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# Home vs. nursing care: Unpacking the impact on health and well-being

Elena Bassoli, Mathieu Lefebvre, Jérôme Schoenmaeckers

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Elena Bassoli, Mathieu Lefebvre, Jérôme Schoenmaeckers. Home vs. nursing care: Unpacking the impact on health and well-being. Social Science & Medicine, 2025, 385, pp.118533. 10.1016/j.socscimed.2025.118533. hal-05460270

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# Home vs. nursing care: Unpacking the impact on health and well-being

Elena Bassoli<sup>a,∗</sup> ◦, Mathieu Lefebvre<sup>b</sup>, Jérôme Schoenmaeckers<sup>c,◦</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Handling editor: Richard Smith

JEL classification:

C210

I100

J140

Keywords:

Health outcomes

Nursing homes

Private vs public

Care

Propensity score matching

## ABSTRACT

In this paper, we present estimates of the effect of different care settings on health and well-being outcomes. We use data from the French CARE Survey, which interviews individuals aged 60 and above, to assess the differential effect of living at home or in a nursing home on mortality, morbidity and well-being indicators. In addition, we differentiate the effect between for-profit and non-profit nursing homes. To do so, we apply a propensity score matching approach that controls for selection on observables by matching people living at home with those living in nursing homes. Our results are threefold. First, we observe a positive effect of being in a nursing home on health outcomes but a negative effect on other well-being indicators such as happiness and nervousness. Second, the ownership status of the nursing home matters and the positive effect is stronger for non-profit and public nursing homes. Third, residents in for-profit nursing homes appear to be worse off than those in nonprofit institutions. These findings raise important questions for the future organization and the funding of long-term care.

## 1. Introduction

The challenges that come with population aging are at the core of Western society. From one side, individuals are living longer and will likely spend part of later life in situation of partial or full dependency, thus needing formal and informal care. One the other side, the provision of formal care is costly and weighs on the public spending. Therefore access to nursing home or long-term care facility is limited. However, understanding how formal care provision impacts individuals' health, and to a broader extent their well-being, is yet to be understood.

In this paper, we provide novel evidence on health and well-being outcomes for those living in a nursing home. Our aim is to compare similar individuals living at home and in a nursing home and to estimate the differences in terms of mortality, health conditions, as well as a series of indicators of happiness and well-being. In addition we look at the ownership status of the nursing homes as well as if they are for-profit or not in order to identify possible difference of outcomes.

In the US and in Europe, recent public policies tend to support home and community-based care for the elderly as an alternative to institutional care. For instance, the European Care Strategy, introduced in 2022, emphasizes the need for accessible, high-quality, and affordable long-term care (LTC) with a significant focus on home care services.

Similarly, in the U.S., there is a growing recognition of the need to expand home care to meet the needs of older adults (Che and Cheung, 2024). See the recent Home and Community-Based Services Final Regulation of Medicaid for example. Thus, recent policies increasingly favor home care for the elderly over institutional care. This shift is driven by a preference among older adults to remain in their homes, which is often more cost-effective and expected to be associated with improved quality of life (Wysocki et al., 2015). In France, the country on which this study is based, the ‘virage domiciliaire’ (‘homeshift’) has become an important public policy question with more and more elderly choosing to stay at home. This phenomenon has been amplified by the COVID 19 crisis, as well as scandals surrounding the mistreatment of residents of private nursing homes.

In terms of public policy, the choice of residence is of great importance since it questions what is the best way to provide long-term care to elderly people and also what is the cost of supporting the care provided in each residential choice. Especially since informal care is expected to decrease with changes in women labor force participation and family arrangements (i.e. women, who used to be the main carers as wifes or children, are working more, people are getting more divorced, there are fewer children and they are moving away, etc.).

In this paper, we use data from the French survey ‘Capacités, Aides et

<footnote><sup>a</sup> ETH Zurich, Switzerland</footnote>

<footnote><sup>b</sup> University of Strasbourg, BETA, CNRS, France</footnote>

<footnote><sup>c</sup> University of Liege, Belgium</footnote>

<footnote>* Corresponding author. Scheuchzerstrasse 7, 8092, Zürich, Switzerland.</footnote>

<footnote>E-mail addresses: ebassoli@ethz.ch (E. Bassoli), mathieu.lefebvre@unistra.fr (M. Lefebvre), jerome.schoenmaeckers@uliege.be (J. Schoenmaeckers).</footnote>
REsources des seniors' (CARE), which can be translated as "Ability, Care and REsources for the elderly", to look at whether there are differences between people living in nursing homes and those receiving home care for a series of health and well-being outcomes. We are also interested in comparing outcomes for those who are in for-profit and non-profit nursing homes. The survey took place in 2015/2016 before the COVID 19 pandemic which allows us to look at the effect of nursing home in normal times. Since comparing individuals with different care arrangements is not trivial, we use propensity score matching methods in order to construct a sample in which treated (being in a nursing home) and untreated individuals (living at home) have similar characteristics in terms of age, gender, degree of dependency, state of health, availability of informal help (partner, children and relatives) and income. Doing so, we assume that after controlling for the determinants of entry into a nursing home, the difference in terms of outcomes between those two samples is to be attributed to the way the nursing homes are designed and organized, or alternatively to the quality of aid and services one finds staying home. This allows us to determine whether there is a significant difference for being in a nursing home on our different measures of outcomes. In addition, we compare the type of nursing home (for-profit vs non-profit) and if they display different effects on our set of outcomes. Even though the analysis relies on propensity score matching to balance observed covariates between groups, it remains vulnerable to bias from unobserved confounding factors. This limitation arises because the method can only account for differences in variables that are measured and included in the matching process; any relevant factors that are unmeasured, mismeasured, or omitted may still influence both treatment assignment and outcomes, thereby distorting causal estimates. So in the rest of the paper, we talk more about comparison between groups than causal effect of being in a nursing home.

Although the question of the difference in outcomes between types of residence is important, little is known about the possible differential in terms of health and general well-being. While many studies have investigated the determinants of the choice of housing at old age, few studies have provided causal evidence on health, mortality or other individual outcomes (Flawinne et al., 2023). Moreover the own-ership status and the type of nursing home may also affect the quality of nursing home care. Several studies have focused on the distinction between non-profit and for-profit institutions [see i.e. Comondore et al., 2009a, Comondore et al., 2009b, Grabowski et al., 2013] or between private and public providers (Stolt et al., 2011) but the results are mixed as we exposed in the literature review below. Recently Laferrère and Schoenmaeckers (2025), have also shown that the life satisfaction of people in nursing homes tends to be different than at home. It is especially important to consider both the health and well-being of people living in nursing homes because these institutions can have contrasting effects on the two. Although nursing homes often provide essential medical care, ensuring residents' physical health and safety, they can sometimes negatively impact happiness and emotional well-being. Limited social interaction or loss of independence may contribute to feelings of loneliness or dissatisfaction in life.

The case of France is particularly interesting because the long-term care system includes a mix of services including home-based care and institutional care in nursing homes. Eligibility for public support is largely determined by the level of dependency but financing comes from a combination of public funds, individual co-payments, and private insurance (although less common). The setting in which care is received (particularly whether care is provided at home or in institutional environments) plays a crucial role in how needs are assessed, coded and then publicly funded (Roy, 2025). As we detail below, nursing homes in France operate under various ownership models. They can be publicly owned (by municipalities or hospitals), non-profit (often run by religious or charitable organizations), or for-profit. The for-profit sector includes both small independent operators ("mom-and-pop" facilities) and large corporate chains, some of which are major players in the European market (e.g. Orpea or Korian). This mix of ownership models

contributes to substantial variation in resources, staffing, and quality across facilities and thus offer a rare opportunity to assess the differences between types.

The paper contributes to a growing literature that tries to estimate the effect of the choice of residence at old age on the elderly situation. Using a rich dataset, we examine how living in a nursing home is associated with differences in mortality, health outcomes, and well-being. We consider outcomes such as survival over the next few months, hospitalizations in the past year, number of falls, self-assessed health, and emotional well-being indicators. To account for differences in individual characteristics and reduce potential selection biases, we apply propensity score matching and conduct additional sensitivity analyses. We also investigate how these associations vary by nursing home type (for-profit or nonprofit) and ownership status (private or public).

The paper is structured as follows. The next section presents a quick review of the existing literature on this topic. In Section 3, we present the propensity score matching method used to overcome selection bias along with sensitivity analysis performed to test the robustness of our results to the estimation assumptions. Section 4 presents the data and descriptive statistics and Section 5 presents the main results ad the sensitivity analysis. Section 6 is dedicated to a series of heterogeneity analysis. Finally, Section 7 concludes.

## 2. Related literature

Our paper is closely related to a literature that deals with long-term care issues and especially how this care is provided. When looking at cross-country comparison, one is surprised by how different is long-term provision between (and within) countries in terms of how it is organized, delivered, and financed. Informal care has been shown to be important (Klimaviciute et al., 2017) and the substitution between formal and informal care has also been studied (Van Houtven and Norton, 2004; Bolin et al., 2008; Bonsang, 2009). But the place of residence is also likely to have an impact on the type of care that is received by the elderly and the question we are interested in is to identify the factors of different morbidity and well-being, if any, within the nursing homes.

There is relatively limited research on the specific issue of the role of the institutionalization on potential excess mortality and morbidity. Only few recent studies have tried to assess mortality differential for people in nursing homes versus at home. Giudici et al. (2019) investigate the role of family contact on mortality by looking at French individuals aged 55 and above, living at home or in institution. Their results suggest a higher mortality for those institutionalized, of about 10 years difference, due to lack of active relationships with family members. However, they do not consider endogeneity issues due to different types of people going into nursing home versus staying at home. Using Italian data, Braggion et al. (2020) find that mortality peaks during the first months after admission into nursing home, in particular for patients with a recent hospitalization. The deterioration of pre-existing chronic conditions appears to be the most common cause of death. Flawinne et al. (2023) confirm these results using data for a series of European countries. On the contrary, relying on Dutch administrative data, Bakx et al. (2020) show that individuals in nursing home have no difference in mortality compared to those at home. Similarly, Werner et al. (2019) show for the US that admission to nursing home lower hospital readmission and medical expenditures and has no impact on mortality. However, Kim and Lim (2015) find evidence of an increase in medical expenditures for highly disabled individuals entering nursing homes.

One of the main issue with comparing individuals at home with those in institutions comes from self-selection. The individuals in nursing homes may often be in worse health condition than those at home which makes residential group comparison difficult. Some scholars have tried to overcome this issue by applying matching techniques on observable characteristics and estimate the impact of institutional care on healthcare use (Chappell et al., 2004; Kok et al., 2015; Blackburn et al., 2016;
Wysocki et al., 2014). Their results are mixed and depends on the sample and the outcomes they consider.

