness analysis using access to credit instead of average household credit confirmed the positive relationship between credit and rural transformation at the district level. Access to education and electricity was also found to be positively related to rural transformation; on the other hand, the decline in land per capita was directly related to rural non-agricultural employment opportunities. Our findings highlight the necessity of improving credit accessibility, enhancing capacity-building skills and knowledge of both institutions and individuals, and implementing financial literacy programs for an inclusive, sustainable rural transformation in Bangladesh.

Determinants of household dietary diversity and consumption patterns of selected food groups among rural households in Nigeria's Northeast and Northwest regions

This study explored the factors affecting dietary diversity among rural households in Northwest and Northeast Nigeria. It utilized the fourth-round survey data from 1,418 households across five states (Adamawa, Borno, Katsina, Yobe, and Zamfara), collected by the Data in Emergencies (DIEM) Monitoring System under the United Nations Food and Agriculture Organization (FAO). Household Dietary diversity was measured on a 0–12 scale, and its determinants were analyzed using Poisson regression. The findings revealed moderate dietary diversity, with an average Household Dietary Diversity Score (HDDS) of 6.4 (SD ± 2.7) on a 0–12 scale. Households predominantly consumed starchy foods, with cereals reported by 94.7% of the respondents. Several factors were identified to significantly influence household dietary diversity (HDDS). Larger household size was negatively associated with HDDS (β = -0.0323, SE = 0.0052, p &lt; .0001), as was severe food insecurity, which was inversely related to dietary diversity (β = -0.3103, SE = 0.0635, p &lt; .0001). Additionally, regional disparities were observed, with households in Yobe (β = -0.0924, SE = 0.0370, p = .0127) and Zamfara (β = -0.1171, SE = 0.0356, p = .0010) reporting significantly lower HDDS compared to those in Adamawa. Households engaged in crop production (β = -0.1564, SE = 0.0445, p = .0005), livestock production (β = -0.1458, SE = 0.0449, p = .0012), or combined crop-livestock farming (β = -0.1481, SE = 0.0535, p = .0057) also exhibited lower HDDS, likely due to the subsistence nature of agriculture in these regions. In contrast, higher income was positively associated with HDDS (β = 0.1493, SE = 0.0588, p = .0112). Interestingly, exposure to shocks was also linked to increased household dietary diversity (β = 0.1452, SE = 0.0236, p &lt; .0001), suggesting possible short-term adaptations. These findings highlighted the urgent need to improve household dietary diversity in Northeast and Northwest Nigeria through region-specific interventions. Efforts should prioritize diversifying livelihoods by promoting income-generating activities beyond agriculture, such as small-scale businesses, while also encouraging agricultural practices that move beyond subsistence farming. Additionally, implementing safety nets, including cash transfers and food assistance, will help mitigate the impacts of shocks and build resilience. Together, these strategies will foster sustainable improvements in dietary quality and food security in the regions. Key words: Dietary Diversity, Food Insecurity, Food Groups, Rural Households, Northern Nigeria

Voting on a Trade Agreement: Firm Networks and Attitudes Towards Openness

Abstract We exploit a unique event to study the extent to which popular attitudes towards trade are driven by economic fundamentals. In 2007, Costa Rica put a free trade agreement (FTA) to a national referendum. With a single question on the ballot, 59% of Costa Rican adult citizens cast a vote on whether they wanted an FTA with the U.S. to be ratified or not. We merge disaggregated referendum results, which break new ground on anonymity-compatible voting data, with employer–employee, customs, and firm-to-firm transactions data, and data on household composition and expenditures. We document that a firm’s exposure to the FTA, directly and via input–output linkages, significantly influences the voting behaviour of its employees. This effect dominates that of sector-level exposure and is greater for voters aligned with pro-FTA political candidates. We also show that citizens considered the expected decrease in consumer prices when exercising their vote. Overall, economic factors explain 7% of the variation in voting patterns, which cannot be accounted for by non-economic factors such as political ideology, and played a pivotal role in this vote.

