Number Of Household Members Research Articles

Czech Consumers' Preference for Organic Products in Online Grocery Stores during the COVID-19 Pandemic.

A major advantage of online organic produce shopping is the fact that it saves energy and reduces emissions otherwise generated by customers during their time spent on the road and while shopping. Organic products in general positively impact sustainability, the environment, and the regions of their origin along with the social changes in these regions and further rural development. Moreover, these products positively impact the perceived health benefits and quality of food labeled as organic. The Czech Republic has currently seen a rise in organic food purchasing and supply trends. This study maps the factors possibly influencing consumers’ decision to go shopping for organic food online. Observed factors include the following demographic characteristics of consumers (respondents): gender, age, education, household income, number of children in the household and number of household members. A total of 757 respondents from the Czech Republic from September 2020 to December 2020 took part in the research. Logistic regression, used for data processing, identified the statistically significant effects of education, income and number of household members on online purchases. These conclusions were confirmed by a detailed contingency tables analysis, including the almost monotonous trend of the dependencies, with only minor deviations in a maximum of one category. The strongest influence of some categories on the emergence of partial dependencies was found by residue analysis. The research confirmed that the frequency of online grocery shopping increases significantly with increasing education and income of respondents and decreases with increasing the number of household members. Most respondents apparently shop for groceries online because of time savings, better product choice and more convenient and easier search.

Factors Influencing Household Expenditure of Rice Farmers in Ketawang Village Gondang District Nganjuk Regency

This study aims to analyze the factors that affect the household expenditure of rice farmers. The population in this study amounted to 573 farmers. The number of samples is determined by 10% of the population. The samples obtained in this study were 57 and rounded up to 60. The sample was selected by purposive sampling with criteria: farmers who own and cultivate the land. The data processing and analysis method used these tenuous equation 2 SLS with the thing of SPSS. The results showed that the factors that affect the household expenditure of farmers in Ketawang Village are as follows: 1) Food consumption blocks are influenced by disposable income, the number of household members and education investment. 2) Non-food consumption blocks are affected by disposable income, education investment and wife's education. 3) Production investment blocks are influenced by disposable income, education investment and total household consumption. 4) The education investment block is influenced by disposable income and the number of children in school. 5) The health investment block is influenced by disposable income, the number of household members, the wife's age and the husband's age.

The effects of individual and community-level factors on community-based health insurance enrollment of households in Ethiopia.

IntroductionCommunity-based health insurance (CBHI) is a type of volunteer health insurance that has been adopted all over the world in which people of the community pool funds to protect themselves from the high costs of seeking medical care and treatment for the disease. In Ethiopia, healthcare services are underutilized due to a lack of resources in the healthcare system. The study aims to identify the individual and community level factors associated with community-based health insurance enrollment of households in Ethiopia.MethodsData from the Ethiopian mini demographic and health survey 2019 were used to identify factors associated with community-based health insurance enrollment of households in Ethiopia. Multilevel logistic regression analysis was used on a nationally representative sample of 8,663 households nested within 305 communities, considering the data’s layered structure. We used a p-value<0.05 with a 95% confidence interval for the results.ResultThe prevalence of community-based health insurance enrollment in Ethiopia was 20.2%. The enrollment rate of households in the scheme was high in both Amhara (57.9), and Tigray (57.9%) regions and low (3.0%) in the Afar region. At the individual level; the age of household heads, number of children 5 and under, number of household members, has land for agriculture, has a mobile telephone, receiving cash of food from the safety Net Program, Owning livestock, and herds of farm animals, wealth index, and at the community level; the region had a significant association with community-based health insurance enrollment.ConclusionBoth individual and community-level characteristics were significant predictors of community-based health insurance enrollment in households. Furthermore, the ministry of health, health bureaus, and other concerning bodies prioritize clusters with low health insurance coverage to strengthen health system financing and intervene in factors that negatively affect the CBHI enrollment of households.

In the thick of relationships?