Another question is also to identify if nursing homes can be a source of worry for individual well-being. If individuals in those facilities suffer from lower well-being compared to their life satisfaction at home, this prevents older people from aging well and might lead them to depression. Studies have investigated this issue, exploiting both cross-sectional and longitudinal data, and the results are also mixed. Böckerman et al. (2012) look at Finnish data and found that institutionalized individuals have a higher level of well-being compared to those at home. Bom et al. (2022) find similar results using Dutch data and exploiting an event study method before and after nursing home admission. Similar evidence is also found by (Kok et al., 2015; Bakx et al., 2020; Rapp et al., 2018). Laferrère and Schoenmaeckers (2025) confirm these results by exploiting a panel of European countries of older people aged 65+. By using propensity score matching methods, they show that living in a nursing home is associated with lower well-being in Europe. However, when they make use of longitudinal data to further reduce the potential impact of non-observables, the conclusions are globally reversed: living in a nursing home is associated with higher well-being. This would be coherent with a model of optimal residential choices: living in a nursing home might not be desired, but proves to be the best choice for those who make it. However, Prieto-Flores et al. (2011) find a strong association between loneliness and institutionalization using Spanish data. Admission may also be perceived as a stressful event and a decrease in contact with friends and family members (Port et al., 2001). The importance of visits and family contacts is also highlighted by (Verbeek et al., 2020).

Finally (Comondore et al., 2009b), conducted a meta-analysis of studies that examined the quality of care in facilities and found that non-profit facilities provide the best care, in particular due to the quality of staff. The importance of staff was also found by (Antwi and Bowblis, 2018), who show how staff turnover can lower quality and increase mortality in the US setting; and by (Lin, 2014) who documented that increasing registered nurse staffing has a large significant impact on quality of care.

## 3. Empirical strategy

We are interested in the effects of residency, either at home or in a nursing home (private or public, for-profit or non-profit) on a series of outcomes. To evaluate the impact, one would ideally compare the outcome for someone who lives, for example, in a nursing home with the outcome we could observe if she had stayed at home. Unfortunately, we only observe individual in either one of the two states. Thus we face two selection problems. First, health status and housing may be determined simultaneously. This is why, as it becomes clear below, we only consider individuals with the same degree of limitations. The second problem arises because the characteristics of people in nursing homes can differ significantly from those still at home. The same is true when we compare people in different types of nursing homes. Therefore, to control for the selection bias due to observables, we use a propensity score matching method. It allows us to condition on sufficient observable information to obtain a counterfactual against which we can measure the effect of being in a nursing home (see Imbens, 2015) for a detailed presentation of the matching method).

### 3.1. Propensity score matching

Applying the model of Rubin (1974), on can write the outcome we observe $Y = T \cdot Y_1 + (1 - T) \cdot Y_0$, where $T$ indicates whether an individual is assigned to treatment (e.g. being in a nursing home) or control group (e.g. living at home). $Y$ is the outcome that is indexed by 1 for the potential outcome in a nursing home and 0 otherwise. We want to estimate $E(Y_1 - Y_0 | X, T = 1)$ where $X$ is a vector of observable characteristics.

By matching similar individuals from the two groups (treated or not),

the average treatment effect on the treated (ATT) can be identified if the conditional independence assumption holds and assignment to treatment is random conditional on controls $X$: $(Y_0, Y_1) \perp T | X$. That is the outcome of the individuals in the control group and in the treated group are independent of the residence status once we control for a set of observable characteristics. Given the high dimension of $X$, a more feasible option is to concentrate on a summary index, a balancing score (Rosenbaum and Rubin, 1983, 1985). The most prominent balancing score is the conditional probability of selection into treatment $P(X)$, i.e. the propensity score of being into a nursing home. The conditional independence assumption then implies $(Y_0, Y_1) \perp T | P(X)$.

To obtain propensity scores, Probit regressions are estimated to determine the probability of living in a nursing home. In our case, explanatory variables are gender, age, partnership situation, the number of informal caregivers (children and relatives), individualized household income. We also control for health and include the number of chronic diseases and the level of the GIR score. The GIR score (Groupe Iso-Ressources) is a widely used classification tool in France to assess the level of dependency of elderly individuals, especially when determining their eligibility for long-term care services or financial aid. It is based on the AGGIR grid (Autonomie Gérontologique Groupes Iso-Ressources), which evaluates a person's physical and mental autonomy across several categories like mobility, personal hygiene, meal preparation, and cognitive functions. The GIR score goes from 1 to 6 with 1 being the worse and 6 means a low level of dependency. A GIR index lower than 5 gives eligibility to long-term care benefits. Finally we include nursing homes characteristics such as the number of years spent in the nursing home, the cost, the size, the number of floors but also if the individual has previously worked for additional comparative analyses by type of nursing home. These variables are strong predictors of both entering a nursing home (Laferrère et al., 2013) and health or well-being status. That is they influence simultaneously the fact of being in a nursing home. Depending on the analysis, the estimations of propensity scores will be done for the total sample or for specific subsamples. All Propensity score estimation results are presented in Table A.1 in the Appendix.

In our main analysis, we use Kernel matching method with replacement to estimate the ATT. As usual with matching analysis, there is a clear trade-off between bias and efficiency when it comes to choosing a matching algorithm. This estimator has the advantage of reducing the variance that is achieved since more information is used compared to other matching methods. However, it possibly uses observations that are not very good matches, so in addition, as robustness checks, we also present in the appendix results when we use other matching algorithms to compare our results to different matching methods, such as nearest-neighbour and radius ones.

### 3.2. Sensitivity analysis

Our analysis is based on the conditional independence assumption, which assumes that there is no unobservable characteristic that explains both the decision of residence and the outcomes of interest. In order to assess whether our average treatment effects are robust to possible deviations from this assumption, we implement a simulated sensitivity analysis as proposed by Ichino et al. (2008). The advantage of this approach compared to other model-free method such as Rosenbaum bound or M-H bound is that it allows to test many kinds of hypothetical confounders that could cause bias in the estimation. The idea is to assume that the conditional independence assumption is not satisfied given the considered observables but would be if one could observe an additional binary variable. This potential confounder can be simulated in the data and used as an additional covariate in combination with the matching estimator. By comparing the results obtained with and without matching on the simulated confounder, we can show to what extent our results are robust to specific sources of failure of the conditional independence assumption.
<table><caption>**Table 1**
Summary statistics.</caption><thead><tr><th></th><th>At home</th><th>Nursing Home</th><th>Both</th><th>(Observations)</th></tr></thead><tbody><tr><td>Age</td><td>75.6</td><td>86.4</td><td>78.7</td><td>(7729; 3135; 10864)</td></tr><tr><td>GIR score</td><td>5.1</td><td>2.8</td><td>4.4</td><td>(7729; 3105; 10834)</td></tr><tr><td>% of female</td><td>61.5</td><td>74.7</td><td>65.3</td><td>(7729; 3135; 10864)</td></tr><tr><td>Disposable Income (€)</td><td>27179.5</td><td>18917.9</td><td>24793.7</td><td>(7721; 3135; 10856)</td></tr><tr><td>% in couple</td><td>48.4</td><td>12.5</td><td>38.1</td><td>(7729; 3129; 10858)</td></tr><tr><td># of helpers</td><td>0.672</td><td>0.940</td><td>0.749</td><td>(7729; 3135; 10864)</td></tr><tr><td># of chronic diseases</td><td>0.773</td><td>0.695</td><td>0.750</td><td>(7729; 3099; 10828)</td></tr><tr><td><em>Outcomes</em></td><td></td><td></td><td></td><td></td></tr><tr><td># months not in life</td><td>9.091</td><td>20.443</td><td>12.217</td><td>(7729; 2938; 10667)</td></tr><tr><td>% hospital nights</td><td>26.9</td><td>30.2</td><td>27.8</td><td>(7722; 3088; 10810)</td></tr><tr><td>% fell</td><td>32.4</td><td>43.6</td><td>35.6</td><td>(7719; 3095; 10814)</td></tr><tr><td>% in poor health</td><td>30.8</td><td>36.5</td><td>32.4</td><td>(7729; 3135; 10864)</td></tr><tr><td>% unhappy</td><td>52.0</td><td>62.1</td><td>55.1</td><td>(7102; 3135; 10237)</td></tr><tr><td>% sad</td><td>40.5</td><td>54.3</td><td>43.3</td><td>(7209; 1818; 9027)</td></tr><tr><td>% not relaxed</td><td>47.6</td><td>71.6</td><td>54.9</td><td>(7191; 3135; 10326)</td></tr><tr><td>% nervous</td><td>17.6</td><td>53.0</td><td>28.3</td><td>(7218; 3135; 10353)</td></tr></tbody></table>

Notes: This table reports the summary statistics for the entire sample. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

The assumption of the analysis is that the conditional independence assumption no longer holds given the set of covariates *X* but it holds given *X* and an unobserved binary variable *U*. The method works by simulating the impact of this hypothetical unobserved confounder *U* on treatment effects. Thus we create an artificial variable that represents the unobserved factor and specify how strongly it would influence both the likelihood of receiving the treatment and the outcome of interest. We can then artificially add this variable into the matching process and re-estimate the treatment effect.

By comparing the new estimates to the original results, we can assess how sensitive the conclusions are to possible hidden bias. If the treatment effect changes notably, it means that the findings may be vulnerable to unobserved confounding. On the contrary if the effect remains stable, the results are considered more robust. See Section Appendix A.2 in the Appendix for technical details of the method. In our sensitivity analysis we follow Ichino et al. (2008) and assume that the distribution of the unobserved variable *U* is similar to the empirical distribution of important binary covariates.

### 3.3. Falsification exercise

In addition to our sensitivity analysis, we perform a falsification population test. We take the sample of people living at home and we randomly split the sample in one control and one fakely treated, respecting the proportions of treated and controls in the main analysis. We repeat it 100 times, and we run our estimation model. Then we can see the size of the effect and in how many cases we find statistically significant differences. We should not find any differences in the outcomes of the two samples in this falsification population exercise if the only reason for the differences is the residential status.

