Very old people are known to participate less often in social surveys than younger age-groups. However, survey participation among very old people in institutional settings is understudied. Additionally, the focus of the literature is on response rates, which neglects the complexity of the process of survey participation. The present study uses standard definitions of the American Association for Public Opinion Research to give a detailed description of survey participation among very old people, including those in institutional settings. Data come from a German survey on quality of life and subjective well-being of persons aged 80-84, 85-89, and 90+ (N = 1800). The present study (a) estimates contact, cooperation, response, and refusal rates and (b) identifies associations of age, sex, and type of residence with each of these rates. Weighted outcome rates for the survey were: contact = 66.0%, cooperation = 39.6%, response = 26.1%, and refusal = 26.9%. Age, sex, and type of residence were not associated with the contact, cooperation, and response rate. Lower refusal rates were found for people aged 90+, men, and institutionalized people. Additional analyses showed higher rates of non-interviews due to health-related reasons for institutionalized people and those aged 90+. Overall, results indicate that institutionalized and non-institutionalized people showed similar levels of survey participation. Willingness to participate is a key factor for women and people in private households, while the ability to participate is more important for institutionalized people.

Depending on their place of residence, older persons have unequal access to long-term care (LTC) services. This article investigates how the county-level supply of inpatient and outpatient LTC services influences individual-level LTC choices of older persons. Administrative data on LTC service supply from the German Care Statistic are combined with representative survey data on the LTC choices of N = 1303 persons aged 80+ from the German Federal State North Rhine-Westphalia. Random utility models are applied to model the choice among three care arrangements: receiving inpatient care in an institutional setting (e.g., nursing home), receiving outpatient care in the community, and living in the community without receiving inpatient or outpatient care. The main findings are: Higher inpatient service supply increases the probability that older persons leave the community and enter institutional LTC. Higher outpatient service supply increases the probability that older persons choose to receive outpatient care in the community instead of entering institutional LTC. The results suggest that policy makers must consider the county-level LTC service supply when designing equitable LTC systems that meet the needs of older persons in a costeffective way.

The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.

Prediction algorithms are regularly used to support and automate high-stakes policy decisions about the allocation of scarce public resources. However, data-driven decision-making raises problems of algorithmic fairness and justice. So far, fairness and justice are frequently conflated, with the consequence that distributive justice concerns are not addressed explicitly. In this paper, we approach this issue by distinguishing (a) fairness as a property of the algorithm used for the prediction task from (b) justice as a property of the allocation principle used for the decision task in data-driven decision-making. The distinction highlights the different logic underlying concerns about fairness and justice and permits a more systematic investigation of the interrelations between the two concepts. We propose a new notion of algorithmic fairness called error fairness which requires prediction errors to not differ systematically across individuals. Drawing on sociological and philosophical discourse on local justice, we present a principled way to include distributive justice concerns into data-driven decision-making. We propose that allocation principles are just if they adhere to well-justified distributive justice principles. Moving beyond the one-sided focus on algorithmic fairness, we thereby make a first step toward the explicit implementation of distributive justice into data-driven decision-making.

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