The study presents the structure and intensity of the relationships of the Hungarian population over 16 years of age through a descriptive analysis of four variables measuring the frequency of personal and distance contact with relatives and friends from the EU-SILC 2015 survey. Ac­cording to the data, the relationship structure is on average balanced, half of the relationships are related to relatives or friends, and the relative proportions of personal and long-distance relationships are similar. According to our results, in addition to age, the financial situation of the household has a significant correlation with the characteristics of the relationship structu­re. One of the lessons of multivariate regression models is that the effect of other background variables on the relationship structure intensifies in parallel with aging, leading to a deepening of relationship inequalities among the elderly. Another lesson of the models is that the inclu­sion of household characteristics (financial situation, number of household members, material transfer relationship with other households) has a significant effect on the mechanism of indi­vidual background variables, thus confirming that a deeper study of relationship intensity and relationship structure within the household is essential. At the end of our analysis, we compiled clusters based on the intensity of relationships, the direction of relationships, and the channel of contacting, with a relative majority of more than one-third of the respondents with extremely weak relationship embeddedness.

Insights into hepatitis E virus epidemiology in Croatia.

BACKGROUNDHepatitis E virus (HEV) is an emerging virus of global health concern. The seroprevalence rates differ greatly according to geographic region and population group.AIMTo analyze the seroprevalence of HEV in exposed (animal-related professions) and nonexposed populations, as well as solid organ and hematopoietic stem cell transplant patients.METHODSForestry workers (n = 93), hunters (n = 74), and veterinarians (n = 151) represented the exposed population. The general population (n = 126) and pregnant women (n = 118) constituted the control group. Transplant patients included liver transplant recipients (LTRs) (n = 83), kidney transplant recipients (KTRs) (n = 43), and hematopoietic stem cell transplant recipients (HSCRs) (n = 39). HEV immunoglobulin G antibodies were detected using the enzyme-linked immunosorbent assay and confirmed by the immunoblot test.RESULTSThe HEV seroprevalence significantly differed between groups: Veterinarians 15.2%, hunters 14.9%, forestry workers 6.5%, general population 7.1%, and pregnant women 1.7%. In transplant patients, the seropositivity was highest in LTRs (19.3%), while in KTRs and HSCRs, the seroprevalence was similar to the general population (6.9% and 5.1%, respectively). A significant increase in seropositivity with age was observed from 2.9% in individuals less than 30 years to 23.5% in those older than 60 years. Sociodemographic characteristics (sex, educational level, area of residence, and number of household members), eating habits (game meat, offal, and pork products consumption), and environmental and housing conditions (drinking water supply, type of water drainage/sewer, waste disposal, domestic animals) were not associated with HEV seropositivity. However, individuals who reported a pet ownership were more often seropositive compared to those who did not have pet animals (12.5% vs 7.0%).CONCLUSIONThe results of this study showed that individuals in professional contact with animals and LTRs are at higher risk for HEV infection. In addition, age is a significant risk factor for HEV seropositivity.

Childhood anaemia levels among under-5 children in Namibia and their associated sociodemographic factors: A multivariate ordinal modelling approach.

Anaemia is a serious global public health problem with high prevalence (>40%) in children particularly in low- and middle-income countries including Namibia with a current 46.1% prevalence rate. This study was aimed at examining the sociodemographic factors influencing the occurrence of childhood anaemia levels in Namibia. A multivariate ordinal regression model was applied to statistically identify potential sociodemographic factors associated with anaemia levels among children under-5 years old using data collected from the 2013 NDHS. The odds of having mild anaemia level was lower for sociodemographic characteristics such as mother's age, total children ever born, health insurance coverage, child's residency, child's age and main language spoken at home. The odds of having moderate anaemia level was higher for characteristics such as mother's age, place of residence, highest education level and child's diarrhoea status, while it was lower for characteristics such as age of head of household, total children ever born, health insurance coverage and sex of child. Similarly, the odds of having severe anaemia level was higher for characteristics such as region, place of residence, highest education level, number of household members, wealth index, health insurance coverage, child's residency and child's diarrhoea status, while it was lower for characteristics such as total children ever born and sex of child. It is therefore recommended that the policies and practices concerning anaemia diagnosis, treatment and prevention in the country be substantially revised by policy-makers, starting with the known prevalent causes and identified sociodemographic factors from this study.