## 4. Data and descriptive statistics

We use the French CARE Survey (Capacités, Aides et ReSSources des Seniors), which is a general population survey of French individuals aged 60 and above. The survey targeted living conditions of individuals living at home and in communities (residential facilities) and was conducted in 2015–2016. The survey aimed at understanding individuals’ relationship with their relatives, limitations of daily activities and any type of support received. It is composed on two parts: one is devoted to individuals living at home (CARE-Menages) and the other to individuals in institutions (CARE-Institutions). The survey is representative of the older population in France aged 60 or more and about 10,628 individuals living at home and 3262 nursing home residents have been interviewed. It provides exhaustive information on socioeconomic characteristics,

health status, limitations, and assistance received. It also asks questions about the general well-being of the respondent.

Our study population covers every individual interviewed in one of the two parts of the survey for whom we have all information we need to match pairs of elderly. Indeed, to obtain propensity scores, we use Probit regressions where the dependent variable is being in a nursing home and explaining variables are those presented in the previous section. Table 1 summarizes the information on the demographic and household variables according to the type of residence. On average, people in nursing homes are older, have more limitations, are more female and single and have lower income than those living at home. They have also less chronic diseases and more helpers. These descriptive statistics show important differences on average between the two groups which motivates the use of propensity score matching method to control for the differences between the two groups elderly and determine a potential impact of nursing homes on the outcomes.

We consider eight outcomes related to health status and individual well-being. First, we consider mortality. The CARE survey of 2015–2016 have been matched with an additional information from the census of 2021. This allows us to identify for each respondent if she was still alive or not and in case of death, the date of passing. On this basis, we construct a variable giving the number of months not in life between the initial date of the survey and the census which is an indicator of mortality. We also consider two other objective health outcomes which are the fact of having spent at least one night in hospital and if the person has fallen during the last 12 months. In the survey, respondents are asked to rate their health by answering to the question “How is your general state of health?”.

The possible answers are 1 = Very good, 2 = Good, 3 = Quite good, 4 = Poor and 5 = Very poor”. We create a dummy variable for being in poor health when the answer to this question is poor or very poor. In Table 1, we see the summary statistics for these four health outcomes. Nursing homes residents have higher mortality and worse health status which is expected given their higher age. Table 2 also presents detailed statistics for our outcomes of interest.

In addition to these health outcomes, we consider four well-being indicators. Respondents have been asked if during the four weeks before the survey they have felt 1) happy, 2) sad and down, 3) calm and relaxed or 4) very nervous. For each of these items they have to answer according to the following scale 1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, and 5 = All the time. We transform the information into dummy variables which indicates if the individual feels Unhappy, Sad, Not relaxed and Nervous. This gives us four well-being indicators that are also displayed in Tables 1 and 2. We justify using binary transformations for well-being and health outcomes for three reasons:
<table><caption>**Table 2**
Summary statistics of the outcomes of interest.</caption><thead><tr><th rowspan="2">Main covariates</th><th rowspan="2"></th><th>Months not in life (#)</th><th>Hospital nights (%)</th><th>Fell (%)</th><th>Poor Health (%)</th><th>Unhappy (%)</th><th>Sad (%)</th><th>Not relaxed (%)</th><th>Nervous (%)</th></tr><tr><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th><th>Mean (s.d.)</th></tr></thead><tbody><tr><td rowspan="3">All</td><td>AH</td><td>9.1 (17.7)</td><td>26.9 (44.3)</td><td>32.4 (46.8)</td><td>30.8 (46.2)</td><td>52.0 (50.0)</td><td>40.5 (49.1)</td><td>47.6 (49.9)</td><td>17.6 (38.0)</td></tr><tr><td>NH</td><td>20.4 (18.3)</td><td>30.2 (45.9)</td><td>43.6 (49/6)</td><td>36.5 (48.2)</td><td>62.1 (48.5)</td><td>54.3 (49.8)</td><td>71.6 (45.0)</td><td>53.0 (50.0)</td></tr><tr><td>Both</td><td>12.2 (18.6)</td><td>27.8 (44.8)</td><td>35.6 (47.9)</td><td>32.4 (46.8)</td><td>55.1 (49.7)</td><td>43.3 (49.5)</td><td>54.9 (49.8)</td><td>28.3 (45.0)</td></tr><tr><td rowspan="3">Women</td><td>AH</td><td>8.3 (16.9)</td><td>26.5 (44.1)</td><td>35.9 (48.0)</td><td>31.4 (46.4)</td><td>48.2 (50.0)</td><td>46.7 (49.9)</td><td>42.9 (49.5)</td><td>20.0 (40.0)</td></tr><tr><td>NH</td><td>19.9 (18.3)</td><td>28.8 (45.2)</td><td>44.2 (49.7)</td><td>37.1 (48.3)</td><td>62.4 (48.4)</td><td>55.0 (49.8)</td><td>70.8 (45.5)</td><td>54.2 (49.8)</td></tr><tr><td>Both</td><td>12.0 (18.2)</td><td>27.2 (44.5)</td><td>38.6 (48.7)</td><td>33.3 (47.1)</td><td>53.2 (49.9)</td><td>48.6 (50.0)</td><td>52.6 (49.9)</td><td>31.9 (46.6)</td></tr><tr><td rowspan="3">Men</td><td>AH</td><td>10.4 (18.9)</td><td>27.5 (44.7)</td><td>26.8 (44.3)</td><td>29.8 (45.7)</td><td>57.9 (49.4)</td><td>30.6 (46.1)</td><td>55.0 (49.8)</td><td>13.6 (34.2)</td></tr><tr><td>NH</td><td>21.9 (18.3)</td><td>34.7 (47.6)</td><td>41.8 (49.4)</td><td>34.7 (47.6)</td><td>61.0 (48.8)</td><td>52.6 (50.0)</td><td>74.2 (43.8)</td><td>49.1 (50.0)</td></tr><tr><td>Both</td><td>12.7 (19.3)</td><td>29.0 (45.4)</td><td>29.9 (45.8)</td><td>30.8 (46.2)</td><td>58.6 (49.3)</td><td>33.8 (47.3)</td><td>59.2 (49.1)</td><td>21.4 (41.1)</td></tr><tr><td rowspan="3">Single</td><td>AH</td><td>11.0 (18.8)</td><td>30.1 (45.9)</td><td>38.0 (48.5)</td><td>33.3 (47.1)</td><td>42.6 (49.5)</td><td>47.4 (49.9)</td><td>46.4 (49.9)</td><td>17.6 (38.1)</td></tr><tr><td>NH</td><td>20.0 (18.3)</td><td>29.5 (45.6)</td><td>43.1 (49.5)</td><td>35.3 (47.8)</td><td>60.9 (48.8)</td><td>53.5 (49.9)</td><td>70.5 (45.6)</td><td>51.9 (50.0)</td></tr><tr><td>Both</td><td>14.5 (19.1)</td><td>29.9 (45.8)</td><td>40.1 (49.0)</td><td>34.1 (47.4)</td><td>50.4 (50.0)</td><td>49.2 (50.0)</td><td>56.6 (49.6)</td><td>32.1 (46.7)</td></tr><tr><td rowspan="3">In couple</td><td>AH</td><td>7.0 (16.2)</td><td>23.4 (42.3)</td><td>26.4 (44.1)</td><td>28.1 (45.0)</td><td>61.9 (48.6)</td><td>33.2 (47.0)</td><td>48.8 (50.0)</td><td>17.5 (38.0)</td></tr><tr><td>NH</td><td>22.9 (18.2)</td><td>35.4 (47.9)</td><td>46.9 (50.0)</td><td>44.6 (49.8)</td><td>70.4 (45.7)</td><td>60.8 (48.9)</td><td>79.8 (40.1)</td><td>59.9 (49.0)</td></tr><tr><td>Both</td><td>8.5 (17.0)</td><td>24.5 (43.0)</td><td>28.3 (45.1)</td><td>29.7 (45.7)</td><td>62.8 (48.3)</td><td>34.6 (47.6)</td><td>52.0 (50.0)</td><td>21.8 (41.3)</td></tr><tr><td rowspan="3">Less than 80</td><td>AH</td><td>4.9 (13.7)</td><td>24.7 (43.1)</td><td>26.4 (44.1)</td><td>27.9 (44.9)</td><td>55.9 (49.7)</td><td>37.2 (48.3)</td><td>48.3 (50.0)</td><td>17.9 (38.3)</td></tr><tr><td>NH</td><td>14.2 (17.6)</td><td>30.4 (46.0)</td><td>38.7 (48.7)</td><td>37.3 (48.4)</td><td>62.2 (48.5)</td><td>57.7 (49.5)</td><td>71.0 (45.4)</td><td>53.7 (49.9)</td></tr><tr><td>Both</td><td>5.9 (14.4)</td><td>25.3 (43.4)</td><td>27.7 (44.8)</td><td>28.9 (45.3)</td><td>56.6 (49.6)</td><td>38.6 (48.7)</td><td>50.8 (50.0)</td><td>21.9 (41.3)</td></tr><tr><td rowspan="3">More than 80</td><td>AH</td><td>17.1 (21.5)</td><td>31.1 (46.3)</td><td>43.8 (49.6)</td><td>36.4 (48.1)</td><td>43.6 (49.6)</td><td>47.5 (50.0)</td><td>46.0 (49.9)</td><td>16.8 (37.4)</td></tr><tr><td>NH</td><td>21.9 (18.1)</td><td>30.2 (45.9)</td><td>44.8 (49.7)</td><td>36.3 (48.1)</td><td>62.0 (48.5)</td><td>53.5 (49.9)</td><td>71.8 (45.0)</td><td>52.8 (49.9)</td></tr><tr><td>Both</td><td>19.4 (20.1)</td><td>30.7 (46.1)</td><td>44.3 (49.7)</td><td>36.3 (48.1)</td><td>53.4 (49.9)</td><td>49.9 (50.0)</td><td>59.5 (49.1)</td><td>35.6 (47.9)</td></tr></tbody></table>

Notes: This table reports the summary statistics for the outcomes of interest by individuals' characteristics and place of residence (at home (AH) or in nursing home (NH), or both). Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

(1) binary results are easier to interpret and more relevant for policy decisions; (2) respondents to the CARE survey may interpret scale ratings inconsistently, reducing comparability; and (3) treating ordinal data as continuous assumes equal spacing between scale points, which is often inaccurate. Binary variables avoid this issue by focusing on meaningful thresholds.

## 5. Main results

### 5.1. *Being in a nursing home*

We begin with our main result on the impact of being in a nursing home compared to living at home. We consider the eight outcomes presented above and Fig. 1 displays the estimated average treatment effects as well as the confidence interval of these estimates.