On the Economic Impacts of Investment in Road Construction and Maintenance: New Applied CGE Analysis for Guinea‐Bissau

ABSTRACTA general equilibrium externality approach is developed to model the economic outcomes of road construction and maintenance investment funded through government savings/debt in Guinea‐Bissau from 2014 to 2030, equivalent to 1% of current Chinese investment in this country. The model is calibrated using sector elasticities and a social accounting matrix (SAM) that includes informal activities. Additionally, workers and households are categorized as rural or urban, allowing for a flexible analysis of the investment's implications for low‐income and high‐income individuals by setting. A 1% increase in public investment in roads generates productive externalities that enhance productivity growth and influence capital accumulation and reinvestment in sectors not initially targeted by the policy. Transportation costs and intermediate input prices fall in local agricultural production markets, increasing the return on investment in these sectors. Households' income and consumption increase, but food prices decrease, which benefits the urban and rural low‐income groups the most. Chinese investments should be reallocated to competitive sectors capable of increasing value added and contributing to job and income generation.

What has influenced the growth and structural transformation of China’s cultural industry? — Based on the input-output bias analysis

ABSTRACT This study uses the structural decomposition and bias analysis models to find the factors influencing the development of China’s cultural industry. The data comes from the input-output tables published by the National Bureau of Statistics of China from 2012 to 2020. The findings reveal that technological advancement is the main factor influencing the cultural industry, with a significantly greater impact than other factors. Besides, household consumption is another vital element in the industry’s growth, but the impact strength is relatively weaker than technological advancement. Contributions of government consumption, capital formation, and net export are observed to have a minor effect on the cultural industry’s development. Therefore, the government should strengthen its support for the cultural industry, promote the expansion of cultural consumption, and strengthen its support for technological innovation to ensure the long-term development of the cultural industry as a national soft power.

Predicting Energy Consumption Patterns with Advanced Machine Learning Techniques for Sustainable Urban Development

As urbanization continues to expand and evolve in the USA, power demand has increased manifold, and with it has arisen significant environmental problems in the form of increased greenhouse gas emissions and loss of resources. In this paper, we explore how future machine-learning techniques could predict power consumption in U.S. cities. The central aim of this research is to develop advanced machine learning models with the potential to effectively predict energy consumption in cities. This involves not only identifying the key variables behind energy consumption but also selecting and fine-tuning machine learning algorithms that are most capable of understanding the dynamics of urban energy intricacies. This study focuses on the energy consumption patterns in the large cities of the United States, recognizing the diversity of challenges and opportunities presented by different geographic and demographic situations. The dataset used in this research project offered a comprehensive view of energy consumption across various fields of household, commercial, and industrial consumption, giving a holistic view of energy dynamics within cities. It integrated data collected from smart meters that offer granular electricity consumption patterns at the level of individual households and businesses with weather reports that detail ambient conditions governing energy demand, such as temperature and humidity fluctuations. Government energy records add historical context and policy information, further enhancing the dataset and enabling close analysis of trends and patterns in energy consumption. The next phase was to select and train three distinct machine models to explore the energy consumption dataset, namely, Logistic Regression, Random Forest, and XG-Boost algorithms. Random Forest outperformed Logistic Regression and XG-Boost slightly in terms of accuracy and other evaluation metrics. However, all models exhibit relatively low accuracy, suggesting the need for further tuning, feature engineering, or alternative models to improve predictions. In major cities in the U.S. such as Los Angeles, Chicago, and New York, smart power forecasting based on AI is revolutionizing power distribution and power planning in cities. By utilizing advanced machine learning models, these cities can process vast amounts of information and predict power usage with high accuracy. The incorporation of artificial intelligence (AI) in urban power planning has been a defining feature of modern-day power management in the USA. Major cities such as Los Angeles, Chicago, and New York are increasingly adopting AI-powered power forecasting technologies to rationalize power distribution. The integration of machine learning insights in U.S. government-driven green construction is instrumental in driving sustainable construction in infrastructure. By utilizing data-driven approaches, policymakers are in a position to identify the optimal design methods and low-power technologies with high performance in buildings.

Association between family economic decision-making power and survival outcomes among older adults in China: A population-based longitudinal study.

Association between family economic decision-making power and survival outcomes among older adults in China: A population-based longitudinal study.