Later-Life Migration in The Netherlands: Propensity to Move and Residential Mobility

Jong, Petra A. de

doi:10.1080/26892618.2020.1858384

Cited by 6 publications

(6 citation statements)

References 66 publications

(86 reference statements)

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“…Both cumulative distribution functions usually provide similar results [ 75 , 76 ], especially in the case of univariate binary response models [ 77 ], where differences between them are only appreciable in cases of very large samples with extreme patterns in the observed data [ 78 ]. The logit model was chosen for this study as it is suitable if a single dependent variable is dichotomous [ 28 ], and it is the most popular linking function for modeling binary data [ 79 ].…”

Section: Methodsmentioning

confidence: 99%

“…So, the most recent literature found is very limited: Some of them focus on the residents’ perspective about loyalty or corporate image [ 27 ], others are related to factors to influence housing decisions among older adults but not loyalty or corporate image [ 28 , 29 , 30 ] and others analyze the caregivers or older adults’ experience, strategies or timing of the decision- making process but not loyalty or corporate image [ 3 , 4 , 7 , 15 , 16 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Influence of the Corporate Image of Nursing Homes on the Loyalty of Residents’ Family Members

Rodríguez

Bermúdez-González

Soler

2022

IJERPH

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This study analyzes the influence of the corporate image of nursing homes on the decisions made by family members as to whether their elderly relatives will stay in the same nursing home. An empirical study was conducted considering 566 residents’ family members with the capacity to decide whether said residents will remain in the same nursing home, using a binary regression model with a logistic link function (i.e., logit). For the first time in the nursing home sector, these results show the specific variables of the corporate image that influence family members when deciding whether their elders will stay in the same nursing home. In order of importance, these variables are the level of trust conveyed by the nursing home, the investment made in the facilities, price-quality ratio, emotional connection to the nursing home, and the promotion of the nursing home’s services. The study also highlights the importance of other personal factors in family members’ decisions to keep their elders in the same nursing home, such as the family members’ employment situations (higher loyalty among those employed by third parties) and the determining factors involved in the relative’s choice of nursing home (higher loyalty among those whose choice was mainly based on humane and dignified treatment). This study offers a discussion of the theoretical contributions this research brings to academia as well as managerial implications for the industry. We believe that one future line of research should be continued after the COVID-19 pandemic comes to an end to compare the results and observe whether the most influential variables on family members’ loyalty remain the same as data for this study was collected from November 2019 to February 2020.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of the Corporate Image of Nursing Homes on the Loyalty of Residents’ Family Members

Rodríguez

Bermúdez-González

Soler

2022

IJERPH

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“…This latter form includes return to place of birth or area where childhood was spent. Apart from life events as triggers for move, De Jong (2020) argued that especially factors concerning the dwelling influence and predict the residential mobility of adults aged 55-years and older.…”

Section: Actual Mobility Of Mid-to-later-life Householdsmentioning

confidence: 99%

‘To buy or not to buy a home in a rural risk area’ by mid-to-later life home purchasers

Kloet

Bulder

Haartsen

2023

J Hous and the Built Environ

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Few studies on residential mobility of ageing adults to rural areas have investigated which of them buy a home in a rural risk area. This paper examines which socio-demographic characteristics, housing attributes and earthquake circumstances influence the actual choice of mid-to-later life adults to purchase a house in a rural risk area. The study focuses on housing transactions in the Dutch Groningen rural earthquake region, compared to transactions in the Groningen rural non-earthquake area from 2012 to 2019 (N = 6,082). Buying a house in the Groningen risk area might be challenging, as the earthquakes have had a significant impact on the regional housing market, the building structure of the houses and the wellbeing of the residents. Earlier research nonetheless suggests that, despite the risks, homebuyers still purchase houses in the Groningen earthquake area. The developed model predicts which mid-to-later life homebuyers of 50 years and older will purchase a dwelling in the Groningen earthquake region. Logistic regression analyses show that characteristics of the mid-to-later life homebuyer and earthquake circumstances are decisive aspects in respect of this choice. Being a single mid-to-later life homebuyer, with a previous residence in the Groningen earthquake region or being born in this region, enlarges the probability of purchasing a home in the earthquake region. Another significant predictor of a home purchase in the earthquake region appears to be a higher earthquake intensity of the homebuyer’s previous residence. These conclusions indicate the existence of a local housing market in rural risk areas.

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“…They found that job loss has a strong positive effect on the propensity to relocate. In [16], the authors examined the residential moving behavior of older adults in the Netherlands. [16] used a data collected from the Housing Research Netherlands (HRN) to provide insights into the housing situation of the Dutch population and their living needs.…”

Section: Propensity To Movementioning

confidence: 99%

“…In [16], the authors examined the residential moving behavior of older adults in the Netherlands. [16] used a data collected from the Housing Research Netherlands (HRN) to provide insights into the housing situation of the Dutch population and their living needs. A logistic regression model was used to assess the likelihood that respondents would report that they are willing to move in the upcoming two years.…”

Section: Propensity To Movementioning

confidence: 99%

When Machine Learning Models Leak: An Exploration of Synthetic Training Data

Slokom

Wolf

Larson

2022

Privacy in Statistical Databases

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We investigate an attack on a machine learning model that predicts whether a person or household will relocate in the next two years, i.e., a propensity-to-move classifier. The attack assumes that the attacker can query the model to obtain predictions and that the marginal distribution of the data on which the model was trained is publicly available. The attack also assumes that the attacker has obtained the values of non-sensitive attributes for a certain number of target individuals. The objective of the attack is to infer the values of sensitive attributes for these target individuals. We explore how replacing the original data with synthetic data when training the model impacts how successfully the attacker can infer sensitive attributes. 4

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Later-Life Migration in The Netherlands: Propensity to Move and Residential Mobility

Cited by 6 publications

References 66 publications

Influence of the Corporate Image of Nursing Homes on the Loyalty of Residents’ Family Members

Influence of the Corporate Image of Nursing Homes on the Loyalty of Residents’ Family Members

‘To buy or not to buy a home in a rural risk area’ by mid-to-later life home purchasers

When Machine Learning Models Leak: An Exploration of Synthetic Training Data

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