The number of observations used for the treated and the control

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Main results: Average Treatment of the Treated (ATT)</figcaption></figure>

Notes: These figure reports the average treatment of the treated (ATT) from the propensity score matching estimates for each outcome. We report results based on no exact matching, exact match on sex; sex and GIR; and sex, GIR and Age. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.
groups are presented in Table A.2. We estimate four different matching models for each outcome by successively introducing exact matching on sex, level of dependency measured by the GIR score, and age. This allows us to see if matching exactly on some important variables affects our results. We present the results according to the Kernell matching method but the results obtained with other matching methods are presented in Table A.3 in the Appendix. They are qualitatively similar. In the Appendix, Table A.4 shows additional results from an OLS regression instead of a propensity score matching estimation.

The ATTs are similar whatever the model which tends to show that exact matching does not provide much more precision to our estimations. Therefore, in the following analysis, we will present estimations without exact matches for the sake of calculation. The number of individuals used to match varies for each estimation. It depends on both the outcome considered and the matching procedure.

The difference between being in a nursing home or staying at home is significant at 1 % level for every outcomes except for being *Sad*. This absence of significant effect could be due to the particularly high number of missing information for this question.

Being in a nursing home has a negative and significant effect on mortality. According to our results, elderly in a nursing home are observed to live almost 5 to 6 more months than those staying at home. They are also about 7–9 percentage points less likely to have hospital stay, 11 percentage points less to fall. Looking at subjective health measure, people in nursing home have a lower probability of being in poor health by almost 16 percentage points. These results depend on the absence of any confounding factors but they show significant differences that are cause for concern. Our sensitivity analysis as well as the falsification exercise below will support these findings.

However these results in favor of nursing homes are tempered by the differences we observe for outcomes related to life satisfaction, stress and nervousness. Indeed, being in a nursing home means that the residents experience much often lack of happiness feeling. They are also more likely to experience a lack of calm and nervousness. These results are very interesting because they show that while living in a nursing home is beneficial for health, the same cannot be said for general well-being and, in particular, feelings of happiness.

We also look at the effect of our various covariates on the ATTs. Table A.5 in the Appendix shows how much each of the existing observables impacts the treatment effect. Although the ATT is rather stable for some outcomes, we see that it is important to control for the level of dependency for others. In particular, the estimated treatment effects for health-related outcomes change signs once we control for the GIR score and remain stable afterward. This comforts us in the necessity to take into account several important explicative variables.

These first results appeal some comments based on additional analyses (not reported here). First, we have decided to use the GIR score as an indicator of the need for long-term care because it is the one that is mainly used in France. However, the survey includes also a very detailed record of daily limitations (up to 23). In our propensity score matching estimations, if we replace the GIR score by the sum of the daily limitations, the estimated coefficients and their significance are almost identical. GIR scores and limitations are indeed highly correlated. Second, we consider mortality by using the census of 2021 which could have been affected by the COVID-19 crisis. If instead, we stop counting the number of months alive at a date just before the start of the pandemic, i. e. we look at whether people have died by February 28, 2020, the estimated ATTs are a little bigger (−6.433 instead of −5.994 without exact matching or −5.602 instead of −4.887 with exact matching), as the contagion and its effects have been significant in nursing homes in terms of deaths, illustrating the consistency of the results.

Finally and to support the quality of the PSM approach, we show three post-matching balance diagnostics: the distribution of propensity scores (Figure A.1), standardized mean differences (Table A.6), and t-tests (Table A.7), all of which indicate substantial improvement in co-variate balance after matching. However, two variables (number of

帮 hands and chronic conditions) show residual imbalance, motivating the use of two additional analyzes as presented in Section 3. First we conduct the sensitivity analysis suggested by Ichino et al., 2008 to test whether our results are robust to the violation of the conditional independence assumption. Second, we perform a falsification population exercise.

## 5.2. Sensitivity analysis

In order to further test whether the results obtained with the propensity score matching are robust to the violation of the conditional independence assumption, we conduct the sensitivity analysis suggested by Ichino et al., 2008. As presented in Section 3.2, we simulate an unobserved variable which would have a distribution similar to the empirical distribution of important binary covariates. Covariates with the greatest selection and outcome effects are reported in Table 3 for three different dependent variables: Being a female, being in couple and be aged 80 or more (Results for all outcomes are available upon request).

The selection of these covariates to simulate unobserved confounders is grounded in both theoretical relevance and empirical evidence. These variables are known to be strongly associated with both the likelihood of being in a NH/receiving long-term care and with key health and well-being outcomes (see literature review). For instance, older individuals and those without a partner are more likely to rely on formal care, while gender differences often influence care expectations and access.

By choosing covariates that plausibly capture latent vulnerabilities or social dynamics that are difficult to observe directly, the simulation aims to mimic the types of omitted variable bias that could threaten causal inference. We see that any unobserved variable with similar treatment and selection effects as the covariates already introduced in the propensity score matching will not confound our results. The estimate for the ATTs are very close to the ones presented in Fig. 1.

## 5.3. Falsification exercise

Fig. 2 displays the results of a falsification population exercise based on 100 simulations of random assignment of fake residency in a nursing home to control groups as presented in Section 3.3. For each of the eight outcomes we consider here, we see that our main estimations presented in Fig. 1 differ significantly from the simulated results, except for the variable *Sad*. These findings support also that there is no confounding factors that affect our estimates.

# 6. Heterogeneity analyses

We perform a series of additional estimations to explore the heterogeneity of effects. In the following all results are obtained with the Kernell method without exact matching for the sake of calculation. We look at the effects for some subsamples obtained according to age, sex, level of need for long-term care and type and ownership status of nursing homes.

## 6.1. By sex, age and level of dependency

We first look at variations according to age by estimating the ATT for the subgroups of people aged less and more than 80. Remember that our sample is made of people aged 60 or more. The first two columns of results of Table 4 show the two estimations. For each estimation, we present the ATT and the standard errors as well as the 95 % confidence intervals that allows us to compare group-specific estimations. For all outcomes except mortality and sadness, the ATTs are significantly different than zero and of the same signs at those presented in Fig. 1. This means that being in nursing homes affects both age groups in the same way. However, the size of the effect may be different for some groups. The negative effect of being in a nursing home appears to be stronger for the young group (less than 80) for what concerns the
<table><caption>**Table 3**
Sensitivity analysis with confounder-like.</caption><thead><tr><th>Dep. variable</th><th></th><th>Outcome Effect r</th><th>Selection Effect A</th><th>ATT</th></tr></thead><tbody><tr><td rowspan="4">Months not in life</td><td>PSM (Kernell)</td><td>1</td><td>1</td><td>-5.994***</td></tr><tr><td>Confounder-like</td><td></td><td></td><td></td></tr><tr><td>Being a female</td><td>0.790</td><td>1.972</td><td>-5.055***</td></tr><tr><td>Being in couple</td><td>0.526</td><td>0.178</td><td>-6.789***</td></tr><tr><td>Be aged 80 and over</td><td>5.069</td><td>5.187</td><td>-9.192***</td></tr><tr><td rowspan="4">Poor health</td><td>PSM (Kernell)</td><td>1</td><td>1</td><td>-0.152***</td></tr><tr><td>Confounder-like</td><td></td><td></td><td></td></tr><tr><td>Being a female</td><td>1.094</td><td>1.861</td><td>-0.144***</td></tr><tr><td>Being in couple</td><td>0.784</td><td>0.156</td><td>-0.158***</td></tr><tr><td>Be aged 80 and over</td><td>1.491</td><td>7.660</td><td>-0.172***</td></tr><tr><td rowspan="4">Unhappy</td><td>PSM (Kernell)</td><td>1</td><td>1</td><td>0.309***</td></tr><tr><td>Confounder-like</td><td></td><td></td><td></td></tr><tr><td>Being a female</td><td>0.670</td><td>1.792</td><td>0.311***</td></tr><tr><td>Being in couple</td><td>2.188</td><td>0.168</td><td>0.350***</td></tr><tr><td>Be aged 80 and over</td><td>0.603</td><td>8.103</td><td>0.347***</td></tr></tbody></table>

Notes: This table reports results for months not in life, poor health and being unhappy, using the Propensity-Score Kernel Matching method. P-values \*p < 0.1, \*\*p < 0.05, \*\*\*p < 0.01. Source: Authors' calculation based on CARE Data 2015/2016, mortality from Census data up to 2021.

number of hospital nights and the probability of being in poor health. This is probably an age effect since older people are more likely to experience bad health issues. Interestingly we observe no effect on mortality for those aged less than 80 years old. Which is expected since it is below the average life expectancy that was in 2018 in France 85.3 for women and 79.4 years for men. Looking at the well-being measure, we do not observe big differences in the probability of Unhappy and Nervous but younger individuals appear to be much more impacted in terms of absence of calm. In Table 4, the confidence intervals allows the comparison of effect sizes between groups. This is interesting because when the size of the effect appears to be different it is not always significant. For example, the difference of mortality between age groups is significant but it is not for being unhappy.

We also look at gender difference in Table 4. For all outcomes, except Sad, and for each sex, the ATTs are significantly different than zero and of the same signs at those presented in Fig. 1. This means that being in nursing homes affects both sex in the same way although the size of the effect is different for some outcomes. In particular, male health outcomes, such as mortality, probability of hospital nights or poor health, are much more affected than female ones. However, there is no much difference of effect for the other outcomes and we do not observe significant difference between the two groups.

Finally we estimate the ATT of being in a nursing home for each of the outcome for those who have a GIR score lower than 5 or greater or equal to 5. That is we make the difference between those who are according to French legislation eligible for long-term care benefits and the others. This is to differentiate the estimation according the level of dependency. In Table 4, we see that the overall difference in terms of health conditions between being in a nursing home and living at home is actually driven by those with a GIR score less than 5. This means that those with important needs for long-term care could benefit from being in a nursing home. However, we also see that the effect on well-being is significant for these individuals and positively affect the probability of being unhappy or nervous.

## 6.2. By type of nursing home

In France, nursing homes can be categorized into different types based on their funding and ownership structures. Public nursing homes are typically managed by local authorities or public health institutions. Among these, some are affiliated with hospitals and are integrated into the healthcare system, providing a higher level of medical supervision and care. Other public nursing homes operate independently, often run by municipalities or regional authorities. Private nursing homes, on the

other hand, are divided into two subcategories: non-profit and for-profit. Non-profit nursing homes are often run by charitable organizations and prioritize affordability, while for-profit facilities are managed by private companies.

Table 5 displays descriptive statistics for each type of nursing homes. In our data, we can identify privately owned nursing homes, either for-profit or non-profit as well as publicly owned affiliated with hospital or not. There is not much difference in the characteristics of the residents of these nursing homes, apart from income. Residents of private nursing homes are richer than others. They also stay for shorter periods on average. For the rest, apart from the fees, there are no notable differences between the institutions either.

