The fraction of households living with insufficient liquid assets is important to understand consumption responses to income changes. Using harmonized data for 23 European countries over 2010--2023 from the Household Finance and Consumption Survey, we investigate the consumption responses to income changes of hand-to-mouth (HtM) households, grouped into non-HtM, poor HtM and wealthy HtM. Our findings indicate significant variability across countries in the shares of HtM households, with the majority in all countries being wealthy households with sizeable wealth in housing and other real estates. By examining the marginal propensity to consume (MPC) to hypothetical shocks across these households, we find that poor HtM households exhibit the highest MPC, whereas wealthy HtM households display a negative association with the MPC. The relationship for poor HtM households seems to be driven by unobserved factors, whereas the relationship with wealthy HtM is negative and significant even controlling for unobserved preference heterogeneity and households who switch their status. These consumption responses align with life-cycle models with liquidity constraints and precautionary savings.
In this paper, we analyse the impact of international migration on the food consumption and dietary diversity of left-behind households. Using the Kerala migration survey 2011, we study whether households with emigrants (on account of international migration) have higher consumption expenditure and improved dietary diversity than their non-migrating counterparts. We use ordinary least square and instrumental variable approach to answer this question. The key findings are that: a) emigrant households have higher overall consumption expenditure as well as higher expenditure on food; b) we find that international migration leads to increase in the dietary diversity of left behind households. Further, we explore the effect on food sub-group expenditure for both rural and urban households. We find that emigrant households spend more on protein (milk, pulses and egg, fish and meat), at the same time there is higher spending on non-healthy food habits (processed and ready to eat food items) among them.
Epidemiological dynamics are affected by the spatial and demographic structure of the host population. Households and neighbourhoods are known to be important groupings but little is known about the epidemiological interplay between them. In order to explore the implications for infectious disease epidemiology of households with similar demographic structures clustered in space we develop a multi-scale epidemic model consisting of neighbourhoods of households. In our analysis we focus on key parameters which control household size, the importance of transmission within households relative to outside of them, and the degree to which the non-household transmission is localised within neighbourhoods. We construct the household reproduction number $R_*$ over all neighbourhoods and derive the analytic probability of an outbreak occurring from a single infected individual in a specific neighbourhood. We find that reduced localisation of transmission within neighbourhoods reduces $R_*$ when household size differs between neighbourhoods. This effect is amplified by larger differences between household sizes and larger divergence between transmission rates within households and outside of t
Rural electrification initiatives worldwide frequently encounter financial planning challenges due to a lack of reliable market insights. This research delves into the preferences and marginal willingness to pay (mWTP) for upfront electricity connections in rural and peri-urban areas of Nigeria. We investigate discrete choice experiment data gathered from 3,599 households and 1,122 Small to Medium-sized Enterprises (SMEs) across three geopolitical zones of Nigeria, collected during the 2021 PeopleSuN project survey phase. Employing conditional logit modeling, we analyze this data to explore preferences and marginal willingness to pay for electricity connection. Our findings show that households prioritize nighttime electricity access, while SMEs place a higher value on daytime electricity. When comparing improvements in electricity capacity to medium or high-capacity, SMEs exhibit a sharp increase in willingness to pay for high-capacity, while households value the two options more evenly. Preferences for the electricity source vary among SMEs, but households display a reluctance towards diesel generators and a preference for the grid or solar solutions. Moreover, households with ol
In 2022, energy prices skyrocketed across Europe, with average day-ahead spot market prices in Germany 2.43 times higher than the previous year, hinting at future trends. At the same time, electricity infrastructure is expected to be overutilized in some regions in the future due to the uptake of electric vehicles, heat pumps, PV systems, and other appliances in the residential sector. Dynamic electricity pricing for households is proposed as a solution to alleviate infrastructure strain and reduce costs. The question arises as to what impact dynamic electricity prices as recently seen on the day-ahead spot market in Germany would have on the use of flexibility in households and whether it can compensate for cost increases for households. We analyze the cost-effectiveness of utilizing flexibility through a home energy management system (HEMS) and smart meters against increased self-consumption using a HEMS and static tariffs. We show that with higher electricity prices and price spreads, a higher share of households can offset initial investments in HEMS and metering operation costs by utilizing flexibility from their electric vehicles, heat pumps, and battery storage systems. Whil
This research paper delves into the financial behavior of urbanized households in India, specifically focusing on million-plus agglomerations. Using data from the 77th round of the All India Debt and Investment Survey, the study analyzes assets, borrowing patterns, and the usage of financial instruments like bank accounts, e-wallets, and life insurance. The research explores demographic factors, household structures, and district-level parameters to understand the intricate financial landscape. With a focus on low-income households, the study identifies income, education, type of employment, and branches per capita as significant factors influencing financial behavior. The study contributes valuable insights for financial institutions, policymakers, and researchers seeking a comprehensive understanding of the financial lives of urban households in India.