음식 배달 및 1인 가구가 생활 폐기물 발생량에 미치는 영향

Purpose - This study empirically analyzes the effects of food delivery service use frequency and the proportion of single-person households on the generation of MSW (Municipal Solid Waste). Additionally, the study analyzes MSW increase during the COVID-19 period. Design/methodology/approach - The study uses annual information on MSW and related characteristics of 228 Si, Gun, and Gu, which are administrative districts of Korea, from 2015 to 2020. Panel fixed effect model is employed for estimations. Findings - The study finds that food delivery and single-person household have significant effects only on recyclable and standard garbage bag MSW, respectively, not on all produce types of MSW. Additionally, it is estimated that, during the COVID-19 period, the increasing effect of food delivery is intensified while single-person households diminish its increasing effect. Lastly, the study confirms that the marginal effect on the increasing effect of MSW generation by the number of household members. Research implications or Originality - The estimated results are expected to provide useful information for the related policies. In particular, it is necessary to focus more on recyclable MSW concerning the developing delivery culture, and regarding changes in the family structure, focus more on the number of household members than single-person households.

Analisis struktur pengeluaran rumah tangga di Provinsi Jambi Tahun 2020

The purpose of this study was to determine the effect of household groups on the structure of household expenditures. The data used in this study is the March 2020 Susenas raw data, namely Susenas data on household scores in 11 districts and cities of Jambi Province. The data analysis method used is descriptive method. The analytical tool used is Multiple Linear Regression (multiple regression). The results showed that in the upper urban household group, the independent variables were: household income, number of household members, age of the head of the household, education level of the head of the household, gender of the head of the household, the status of the head of the household in the main occupation, and the status of the household head. marriage of the head of the household can explain household non-food competition by 94.3 percent and together the independent variables can have an influence on household non-food expenditure. In the lower urban household group, the independent variables cannot explain expenditures other than household food because they only have an R2 value of 2.7 and together the independent variables also cannot have an effect on expenditures other than household food. As well as in the upper rural household group, the independent variable can explain household non-food competition by 65.5 percent and the independent variable can have an influence on household non-food expenditure.

Determining the Unemployment Rate in Indonesia during the COVID-19 Pandemic

Unemployment is one of Indonesia's main economic development problems during the COVID-19 pandemic. This study aims to determine the chances of a person becoming unemployed in Indonesia during the COVID-19 pandemic, which individual and household characteristics influence. The data were collected from National Labour Force Survey (SAKERNAS) in August 2020. The data were analysed using a logistic regression model. The result proved that there was a significant opportunity between individual characteristics, namely age, gender, household head status, marital status, education level, training, work experience, and household characteristics, namely the number of household members, against unemployment. While the location of the household is not significant because this research was only conducted in the city centre so that with the same characteristics of the city in each region, the labour force living in the city is less likely to become unemployed. The coefficient value showed that women are more likely to become unemployed. Thus, the government is expected to focus on providing facilities to increase the human capital of the young female workforce, such as creating job training programs and mastering information and technology, given the changes in the digital age.

Use of insecticide treated nets in children under five and children of school age in Nigeria: Evidence from a secondary data analysis of demographic health survey.

Use of insecticide treated nets (ITN), one of the most cost-effective malaria interventions contributes to malaria cases averted and reduction in child mortality. We explored the use of ITN in children under five (CU5) and children of school age to understand factors contributing to ITN use. A cross-sectional study analyzed 2018 Nigeria Demographic and Health Survey data. The outcome variable was CU5 or children of school age who slept under ITN the night before the survey. Independent variables include child sex, head of household's sex, place of residence, state, household owning radio and television, number of household members, wealth quintile, years since ITN was obtained and level of malaria endemicity. Multi-level logistic regression model was used to access factors associated with ITN use among children. In total, 32,087 CU5 and 54,692 children of school age were examined with 74.3% of CU5 and 57.8% of children of school age using ITN the night before the survey. While seven states had more than 80% of CU5 who used ITN, only one state had over 80% of school children who used ITN. ITN use in CU5 is associated with living in rural area (aOR = 1.20, 95% CI 1.14 to 1.26) and residing in meso endemic area (aOR = 3.1, 95% CI 2.89 to 3.54). While In children of school age, use of ITN was associated with female headed households (aOR = 1.14, 95% CI 1.09 to 1.19), meso (aOR = 3.17, 95% CI 2.89 to 3.47) and hyper (aOR = 14.9, 95% CI 12.99 to 17.07) endemic areas. Children residing in larger households were less likely to use ITN. This study demonstrated increased use of ITN in CU5 from poor households and children living in rural and malaria endemic areas. Findings provide some policy recommendations for increasing ITN use in school children.

Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm

BackgroundMore than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells or hemoglobin in the blood. The world health organization divides anemia in pregnancy into mild anemia (Hb 10–10.9 g/dl), moderate anemia (Hb 7.0–9.9 g/dl), and severe anemia (Hb < 7 g/dl). This study aims to predict the level of anemia among pregnant women in the case of Ethiopia using homogeneous ensemble machine learning algorithms.MethodsThis study was conducted following a design science approach. The data were gathered from the Ethiopian demographic health survey and preprocessed to get quality data that are suitable for the machine learning algorithm to develop a model that predicts the levels of anemia among pregnant. Decision tree, random forest, cat boost, and extreme gradient boosting with class decomposition (one versus one and one versus rest) and without class decomposition were employed to build the predictive model. For constructing the proposed model, twelve experiments were conducted with a total of 29,104 instances with 23 features, and a training and testing dataset split ratio of 80/20.ResultsThe overall accuracy of random forest, extreme gradient boosting, and cat boost without class decompositions is 91.34%, 94.26%, and 97.08.90%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus one is 94.4%, 95.21%, and 97.44%, respectively. The overall accuracy of random forest, extreme gradient boosting, and cat boost with one versus the rest are 94.4%, 94.54%, and 97.6%, respectively.ConclusionFinally, the researcher decided to use cat boost algorithms with one versus the rest for further use in the development of artifacts, model deployment, risk factor analysis, and generating rules because it has registered better performance with 97.6% accuracy. The most determinant risk factors of anemia among pregnant women were identified using feature importance. Some of them are the duration of the current pregnancy, age, source of drinking water, respondent’s (pregnant women) occupation, number of household members, wealth index, husband/partner's education level, and birth history.

Determinants of age at first sex inequality between women and men youth in Uganda: A decomposition analysis.

Teenage pregnancies and sexually transmitted diseases are major public health problems in Uganda. Early sexual debut is one of the main routes of these public health problems. This study aimed to identify factors that explain age at first sex inequality between men and women Ugandan youth. This study used secondary data from a cross-sectional Uganda demographic health survey (2016). Participants were 10 189 sexually experienced youth. Using Stata 14, intermediary analysis was done to assess the statistical association between explanatory variables and age at first sex in a multiple logistic regression analysis. Oaxaca decomposition was used to decompose factors that explain inequalities in age at first sex between men and women youth. Intermediary results showed Islam, many household members, residing in the eastern region, and being divorced/widowed were predictors of early age at first sex. While secondary education, higher education, blue-collar jobs, and being 20 to 30 years old were protective factors against early age at first sex. Material, behavior/cultural, psychosocial, and demographic explanatory factors jointly explained a statistically significant portion of the observed gap in early age at first sex between women and men youth. More women were at a disadvantage at an early age at first sex compared to men youth. About 96.37% of this gap was explained by unequal distribution of material, behavior/cultural, psychosocial, and demographic factors between men and women youth. Relationship to household head (49%), education (16.87%), occupation (8,94%), number of household members (8.57%), using the internet (7.99%), and reading newspapers or magazines (4.39%) made a significant contribution to the explanation of early age at first sex inequality between men and women youth. Results showed early age at first sex inequality between women and men youth that favored men. Programs designed to address early age at first sex and related health outcomes must combat inequities in education, employment opportunities, access to sexual reproductive information through internet, and newspapers or magazines between men and women youth. They should also foster household relationships and monitor girls.

Faktor-Faktor yang Mempengaruhi Pengeluaran Rumah Tangga Miskin di Desa Keramas, Kabupaten Gianyar