In Table 6, we present the effect of being in a nursing home when we differentiate by types. For each outcome, we estimate the ATT of being in a certain type of nursing home vs living at home. The first columns of results show the effect for private for-profit and non-profit institutions as well as public hospital related or not. The last two columns present estimations once we gather all private nursing homes together (for-profit and non-profit) and when we gather all non-profit institutions (private non-profit and public).

At first sight, we do not observe much difference between the types of nursing home. The ATTs are all significant (except for Sad) and of the same sign as in our main estimates. Whatever type of nursing home, it reduces mortality, the probability of hospital nights and of falling, and the likelihood of poor health, this compared to living at home. However, the size of the difference with living at home varies. In particular, it seems that non-profit institutions (private or public) shows a bigger difference than for-profit ones and the two groups appear to be often significantly different at 5%. This is shown in the last column, which displays bigger effects for non-profit nursing homes (except

for mortality where the coefficients are similar). The reduction in health outcomes is greater when in a non-profit nursing home.

## 6.3. For-profit versus non-profit nursing homes

Observing that non-profit nursing homes are different than for-profit ones is important because it highlights the prioritization of resident well-being over financial gain. Previous studies have shown that nursing homes owned by for-profit companies tend to have a lower quality than homes with public and non-profit owners (Harrington et al., 2002; Comondore et al., 2009b; Winblad et al., 2017; Hjelmar et al., 2018). Non-profit nursing homes often reinvest resources into improving care quality, staffing, and facilities, which directly benefits residents. They tend to focus on accessibility and equitable service, ensuring that even
Months not in life

<figure><img src="image_5.png" /></figure>

Fell

<figure><img src="image_6.png" /></figure>

Unhappy

<figure><img src="image_7.png" /></figure>

Not relaxed

<figure><img src="image_8.png" /><figcaption>**Fig. 2.** Falsification population exercise</figcaption></figure>

NOTE: The figures display ATT estimates and their 95 % confidence intervals from 100 simulations of random assignment of fake residency in a nursing home to the control groups. Source: Authors’ calculation based on CARE Data 2015/2016, mortality from Census up to 2021.

those with limited financial means receive adequate care. This contrasts
with for-profit nursing homes, where profit motives can sometimes
result in cost-cutting measures that may compromise the quality of care.

We further investigate this difference by directly comparing the

outcomes of for-profit and non-profit nursing homes. To do so, we apply
the same propensity score matching method, but we match residents
from a given type of nursing home to other types to see if we observe a
significant difference. Table 7 presents several estimations. In the first

Hospital nights

<figure><img src="image_9.png" /></figure>

Poor health

<figure><img src="image_10.png" /></figure>

Sad

<figure><img src="image_11.png" /></figure>

Nervous

<figure><img src="image_12.png" /></figure>
<table><caption>**Table 4**
Analysis by age, sex and GIR.</caption><thead><tr><th rowspan="2">Dep. Variable</th><th colspan="2">Age</th><th colspan="2">Sex</th><th colspan="2">GIR</th></tr><tr><th>≤80</th><th>>80</th><th>Male</th><th>Female</th><th>&lt;5</th><th>≥5</th></tr></thead><tbody><tr><td><strong>Months not in life</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.151<br>(2.116)<br>[-4.299, 3.997]</td><td>-7.352***<br>(1.155)<br>[-9.616, -5.088]</td><td>-7.235***<br>(1.648)<br>[-10.465, -4.005]</td><td>-5.395***<br>(1.240)<br>[-7.825, -2.965]</td><td>-6.274***<br>(1.054)<br>[-8.339, -4.209]</td><td>-2.401*<br>(1.236)<br>[-4.823, 0.021]</td></tr><tr><td><strong>Hospital nights</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.144***<br>(0.044)<br>[-0.230, -0.058]</td><td>-0.0684***<br>(0.024)<br>[-0.116, -0.021]</td><td>-0.136***<br>(0.037)<br>[-0.208, -0.064]</td><td>-0.0823***<br>(0.026)<br>[-0.134, -0.031]</td><td>-0.108***<br>(0.023)<br>[-0.153, -0.063]</td><td>0.0246<br>(0.030)<br>[-0.034, 0.083]</td></tr><tr><td><strong>Fell</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.095**<br>(0.045)<br>[-0.183, -0.007]</td><td>-0.112***<br>(0.025)<br>[-0.161, -0.063]</td><td>-0.123***<br>(0.036)<br>[-0.193, -0.053]</td><td>-0.114***<br>(0.027)<br>[-0.167, -0.061]</td><td>-0.120***<br>(0.023)<br>[-0.165, -0.075]</td><td>0.005<br>(0.034)<br>[-0.061, 0.071]</td></tr><tr><td><strong>Poor health</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.288***<br>(0.042)<br>[-0.370, -0.206]</td><td>-0.115***<br>(0.025)<br>[-0.164, -0.066]</td><td>-0.203***<br>(0.037)<br>[-0.275, -0.131]</td><td>-0.121***<br>(0.027)<br>[-0.174, -0.068]</td><td>-0.172***<br>(0.023)<br>[-0.217, -0.127]</td><td>0.0123<br>(0.026)<br>[-0.039, 0.064]</td></tr><tr><td><strong>Unhappy</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.268***<br>(0.063)<br>[0.144, 0.392]</td><td>0.299***<br>(0.032)<br>[0.236, 0.362]</td><td>0.245***<br>(0.047)<br>[0.153, 0.337]</td><td>0.321***<br>(0.030)<br>[0.262, 0.380]</td><td>0.343***<br>(0.032)<br>[0.280, 0.406]</td><td>-0.050<br>(0.036)<br>[-0.120, 0.020]</td></tr><tr><td><strong>Sad</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.001<br>(0.052)<br>[-0.103, 0.101]</td><td>0.008<br>(0.028)<br>[-0.046, 0.062]</td><td>0.044<br>(0.041)<br>[-0.037, 0.125]</td><td>-0.015<br>(0.031)<br>[-0.076, 0.046]</td><td>-0.008<br>(0.030)<br>[-0.066, 0.050]</td><td>0.061<br>(0.038)<br>[-0.014, 0.136]</td></tr><tr><td><strong>Not relaxed</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.439***<br>(0.042)<br>[0.356, 0.522]</td><td>0.286***<br>(0.033)<br>[0.222, 0.350]</td><td>0.229***<br>(0.043)<br>[0.144, 0.314]</td><td>0.341***<br>(0.035)<br>[0.273, 0.409]</td><td>0.352***<br>(0.033)<br>[0.288, 0.416]</td><td>0.038<br>(0.035)<br>[-0.031, 0.107]</td></tr><tr><td><strong>Nervous</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.275***<br>(0.056)<br>[0.165, 0.385]</td><td>0.294***<br>(0.034)<br>[0.227, 0.361]</td><td>0.342***<br>(0.031)<br>[0.281, 0.403]</td><td>0.287***<br>(0.038)<br>[0.212, 0.362]</td><td>0.308***<br>(0.033)<br>[0.243, 0.373]</td><td>0.107***<br>(0.027)<br>[0.054, 0.160]</td></tr></tbody></table>

NOTE: The table reports results for the outcomes from the Propensity-Score Kernel Matching method by group of individuals' characteristics (Age, Sex and GIR). Standard errors are reported in parentheses. Confidence intervals are in square brackets. P-values: *p < 0.1, **p < 0.05.

***p < 0.01. Source: Authors' calculation based on CARE Data 2015/2016 and mortality data up to 2021.

<table><caption>**Table 5**
Descriptive statistics by type of nursing home.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Private</th><th colspan="2">Public</th></tr><tr><th>For-profit</th><th>Non-profit</th><th>Hospital</th><th>Non-hospital</th></tr></thead><tbody><tr><td>Women (%)</td><td>76.8</td><td>78.7</td><td>70.3</td><td>73.1</td></tr><tr><td>Couple (%)</td><td>13.8</td><td>10.1</td><td>13.5</td><td>13.4</td></tr><tr><td>More than 80 (%)</td><td>85.7</td><td>82.9</td><td>74.1</td><td>80.3</td></tr><tr><td>GIR 1 or 2 (%)</td><td>54.4</td><td>45.4</td><td>59.2</td><td>45.9</td></tr><tr><td>GIR 3 or 4 (%)</td><td>38.2</td><td>41.3</td><td>32.6</td><td>42.7</td></tr><tr><td>GIR 5 or 6 (%)</td><td>7.4</td><td>13.3</td><td>8.2</td><td>11.4</td></tr><tr><td>Individualized income (€, mean)</td><td>24883</td><td>18779</td><td>16743</td><td>16932</td></tr><tr><td>Years in nursing home (mean)</td><td>2.7</td><td>3.6</td><td>3.6</td><td>4.1</td></tr><tr><td># Dwellings before (mean)</td><td>3.3</td><td>3.5</td><td>4.8</td><td>3.6</td></tr><tr><td># Floors in nursing home (mean)</td><td>2.3</td><td>2.1</td><td>2.1</td><td>2.0</td></tr><tr><td># Beds in nursing home (mean)</td><td>81.1</td><td>80.1</td><td>129.3</td><td>94.0</td></tr><tr><td>Nursing home fees (€, mean)</td><td>2532</td><td>1779</td><td>1651</td><td>1578</td></tr></tbody></table>

Note: The table reports the descriptive statistics by type of nursing home (private for profit, private non-profit, public hospital and public non-hospital facilities).

Source: Authors' calculation based on CARE Data 2015/2016 and mortality data up to 2021.

column, we look at the difference between private for-profit nursing homes and other institutions. In the second column, we compare private non-profit nursing homes and others. In the third and fourth columns, we look at the effect of public institutions, and in the last two columns, we compare private and public nursing homes as well as for-profit and non-profit ones.

In Table 7, we observe a series of interesting results. First, there is no difference between the different nursing homes in terms of mortality. In the first set of results of Table 7, there is no significant ATT regardless of the groups we compare. However, private nursing homes, in particular for-profit ones, display a higher risk of hospital nights and falls as well as lower level of subjective health than other institutions. When we compare for-profit retirement homes with other types of institution, we observe a positive and highly significant effect on the probability of falling and on the number of nights spent in the hospital. The probability of being in poor health is also higher. This is not true if you only compare private non-profit nursing homes with the others. On the contrary, they even have a positive effect on health. This difference between private for-profit and non-profit institutions is confirmed by the results presented in the last two columns. Finally, there is no main difference in indicators of well-being such as Sad. Respondents in private for-profit nursing homes show more nervousness, while the coefficient is reduced for those in non-profit facilities.