Matching households and individuals across different databases poses challenges due to the lack of unique identifiers, typographical errors, and changes in attributes over time. Record linkage tools play a crucial role in overcoming these difficulties. This paper presents a multi-step record linkage procedure that incorporates household information to enhance the entity-matching process across multiple databases. Our approach utilizes the Hausdorff distance to estimate the probability of a match between households in multiple files. Subsequently, probabilities of matching individuals within these households are computed using a logistic regression model based on attribute-level distances. These estimated probabilities are then employed in a linear programming optimization framework to infer one-to-one matches between individuals. To assess the efficacy of our method, we apply it to link data from the Italian Survey of Household Income and Wealth across different years. Through internal and external validation procedures, the proposed method is shown to provide a significant enhancement in the quality of the individual matching process, thanks to the incorporation of household infor
Households play an important role in disease dynamics. Many infections happening there due to the close contact, while mitigation measures mainly target the transmission between households. Therefore, one can see households as boosting the transmission depending on household size. To study the effect of household size and size distribution, we differentiated the within and between household reproduction rate. There are basically no preventive measures, and thus the close contacts can boost the spread. We explicitly incorporated that typically only a fraction of all household members are infected. Thus, viewing the infection of a household of a given size as a splitting process generating a new, small fully infected sub-household and a remaining still susceptible sub-household we derive a compartmental ODE-model for the dynamics of the sub-households. In this setting, the basic reproduction number as well as prevalence and the peak of an infection wave in a population with given households size distribution can be computed analytically. We compare numerical simulation results of this novel household-ODE model with results from an agent--based model using data for realistic household
Data recording connections between people in communities and villages are collected and analyzed in various ways, most often as either networks of individuals or as networks of households. These two networks can differ in substantial ways. The methodological choice of which network to study, therefore, is an important aspect in both study design and data analysis. In this work, we consider various key differences between household and individual social network structure, and ways in which the networks cannot be used interchangeably. In addition to formalizing the choices for representing each network, we explore the consequences of how the results of social network analysis change depending on the choice between studying the individual and household network -- from determining whether networks are assortative or disassortative to the ranking of influence-maximizing nodes. As our main contribution, we draw upon related work to propose a set of systematic recommendations for determining the relevant network representation to study. Our recommendations include assessing a series of entitativity criteria and relating these criteria to theories and observations about patterns and norms
This study critically evaluates the impact of the Pradhan Mantri Ujjwala Yojana (PMUY) on LPG accessibility among poor households in India. Using Propensity Score Matching and Difference-in-Differences estimators and the National Family Health Survey (NFHS) dataset, the Average Treatment Effect on the interdedly Treated is a modest 2.1 percentage point increase in LPG consumption due to PMUY, with a parallel decrease in firewood consumption. Regional analysis reveals differential impacts, with significant progress in the North, West, and South but less pronounced effects in the East and North East. The study also underscores variance across social groups, with Schedule Caste households showing the most substantial benefits, while Scheduled Tribes households are hardly affected. Despite the PMUY's initial success in facilitating LPG access, sustaining its usage remains challenging. Policy should emphasise targeted interventions, income support, and address regional and community-specific disparities for the sustained usage of LPG.
Technologies to monitor the provision of renewable energy are part of emerging technologies to help address the discrepancy between renewable energy production and its related usage in households. This paper presents various ways householders use a technological artifact for the real-time monitoring of renewable energy provision. Such a monitoring thus affords householders with an opportunity to adjust their energy consumption according to renewable energy provision. In Denmark, Ewii, previously Barry, is a Danish energy supplier which provides householders with an opportunity to monitor energy sources in real time through a technological solution of the same name. This paper use provision afforded by Ewii as a case for exploring how householders organize themselves to use a technological artefact that supports the monitoring of energy and its related usage. This study aims to inform technology design through the derivation of four personas. The derived personas highlight the differences in energy monitoring practices for the householders and their engagement. These personas are characterised as dedicated, organised, sporadic, and convenient. Understanding these differences in ener
This paper investigates the optimal investment, consumption, and life insurance strategies for households under the impact of health shock risk. Considering the uncertainty of the future health status of family members, a non-homogeneous Markov process is used to model the health status of the breadwinner. Drawing upon the theory of habit formation, we investigate the influence of different consumption habits on households' investment, consumption, and life insurance strategies. Based on whether the breadwinner is alive or not, we formulate and solve the corresponding Hamilton-Jacobi-Bellman (HJB) equations for the two scenarios of breadwinner survival and breadwinner's demise, respectively, and obtain explicit expressions for the optimal investment, consumption, and life insurance strategies. Through sensitivity analysis, it has been shown that the presence of health shocks within households has a negative impact on investment and consumption decisions, while the formation of consumption habits increases household propensity for precautionary savings.
This paper examines whether personality influences the allocation of resources within households. To do so, I model households as couples who make Pareto-efficient allocations and divide resources according to a distribution function. Using a sample of Dutch couples from the LISS survey with detailed information on consumption, labor supply, and personality traits at the individual level, I find that personality affects intrahousehold allocations through two channels. Firstly, the level of these traits acts as preference factors that shape individual tastes for consumed goods and leisure time. Secondly, by testing distribution factor proportionality and the exclusion restriction of a conditional demand system, I observe that differences in personality between spouses act as distribution factors. Specifically, these differences in personality impact the allocation of resources by affecting the bargaining process within households. For example, women who are relatively more conscientious, have higher self-esteem, and engage more cognitively than their male partners receive a larger share of intrafamily resources.