ABSTRACT&#x0D; The problem of poverty in Gianyar Regency is that the poverty rate is still relatively high. Therefore poverty is a shared responsibility. The aims of this study are: 1) to analyze the simultaneous effect of household income, intensity of traditional and religious activities, household consumption, and number of household members on the expenditure of poor households in Keramas Village, Gianyar Regency, 2) to analyze the partial effect of household income. households, the intensity of traditional and religious activities, household consumption, and the number of household members on the expenditure of poor households in Keramas Village, Gianyar Regency, 3) to analyze the role of the number of household members in moderating the effect of consumption on the expenditure of poor households in Keramas Village. , Gianyar Regency. The population in this study were 157 poor households in Keramas Village, Gianyar Regency using the Slovin formula, then 61 poor households were obtained. The sampling method is Stratified Random Sampling with the type of Proportionate Stratified Random Sampling and data collection techniques through observation, interviews, and in-depth interviews. The analytical technique used in this research is Moderation Regression Analysis. The results of the study show the results: 1) household income, household consumption, the intensity of traditional and religious activities, and the intensity of traditional and religious activities simultaneously have a significant effect on the expenditure of poor households in Keramas Village, Gianyar Regency, 2) household income, household consumption, and the number of household members partially have a positive and significant effect on the expenditure of poor households in Keramas Village, Gianyar Regency, the variable intensity of traditional and religious activities has no effect on the expenditure of poor households in Keramas Village, Gianyar Regency, 3) the number of family members weaken the influence of household consumption variables on the expenditures of poor households in Keramas Village, Gianyar Regency.&#x0D; &#x0D; &#x0D; Keywords: income, intensity of customary activities, household consumption, number of family members, poor household expenditure&#x0D;

Alternative tariff structures and household composition: Evidence from Spain's Valencia region

Water pricing is one of the fundamental tools for water resources management. However, the current tariff structure in Spain has associated problems in relation to the composition of households. This paper analyses the current tariffs of Spain's Valencia region and studies the effect of alternative tariff structures. The results show an imbalance irrespective of the tariff applied even when the number of household members is considered, as the relationship between the number of household members and water consumption is not linear. Therefore, the problem is not the tariff structure per se but not including the composition of the household in the tariff.

Impact of COVID-19 pandemic lockdown on the mental health status of Indian undergraduate health professional students

Introduction and Aim: The psychological effects of the COVID-19 outbreak and related lockdown measures among the Indian population are less known. Undergraduate students are prone to mental health problems making them more vulnerable to the impact of the pandemic and its lockdown. This study sought to determine the impact of the COVID-19 pandemic lockdown on the mental health status of undergraduate health professional students of a tertiary care setup. Materials and Methods: A cross-sectional study was carried on undergraduate health professional students of a private Deemed-to-be University. 246 participants took part in this study. A questionnaire was provided as a Google form containing 26 questions. It captured the main dimensions of mental health-related issues in undergraduate students. Paired ‘t’ test was employed to compare the mental health status of the participants before and during the lockdown. Results: A significant majority of our participants experienced stress during the lockdown. There was no significant effect of gender, the number of household members, or prior mental health challenges on the mental status of the students. COVID-19 updates on central government introduced mobile application significantly decreased stress but reports from television/radio and social media significantly contributed to stress. Students engaged in online academic activities of more than four hours per day were significantly stressed. Conclusion: Undergraduate health professional students in our study were significantly more stressed during the lockdown as compared to the pre-lockdown period. Academic online activity also significantly contributed to stress with more than four hours of online engagement precipitating severe stress.

The Impact of the COVID-19 Pandemic on Household Financial Well-being

Purpose: The current research is one of the few studies investigating the impact of COVID-19 on shattered households' financial well-being in light of Friedman's Theory of Consumption Function. It explored if there are statistical differences in the impact of COVID-19 due to the demographic variables of the study.&#x0D; Design/methodology/approach: The study is descriptive and analytical. It used an online survey that targeted respondents representing 387 Palestinian households randomly selected from the community of the Gaza Strip.&#x0D; Findings: The results showed a negative impact of the COVID-19 pandemic on households' financial well-being. It also revealed these households' lack of financial resilience due to the combined effect of the pandemic and the concurrent siege and its economic drawbacks. The statistical results demonstrated no statistically significant differences in the households' mean financial well-being scores due to gender before and during the COVID-19 pandemic. In contrast, it was found that there are statistically significant differences in the households' mean scores of financial well-being due to the (education level, number of household members, employability status, and the number of households' breadwinners) before and during the COVID-19 pandemic.&#x0D; Research limitations/implications: The researcher conducted an online survey among the general population in the Gaza Strip during the COVID-19 pandemic between the 2nd and 5th of June 2021. Although the results of this study are based on the Palestinian context, the main hypotheses can be tested in other countries to assess the impact of COVID-19 on household financial well-being, especially in emerging countries.&#x0D; Originality/value: This study is the first to address the financial and economic effects of the COVID-19 pandemic on households already shattered and torn by suffrage political and economic inequalities. It also demonstrates how people in emergencies tend to respond to the resulting conditions of the pandemic in the light of Friedman's Theory of the Consumption Function.