Observing that for-profit nursing homes compared to nonprofit ones appear to have a detrimental impact on health conditions but not on other well-being indicators offers a nuanced perspective. The first set of main results tend to show that being in a nursing home is better in terms of health than living at home, but this positive finding is actually driven by non-profit nursing homes. All else equal, when comparing nursing homes, it is better to choose a non-profit or a publicly owned one.

## 7. Conclusion

This paper investigates the impact of living in a nursing home compared to staying at home on a series of health and well-being outcomes for the elderly. Using propensity score matching techniques, we
<table><caption>**Table 6**
Analysis by type of nursing home.</caption><thead><tr><th rowspan="2">Dep. Variable</th><th colspan="2">Private</th><th colspan="2">Public</th><th rowspan="2">Private</th><th rowspan="2">Non-profit</th></tr><tr><th>For-profit</th><th>Non-profit</th><th>Hospital</th><th>Non-hospital</th></tr></thead><tbody><tr><td><strong>Months not in life</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-6.838***<br>(1.402)<br>[-9.586, -4.09]</td><td>-5.869***<br>(1.156)<br>[-8.135, -3.603]</td><td>-5.341***<br>(1.221)<br>[-7.734, -2.948]</td><td>-5.763***<br>(1.217)<br>[-8.148, -3.378]</td><td>-6.880***<br>(1.126)<br>[-9.087, -4.673]</td><td>-6.179***<br>(0.981)<br>[-8.102, -4.256]</td></tr><tr><td><strong>Hospital nights</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.015<br>(0.030)<br>[-0.074, 0.044]</td><td>-0.076***<br>(0.023)<br>[-0.121, -0.031]</td><td>-0.143***<br>(0.026)<br>[-0.194, -0.092]</td><td>-0.054**<br>(0.026)<br>[-0.105, -0.003]</td><td>-0.054**<br>(0.022)<br>[-0.097, -0.011]</td><td>-0.101***<br>(0.021)<br>[-0.142, -0.06]</td></tr><tr><td><strong>Fell</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.010<br>(0.031)<br>[-0.071, 0.051]</td><td>-0.103***<br>(0.025)<br>[-0.152, -0.054]</td><td>-0.157***<br>(0.027)<br>[-0.21, -0.104]</td><td>-0.117***<br>(0.026)<br>[-0.168, -0.066]</td><td>-0.071***<br>(0.024)<br>[-0.118, -0.024]</td><td>-0.138***<br>(0.021)<br>[-0.179, -0.097]</td></tr><tr><td><strong>Poor health</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.132***<br>(0.031)<br>[-0.193, -0.071]</td><td>-0.165***<br>(0.024)<br>[-0.212, -0.118]</td><td>-0.174***<br>(0.027)<br>[-0.227, -0.121]</td><td>-0.132***<br>(0.026)<br>[-0.183, -0.081]</td><td>-0.150***<br>(0.024)<br>[-0.197, -0.103]</td><td>-0.169***<br>(0.021)<br>[-0.21, -0.128]</td></tr><tr><td><strong>Unhappy</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.302***<br>(0.033)<br>[0.237, 0.367]</td><td>0.280***<br>(0.029)<br>[0.223, 0.337]</td><td>0.280***<br>(0.037)<br>[0.207, 0.353]</td><td>0.341***<br>(0.030)<br>[0.282, 0.4]</td><td>0.270***<br>(0.030)<br>[0.211, 0.329]</td><td>0.305***<br>(0.026)<br>[0.254, 0.356]</td></tr><tr><td><strong>Sad</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.017<br>(0.039)<br>[-0.059, 0.093]</td><td>0.034<br>(0.030)<br>[-0.025, 0.093]</td><td>0.016<br>(0.034)<br>[-0.051, 0.083]</td><td>-0.0035<br>(0.033)<br>[-0.068, 0.061]</td><td>0.014<br>(0.027)<br>[-0.039, 0.067]</td><td>0.009<br>(0.024)<br>[-0.038, 0.056]</td></tr><tr><td><strong>Not relaxed</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.282***<br>(0.036)<br>[0.211, 0.353]</td><td>0.286***<br>(0.030)<br>[0.227, 0.345]</td><td>0.302***<br>(0.037)<br>[0.229, 0.375]</td><td>0.296***<br>(0.031)<br>[0.235, 0.357]</td><td>0.258***<br>(0.030)<br>[0.199, 0.317]</td><td>0.298***<br>(0.029)<br>[0.241, 0.355]</td></tr><tr><td><strong>Nervous</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.329***<br>(0.033)<br>[0.264, 0.394]</td><td>0.263***<br>(0.026)<br>[0.212, 0.314]</td><td>0.305***<br>(0.035)<br>[0.236, 0.374]</td><td>0.316***<br>(0.028)<br>[0.261, 0.371]</td><td>0.287***<br>(0.027)<br>[0.234, 0.34]</td><td>0.282***<br>(0.028)<br>[0.229, 0.335]</td></tr></tbody></table>

NOTE: This Table reports the results by type of nursing home facility. Each ATT is estimated by taking the given type of nursing home as a treatment group and the control group is composed of people living at home. We apply a Propensity-Score Kernel Matching method. Standard errors are reported in parentheses. Confidence intervals are reported in square brackets. P-values: *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors' calculation based on CARE Data 2015/2016, mortality from Census up to 2021.

estimate the Average Treatment Effects on the Treated (ATT) across these outcomes. Our results reveal a trade-off between physical health and subjective well-being. While residing in a nursing home is significantly correlated with reduced mortality, lower likelihood of hospital stays as well as lower probability of falling or reporting poor health, it comes at the cost of a reduced emotional well-being, including a higher likelihood of experiencing sadness, nervousness, and lack of calm. To ensure the robustness of our results, we performed sensitivity analyses to test the conditional independence assumption and a falsification exercise to rule out confounding factors. Both approaches confirmed the validity of our findings.

These findings highlight the dual nature of nursing home care, which offers tangible health benefits but may not fully address the emotional and social needs of its residents (Kane et al., 2003). Importantly, we observe substantial heterogeneity in outcomes depending on the ownership type of the nursing home. Non-profit nursing homes are associated with better health outcomes, including lower risks of hospital nights, falls, and poor health, compared to for-profit institutions (Comondore et al., 2009a; Harrington et al., 2002). Our evidence aligns with (Grabowski and Hirth, 2003) which supports that non-profit facilities serves as a quality signal for uninformed nursing home patients. Interestingly, our analysis does not find significant differences in subjective well-being indicators, such as sadness and happiness, across different types of ownership.

Several mechanisms likely explain the observed differences. Home care may preserve older adults' sense of autonomy, identity, and social embeddedness (Wiles et al., 2012), while nursing home care offers structured medical support but may entail losses in personal control or social integration (Street and Burge, 2012). On the one hand,

institutional care settings provide structured medical oversight, routine monitoring, and rapid access to healthcare resources, which may explain improvements in clinical health indicators (Grabowski and Mor, 2020). On the

On the other hand, these same settings can reduce older adults' sense of autonomy, disrupt familiar social networks, and impose institutional routines that limit personal control (Bom et al., 2022; Böckerman et al., 2012; Comondore et al., 2009a). Understanding these mechanisms is essential for designing care models that balance clinical needs with emotional and social dimensions of aging. Saying differently, they highlight the importance of considering not just the provision of care, but also the quality and context in which care is delivered (Bostick et al., 2006; World Health Organization, 2015).

From a policy perspective, these insights are particularly relevant for ongoing debates about long-term care systems in France and globally (OECD, 2023). France faces growing demographic pressures due to rapid population aging, yet its long-term care system has historically struggled with balancing institutional care and home-based support (Carrère et al., 2023). The 2024 “Loi Grand Age” reform agenda, as well as ongoing debates around the financing and organization of long-term care, highlight the need for evidence-based strategies that promote not only medical safety but also social well-being and dignity. Our results suggest that policies should not only focus on expanding access but also on enhancing the quality of life in both home and institutional settings. In the French context, this may involve strengthening home care services, supporting family caregivers, and improving the social environment within nursing homes.

Beyond France, these findings have broader international relevance. As many countries confront similar challenges, questions about how to
<table><caption>**Table 7**
Analysis between types of nursing home.</caption><thead><tr><th rowspan="2">Dep. Variable</th><th colspan="2">Private</th><th colspan="2">Public</th><th rowspan="2">Private</th><th rowspan="2">Non-profit</th></tr><tr><th>For-profit</th><th>Non-profit</th><th>Hospital</th><th>Non-hospital</th></tr></thead><tbody><tr><td><strong>Months not in life</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.918<br>(0.981)<br>[-1.005, 2.841]</td><td>-0.968<br>(0.862)<br>[-2.658, 0.722]</td><td>-0.181<br>(0.890)<br>[-1.925, 1.563]</td><td>-0.161<br>(0.927)<br>[-1.978, 1.656]</td><td>0.634<br>(0.763)<br>[-0.861, 2.129]</td><td>0.219<br>(1.068)<br>[-1.874, 2.312]</td></tr><tr><td><strong>Hospital nights</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.070***<br>(0.025)<br>[0.021, 0.119]</td><td>-0.012<br>(0.020)<br>[-0.051, 0.027]</td><td>-0.064***<br>(0.021)<br>[-0.105, -0.023]</td><td>0.024<br>(0.022)<br>[-0.019, 0.067]</td><td>0.033*<br>(0.019)<br>[-0.004, 0.07]</td><td>-0.089***<br>(0.027)<br>[-0.142, -0.036]</td></tr><tr><td><strong>Fell</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.090***<br>(0.025)<br>[0.041, 0.139]</td><td>0.010<br>(0.022)<br>[-0.033, 0.053]</td><td>-0.063***<br>(0.023)<br>[-0.108, -0.018]</td><td>0.001<br>(0.024)<br>[-0.046, 0.048]</td><td>0.050**<br>(0.021)<br>[0.009, 0.091]</td><td>-0.092***<br>(0.028)<br>[-0.147, -0.037]</td></tr><tr><td><strong>Poor health</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.051**<br>(0.024)<br>[0.004, 0.098]</td><td>-0.048**<br>(0.021)<br>[-0.089, -0.007]</td><td>0.008<br>(0.023)<br>[-0.037, 0.053]</td><td>0.020<br>(0.023)<br>[-0.025, 0.065]</td><td>-0.010<br>(0.019)<br>[-0.047, 0.027]</td><td>-0.012<br>(0.027)<br>[-0.065, 0.041]</td></tr><tr><td><strong>Unhappy</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.009<br>(0.025)<br>[-0.04, 0.058]</td><td>0.027<br>(0.022)<br>[-0.016, 0.07]</td><td>-0.029<br>(0.023)<br>[-0.074, 0.016]</td><td>0.061***<br>(0.023)<br>[0.016, 0.106]</td><td>0.000<br>(0.020)<br>[-0.039, 0.039]</td><td>0.035<br>(0.028)<br>[-0.02, 0.09]</td></tr><tr><td><strong>Sad</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.054<br>(0.035)<br>[-0.015, 0.123]</td><td>0.014<br>(0.029)<br>[-0.043, 0.071]</td><td>-0.011<br>(0.032)<br>[-0.074, 0.052]</td><td>-0.047<br>(0.032)<br>[-0.11, 0.016]</td><td>0.015<br>(0.027)<br>[-0.038, 0.068]</td><td>-0.030<br>(0.039)<br>[-0.106, 0.046]</td></tr><tr><td><strong>Not relaxed</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.007<br>(0.023)<br>[-0.038, 0.052]</td><td>0.014<br>(0.020)<br>[-0.027, 0.055]</td><td>0.012<br>(0.021)<br>[-0.029, 0.053]</td><td>-0.006<br>(0.022)<br>[-0.049, 0.037]</td><td>0.003<br>(0.019)<br>[-0.034, 0.04]</td><td>0.047*<br>(0.027)<br>[-0.006, 0.1]</td></tr><tr><td><strong>Nervous</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.049*<br>(0.025)<br>[0.0, 0.098]</td><td>-0.020<br>(0.022)<br>[-0.063, 0.023]</td><td>-0.007<br>(0.023)<br>[-0.052, 0.038]</td><td>0.015<br>(0.024)<br>[-0.032, 0.062]</td><td>-0.006<br>(0.020)<br>[-0.045, 0.033]</td><td>-0.050*<br>(0.028)<br>[-0.105, 0.005]</td></tr></tbody></table>