I analyze the risk-coping strategies of factory-worker households in early twentieth-century Tokyo. I digitized a unique daily longitudinal household budget survey conducted in Tsukishima, a representative manufacturing area, to examine how consumption was affected by idiosyncratic shocks. I find that although the households were vulnerable and their consumption levels were impacted by these shocks, the estimated income elasticity of indispensable consumption was relatively low in the short run. The results of the mechanism analysis suggest that credit purchases from local retailers helped smooth short-run consumption, highlighting the role of informal credit institutions in mitigating vulnerability among urban worker households.
This article examines the impact of China's delayed retirement announcement on households' savings behavior using data from China Family Panel Studies (CFPS). The article finds that treated households, on average, experience an 8% increase in savings rates as a result of the policy announcement. This estimation is both significant and robust. Different types of households exhibit varying degrees of responsiveness to the policy announcement, with higher-income households showing a greater impact. The increase in household savings can be attributed to negative perceptions about future pension income.
In economic modeling, there has been an increasing investigation into multi-agent simulators. Nevertheless, state-of-the-art studies establish the model based on reinforcement learning (RL) exclusively for specific agent categories, e.g., households, firms, or the government. It lacks concerns over the resulting adaptation of other pivotal agents, thereby disregarding the complex interactions within a real-world economic system. Furthermore, we pay attention to the vital role of the government policy in distributing tax credits. Instead of uniform distribution considered in state-of-the-art, it requires a well-designed strategy to reduce disparities among households and improve social welfare. To address these limitations, we propose an expansive multi-agent economic model comprising reinforcement learning agents of numerous types. Additionally, our research comprehensively explores the impact of tax credit allocation on household behavior and captures the spectrum of spending patterns that can be observed across diverse households. Further, we propose an innovative government policy to distribute tax credits, strategically leveraging insights from tax credit spending patterns. Sim
Use of healthcare services is inadequate in Ethiopia in spite of the high burden of diseases. User-fee charges are the most important factor for this deficiency in healthcare utilization. Hence, the country is introducing community based and social health insurances since 2010 to tackle such problems. This study was conducted cross-sectionally, in March 2013, to assess willingness of rural households to pay for community-based health insurance in Debub Bench district of Southwest Ethiopia. Two-stage sampling technique was used to select 845 households. Selected households were contacted using simple random sampling technique. Double bounded dichotomous choice method was used to illicit the willingness to pay. Data were analyzed with STATA 11. Krinsky and Rob method was used to calculate the mean/median with 95% CI willingness to pay after the predictors have been estimated using Seemingly Unrelated Bivariate Probit Regression. Eight hundred and eight (95.6%) of the sampled households were interviewed. Among them 629(77.8%) households were willing to join the proposed CBHI scheme. About 54% of the households in the district were willing to pay either the initial or second bids prese
Household size impacts the spread of respiratory infectious diseases: Larger households tend to boost transmission by acquiring external infections more frequently and subsequently transmitting them back into the community. Furthermore, mandatory interventions primarily modulate contagion between households rather than within them. We developed an approach to quantify the role of household size in epidemics by separating within-household from out-household transmission, and found that household size explains 41% of the variability in cumulative COVID-19 incidence across 34 European countries (95% confidence interval: [15%, 46%]). The contribution of households to the overall dynamics can be quantified by a boost factor that increases with the effective household size, implying that countries with larger households require more stringent interventions to achieve the same levels of containment. This suggests that households constitute a structural (dis-)advantage that must be considered when designing and evaluating mitigation strategies.
During the COVID-19 crisis, policymakers have implemented social bubble merging strategies, which allowed people from different households to meet and interact. Although these measures can mitigate the negative effects of extreme isolation, they also introduce additional contacts that may facilitate disease spread. As a result, several modeling studies have explored the epidemiological impact of different household-merging strategies, in which the selection of households to be merged is guided by specific demographic criteria, such as household size or the age composition of their members. Here, we investigate an alternative pairing strategy in which households are merged according to the number of economically active (working) members. We develop a mathematical model of household networks using real demographic data from multiple regions around the world, and simulate a lockdown scenario in which only economically active individuals can leave their households, while the remaining non-working members stay indoors. By using numerical simulations and the generating function technique, we then estimate the epidemic risk for different household merging strategies. We find that merging
There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL) based models that focus solely on a single type of agents, such as households, firms, or the government. Such an approach overlooks the adaptation of interacting agents thereby failing to capture the complexity of real-world economic systems. In this work, we consider a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government. In particular, we focus on the crucial role of the government in distributing tax credits to households. We conduct two broad categories of comprehensive experiments dealing with the impact of tax credits on 1) households with varied degrees of myopia (short-sightedness in spending and saving decisions), and 2) households with diverse liquidity profiles. The first category of experiments examines the impact of the frequency of tax credits (e.g. annual vs quarterly) on consumption patterns of myopic households. The second category of experiments focuses on the impact of varying tax credit distribution strat