Identifying Characteristics of Households Recipient of the Government’s Social Protection Program

According to Statistics Indonesia, the number of poor people increased by 1,12 million people in March 2020. In March 2021, the percentage of poor people increased by 0,36 points compared to March 2020. The percentage of poor people in Banten Province has increased in the last three years (2019-2021). One way to reduce poverty by the government is to increase social protection programs. The characteristics of households receiving social protection programs were identified by modeling the classification of households using the random forest technique, obtaining important variables using the permutation feature importance and Shapley additive explanations interpretation techniques, and analyzing the most important variables from the two interpretations methods. Handling the imbalance data on the response variables using SMOTE technique and evaluating the classification model obtained an AUC value of 0,718. The important variables were selected from the permutation feature importance and Shapley additive explanation methods based on a consistent ranking at the top. Shapley’s additive explanation was more consistent than permutation feature importance. Six important, namely capita expenditure, education of the head of household, age of head of household, source of drinking water, floor area, and the number of household members.

Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household

The first task for eradicating poverty is accurate poverty identification. Deep poverty identification is conducive to investing resources to help deeply poor populations achieve prosperity, one of the most challenging tasks in poverty eradication. This study constructs a deep poverty identification model utilizing explainable artificial intelligence (XAI) to identify deeply poor households based on the data of 23,307 poor households in rural areas in China. For comparison, a logistic regression-based model and an income-based model are developed as well. We found that our XAI-based model achieves a higher identification performance in terms of the area under the ROC curve than both the logistic regression-based model and the income-based model. For each rural household, the odds of being identified as deeply poor are obtained. Additionally, multidimensional household characteristics associated with deep poverty are specified and ranked for each poor household, while ordinary feature ranking methods can only provide ranking results for poor households as a whole. Taking all poor households into consideration, we found that common important characteristics that can be used to identify deeply poor households include household income, disability, village attributes, lack of funds, labor force, disease, and number of household members, which are validated by mutual information analysis. In conclusion, our XAI-based model can be used to identify deep poverty and specify key household characteristics associated with deep poverty for individual households, facilitating the development of new targeted poverty reduction strategies.

GROUPING OF SEWING TOOL ASSISTANCE RECIPIENTS USING K-MEANS CLUSTERING ANALYSIS

Aid programs for underprivileged communities need continuous data collection in overcoming welfare problems, what has happened so far is by providing direct assistance to very poor families in every village in Indonesia. Related to this, the problem that has occurred so far is that the direct assistance program is not right on target, because many deserving families should not receive the assistance. The data obtained from the village government is not accurate, it is found that data is considered invalid. This study aimed to determine the distribution of sewing equipment recipients in the best cluster, for sewing equipment recipients in Greged Village, Cirebon Regency. As one way to improve data accuracy, a computational method or model is needed in the form of a data mining algorithm using the k-means clustering method to generate priority groups among hundreds of citizens or the poor. The stages start from data collection, training data, and testing data that consider several criteria from household information, economic conditions, housing conditions, and the number of household members in Greged Village, Cirebon Regency. The results of the tests carried out using 155 data with the best level of accuracy were in the K3 cluster with the Davies Bouldin Index’s value o: - 0.584. With the K-Means method, it is very appropriate to determine the recipient of the sewing equipment program in Greged Village.

이변량 프로빗 모형을 이용한 노인가구의 주거이동 의사 결정요인 분석

The purpose of the study is to analyze the determinants that affect elderly households’ intention to move and a potential destination (between regions or within a region), with the elderly who resides in Busan, Ulsan, and Gyengsangnam-do, by demographic characteristics, residential environmental characteristics, and economic characteristics, using Korea housing survey. The study uses a bivariate probit model for the analysis, considering the characteristics of dependent variables. The major findings are as followed. First, the variables influencing the elderly households’ intention to move and a potential destination are householder age, housing occupancy type, stress related to housing expense, and value of housing assets. Second, it is found that the variables only affecting the elderly households’ residential mobility plan are householder educational background, the number of household members, housing type, housing area, residential region, housing satisfaction, residential environment satisfaction, National Basic Living Security, and value of total assets. Third, the variable that has significant effects on the elderly households’ potential destination only is retirement status of householder. According to the findings, it is necessary to decide policy priority and prepare housing policy for the elderly households.