Note: This table reports the ATT estimated by taking the given type of nursing home in the column as a treatment group, and the other types of nursing homes as a control group. All estimations are obtained with the Propensity-Score Kernel Matching method. Standard errors are reported in parentheses. Confidence intervals are reported in square brackets. P-values: *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors' calculation based on CARE Data 2015/2016, mortality from Census up to 2021.

design systems that balance cost-effectiveness, equity, and person-centered care are at the forefront of aging policy debates. By identifying the pathways through which care settings influence well-being, this research contributes to a global conversation about how to support aging populations in ways that honor their health, dignity, and social integration.

Another important dimension is the role of ownership type in shaping the outcomes we observe. Beyond the binary distinction between home care and institutional care, our findings suggest that the ownership structure of nursing homes (whether public, private for-profit, or private non-profit) can influence both health and well-being outcomes in meaningful ways. Prior research indicates that for-profit facilities may prioritize efficiency and cost-containment, sometimes at the expense of staffing levels or care quality (Comondore et al., 2009a; Harrington et al., 2012), whereas non-profit and public providers often emphasize resident-centered care and community integration and the preservation of social relationships (Mor et al., 2004; Harrington et al., 2002). Long-term care policy should account for ownership-specific practices, ensuring that financial incentives do not undermine care quality, especially in for-profit settings. Rather than treating all nursing homes as equivalent, policy frameworks should acknowledge the heterogeneity of institutional contexts and support governance structures that align with the multidimensional needs of aging populations.

This study has some limitations. Since we can only observe the well-being of the individuals at one point in time, we can not rule out the possibility that their well-being adapts and becomes better

the longer they stay in nursing homes. Indeed, Laferrière and Schoenmaeckers (2025) exploit panel data of institutionalized individuals in Europe and shows that after two years (for those who

survive), the negative effect on well-being fades away and living in a nursing home is associated with higher well-being. Another limitation is that we do not observe the quality of nursing homes directly, for example, with information related to staff turnover or patients to workers ratio and recreational activities, which could explain some of the mechanisms of the differences we found between private and public facilities. Further research could provide more evidence on the quality of nursing home institutions.

While our analysis focuses on the French context, the issues it raises are highly relevant inter-nationally. Many countries face similar demographic pressures, ongoing debates about the balance between home-based and institutional care, and concerns about the implications of ownership structures for care quality. Although the magnitude of the differences we document may vary across countries, the underlying mechanisms, such as trade-offs between medical oversight and autonomy, or the influence of ownership on staffing and resources, are common to a wide range of long-term care systems. However, our data reflect a period before the COVID-19 pandemic, during which infection control measures, visitation restrictions, and staff shortages may have affected residents' experiences in ways that differ from pre-pandemic or future conditions. These findings can therefore inform both French policy debates and broader international discussions on how to design and regulate long-term care systems that safeguard not only residents' health, but also their social and emotional well-being. Our results raise several questions for future research. First, understanding the mechanisms behind the effect on the well-being of nursing home care is crucial. Future work could explore interventions aimed at improving the social and emotional environment in nursing homes, such as enhancing social interactions, providing psychological support, or creating more
personalized care plans. Second, our study draws a picture at a given moment in time. Longitudinal studies examining the long-term trajectories of health and well-being for nursing home residents would provide a more comprehensive picture. It would be very interesting to see how and why our measures of outcomes change through time. Finally, further investigation is needed to identify the mechanisms through which non-profit nursing homes achieve superior health outcomes. This could involve study-ing staffing models, resource allocation and care practices that differentiate non-profit from for-profit institutions.

### CRediT authorship contribution statement

**Elena Bassoli:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. **Mathieu Lefebvre:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Conceptualization. **Jérôme Schoenmaeckers:** Writing – review & editing, Validation, Methodology, Formal analysis, Data curation, Conceptualization.

### Disclosure statement

This work of the Interdisciplinary Thematic Institute MAKErS, as part of the ITI 2021–2028 program of the University of Strasbourg, CNRS and INSERM, was supported by IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT’US project (ANR-20-SFRI-0012). Bassoli thanks funding from HI-GEN-SHARE GRA101066047.

### Declaration of competing interest

The author declare no conflict of interest.

### Acknowledgments

This work of the Interdisciplinary Thematic Institute MAKErS, as part of the ITI 2021–2028 program of the University of Strasbourg, CNRS and INSERM, was supported by IdEx Unistra (ANR-10-IDEX-0002), and by SFRI-STRAT’US project (ANR-20-SFRI-0012). Bassoli thanks funding from HI-GEN-SHARE GRA101066047.

## Appendix A. Appendix

### Appendix A.1. Additional tables.

<table><caption>**Table A.1**
Propensity score estimation</caption><thead><tr><th></th><th>Nursing home</th><th>Private profit NH</th><th>Private not profit NH</th><th>Public hospital NH</th><th>Public no hospital NH</th><th>Private NH NH</th><th>Public NH</th><th>Not profit NH</th></tr></thead><tbody><tr><td>Covariates</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Health characteristics</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GIR≥5</td><td>-0.315***<br>(0.006)</td><td>-0.101***<br>(0.007)</td><td>-0.106***<br>(0.006)</td><td>-0.129***<br>(0.007)</td><td>-0.089***<br>(0.006)</td><td>-0.187***<br>(0.007)</td><td>-0.198***<br>(0.007)</td><td>-0.266***<br>(0.007)</td></tr><tr><td>Chronic disease</td><td>-0.109***<br>(0.008)</td><td>-0.022***<br>(0.005)</td><td>-0.040***<br>(0.006)</td><td>-0.017***<br>(0.006)</td><td>-0.020***<br>(0.005)</td><td>-0.063***<br>(0.007)</td><td>-0.037***<br>(0.007)</td><td>-0.081***<br>(0.007)</td></tr><tr><td>Socio-economic characteristics</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>0.009***<br>(0.000)</td><td>0.003***<br>(0.000)</td><td>0.004***<br>(0.000)</td><td>0.002***<br>(0.000)</td><td>0.003***<br>(0.000)</td><td>0.006***<br>(0.000)</td><td>0.005***<br>(0.000)</td><td>0.008***<br>(0.000)</td></tr><tr><td>Woman</td><td>-0.026***<br>(0.007)</td><td>0.005<br>(0.005)</td><td>0.005<br>(0.006)</td><td>-0.024***<br>(0.006)</td><td>-0.019***<br>(0.005)</td><td>0.010<br>(0.007)</td><td>-0.040***<br>(0.007)</td><td>-0.032***<br>(0.000)</td></tr><tr><td>Percentiles of income</td><td>-0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>-0.000<br>(0.000)</td><td>-0.001***<br>(0.000)</td><td>-0.001***<br>(0.000)</td><td>0.001***<br>(0.000)</td><td>-0.001***<br>(0.000)</td><td>-0.002***<br>(0.000)</td></tr><tr><td>Family characteristics</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>In couple</td><td>-0.121***<br>(0.009)</td><td>-0.049***<br>(0.006)</td><td>-0.057***<br>(0.008)</td><td>-0.029***<br>(0.007)</td><td>-0.018**<br>(0.007)</td><td>-0.102***<br>(0.009)</td><td>-0.042***<br>(0.009)</td><td>-0.083***<br>(0.010)</td></tr><tr><td># caregivers</td><td>-0.024***<br>(0.003)</td><td>-0.005**<br>(0.002)</td><td>-0.011***<br>(0.002)</td><td>-0.006***<br>(0.002)</td><td>-0.002<br>(0.002)</td><td>-0.016***<br>(0.003)</td><td>-0.008**<br>(0.003)</td><td>-0.019***<br>(0.003)</td></tr><tr><td>Pseudo-R<sup>2</sup></td><td>0.39</td><td>0.22</td><td>0.18</td><td>0.18</td><td>0.17</td><td>0.25</td><td>0.24</td><td>0.32</td></tr><tr><td>Obs</td><td>10784</td><td>10784</td><td>10784</td><td>10784</td><td>10784</td><td>10784</td><td>10784</td><td>10784</td></tr></tbody></table>

Notes: This Table reports the average treatment of the treated (ATT) from the propensity score matching estimates for each outcome. We report results based different characteristics. Source: Authors’ calculation based on CARE Data, years 2015/2016.

<table><caption>**Table A.2**
Main results: Average Treatment of the Treated (ATT)</caption><thead><tr><th rowspan="2">Dep. Variable</th><th>No exact match</th><th>Exact match on sex</th><th>Exact match on sex &amp; GIR</th><th>Exact match on sex, GIR &amp; age</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Months not in life</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-5.994***<br>(1.025)</td><td>-6.190***<br>(1.004)</td><td>-6.014***<br>(0.987)</td><td>-4.887***<br>(0.951)</td></tr><tr><td>Treated</td><td>2757</td><td>2743</td><td>2689</td><td>2509</td></tr><tr><td>Control</td><td>6955</td><td>7078</td><td>7264</td><td>4995</td></tr><tr><td>Obs</td><td>9712</td><td>9821</td><td>9953</td><td>7504</td></tr><tr><td><strong>Hospital nights</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.092***<br>(0.020)</td><td>-0.077***<br>(0.021)</td><td>-0.070***<br>(0.021)</td><td>-0.080***<br>(0.020)</td></tr><tr><td>Treated</td><td>2899</td><td>2889</td><td>2858</td><td>2637</td></tr><tr><td>Control</td><td>6742</td><td>6995</td><td>7219</td><td>5022</td></tr><tr><td>Obs</td><td>9641</td><td>9884</td><td>10077</td><td>7659</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table A.2 (continued)**</caption><thead><tr><th>Dep. Variable</th><th>No exact match<br>(1)</th><th>Exact match on sex<br>(2)</th><th>Exact match on sex & GIR<br>(3)</th><th>Exact match on sex, GIR & age<br>(4)</th></tr></thead><tbody><tr><td><strong>Fell</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.116***<br>(0.021)</td><td>-0.109***<br>(0.021)</td><td>-0.119***<br>(0.021)</td><td>-0.104***<br>(0.020)</td></tr><tr><td>Treated</td><td>2917</td><td>2892</td><td>2848</td><td>2617</td></tr><tr><td>Control</td><td>6884</td><td>7079</td><td>7208</td><td>5043</td></tr><tr><td>Obs</td><td>9801</td><td>9971</td><td>10056</td><td>7660</td></tr><tr><td><strong>Poor health</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>-0.152***<br>(0.021)</td><td>-0.153***<br>(0.021)</td><td>-0.163***<br>(0.022)</td><td>-0.188***<br>(0.020)</td></tr><tr><td>Treated</td><td>2934</td><td>2924</td><td>2880</td><td>2667</td></tr><tr><td>Control</td><td>6843</td><td>7032</td><td>7199</td><td>5046</td></tr><tr><td>Obs</td><td>9777</td><td>9956</td><td>10079</td><td>7713</td></tr><tr><td><strong>Unhappy</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.309***<br>(0.026)</td><td>0.315***<br>(0.025)</td><td>0.317***<br>(0.027)</td><td>0.261***<br>(0.023)</td></tr><tr><td>Treated</td><td>2931</td><td>2876</td><td>2879</td><td>2280</td></tr><tr><td>Control</td><td>6841</td><td>7006</td><td>6802</td><td>4485</td></tr><tr><td>Obs</td><td>9772</td><td>9882</td><td>9681</td><td>6765</td></tr><tr><td><strong>Sad</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.010<br>(0.024)</td><td>-0.005<br>(0.024)</td><td>-0.010<br>(0.026)</td><td>-0.024<br>(0.022)</td></tr><tr><td>Treated</td><td>1672</td><td>1663</td><td>1670</td><td>1516</td></tr><tr><td>Control</td><td>6972</td><td>7116</td><td>6846</td><td>4497</td></tr><tr><td>Obs</td><td>8644</td><td>8779</td><td>8516</td><td>6013</td></tr><tr><td><strong>Not relaxed</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.310***<br>(0.030)</td><td>0.323***<br>(0.030)</td><td>0.314***<br>(0.033)</td><td>0.291***<br>(0.024)</td></tr><tr><td>Treated</td><td>2921</td><td>2895</td><td>2879</td><td>2290</td></tr><tr><td>Control</td><td>6976</td><td>7095</td><td>6892</td><td>4565</td></tr><tr><td>Obs</td><td>9897</td><td>9990</td><td>9771</td><td>6855</td></tr><tr><td><strong>Nervous</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>ATT</td><td>0.308***<br>(0.026)</td><td>0.291***<br>(0.034)</td><td>0.258***<br>(0.045)</td><td>0.263***<br>(0.023)</td></tr><tr><td>Treated</td><td>2942</td><td>2889</td><td>2880</td><td>2293</td></tr><tr><td>Control</td><td>702</td><td>7131</td><td>6927</td><td>4581</td></tr><tr><td>Obs</td><td>3644</td><td>10020</td><td>9807</td><td>6874</td></tr></tbody></table>

Notes: This table reports the average treatment of the treated (ATT) from the propensity score matching estimates for each outcome. We report results based on: no exact matching (1), exact match on sex (2); sex and GIR (3); and sex, GIR and Age (4). Standard errors are in parenthesis. P-values: *p < 0.1, **p < 0.05, ***p < 0.01.

Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

<table><caption>**Table A.3**
Analysis with alternative matching algorithms</caption><thead><tr><th rowspan="2">Dep. variable</th><th colspan="2">Algorithm Alternatives</th><th colspan="3">Ichino et al. (2006)</th></tr><tr><th>NearestNeighbour (3)</th><th>NearestNeighbour (1)</th><th>Outcome Effect Γ</th><th>Selection Effect Λ</th><th>ATT (S.E.)</th></tr></thead><tbody><tr><td>Months not in life</td><td>ATT (S.E.)</td><td>ATT (S.E.)</td><td>PSM (Nearest)</td><td>1</td><td>-6.993*** (1.233)</td></tr><tr><td></td><td>(1) -6.987*** (1.044)</td><td>-6.993*** (1.233)</td><td>Confounder-like</td><td></td><td></td></tr><tr><td></td><td>(2) -6.519*** (1.028)</td><td>-6.929*** (1.172)</td><td>Female</td><td>0.765</td><td>-6.538*** (1.257)</td></tr><tr><td></td><td>(3) -6.174*** (1.004)</td><td>-7.359*** (1.168)</td><td>In couple</td><td>0.540</td><td>-8.155*** (1.423)</td></tr><tr><td></td><td>(4) -5.109*** (1.008)</td><td>-5.272*** (1.237)</td><td>age80+</td><td>5.044</td><td>-10.443*** (1.481)</td></tr><tr><td>Poor health</td><td>ATT (S.E.)</td><td>ATT (S.E.)</td><td>PSM (Nearest)</td><td>1</td><td>-0.142*** (0.025)</td></tr><tr><td></td><td>(1) -0.144*** (0.022)</td><td>-0.142*** (0.025)</td><td>Confounder-like</td><td></td><td></td></tr><tr><td></td><td>(2) -0.152*** (0.022)</td><td>-0.147*** (0.025)</td><td>Female</td><td>1.099</td><td>0.141*** (0.031)</td></tr><tr><td></td><td>(3) -0.133*** (0.022)</td><td>-0.157*** (0.026)</td><td>In couple</td><td>0.763</td><td>-0.164*** (0.034)</td></tr><tr><td></td><td>(4) -0.194*** (0.021)</td><td>-0.171*** (0.025)</td><td>age80+</td><td>1.486</td><td>-0.179*** (0.037)</td></tr><tr><td>Unhappy</td><td>ATT (S.E.)</td><td>ATT (S.E.)</td><td>PSM (Nearest)</td><td>1</td><td>0.307*** (0.033)</td></tr><tr><td></td><td>(1) 0.298*** (0.033)</td><td>0.307*** (0.033)</td><td>Confounder-like</td><td></td><td></td></tr><tr><td></td><td>(2) 0.329*** (0.029)</td><td>0.326*** (0.034)</td><td>Female</td><td>0.679</td><td>0.323*** (0.049)</td></tr><tr><td></td><td>(3) 0.329*** (0.029)</td><td>0.337*** (0.035)</td><td>In couple</td><td>2.211</td><td>0.367*** (0.050)</td></tr><tr><td></td><td>(4) 0.224*** (0.024)</td><td>0.269*** (0.028)</td><td>age80+</td><td>0.611</td><td>0.374*** (0.061)</td></tr></tbody></table>

Notes: This table reports the average treatment of the treated (ATT) from the propensity score matching estimates using alternative algorithms following (Ichino et al., 2008). We report results based on: no exact matching (1), exact match on sex (2); sex and GIR (3); and sex, GIR and Age (4). Standard errors are in parenthesis. P-values.

*p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.
<table><caption>**Table A.4**

OLS Analysis for several outcomes</caption><thead><tr><th>Dep. variable</th><th>(1)<br>Baseline</th><th>(2)<br>including<br>GIR(0-6)</th><th>(3)<br>including<br>GIR(0/1)</th><th>(4)<br>including<br>categorical GIR</th></tr></thead><tbody><tr><td><strong>Months not in life</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>4.941***<br>(0.416)</td><td>-1.926***<br>(0.478)</td><td>-1.575***<br>(0.459)</td><td>-1.268***<br>(0.479)</td></tr><tr><td>Observations</td><td>10,631</td><td>10,609</td><td>10,602</td><td>10,602</td></tr><tr><td><strong>Hospital nights</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.003<br>(0.010)</td><td>-0.062***<br>(0.012)</td><td>-0.049***<br>(0.012)</td><td>-0.053***<br>(0.012)</td></tr><tr><td>Observations</td><td>10,779</td><td>10,757</td><td>10,750</td><td>10,750</td></tr><tr><td><strong>Fell</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.025**<br>(0.011)</td><td>-0.074***<br>(0.013)</td><td>-0.071***<br>(0.012)</td><td>-0.068***<br>(0.013)</td></tr><tr><td>Observations</td><td>10,782</td><td>10,759</td><td>10,752</td><td>10,752</td></tr><tr><td><strong>Poor health</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.040***<br>(0.010)</td><td>-0.174***<br>(0.012)</td><td>-0.108***<br>(0.011)</td><td>-0.149***<br>(0.012)</td></tr><tr><td>Observations</td><td>10,823</td><td>10,800</td><td>10,793</td><td>10,793</td></tr><tr><td><strong>Unhappy</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.202***<br>(0.012)</td><td>0.228***<br>(0.015)</td><td>0.284***<br>(0.013)</td><td>0.192***<br>(0.015)</td></tr><tr><td>Observations</td><td>10,208</td><td>10,185</td><td>10,178</td><td>10,178</td></tr><tr><td><strong>Sad</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.101***<br>(0.014)</td><td>-0.001<br>(0.016)</td><td>0.0144<br>(0.015)</td><td>0.016<br>(0.016)</td></tr><tr><td>Observations</td><td>9012</td><td>8994</td><td>8989</td><td>8989</td></tr><tr><td><strong>Not relaxed</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.259***<br>(0.012)</td><td>0.264***<br>(0.015)</td><td>0.322***<br>(0.013)</td><td>0.245***<br>(0.014)</td></tr><tr><td>Observations</td><td>10,299</td><td>10,276</td><td>10,269</td><td>10,269</td></tr><tr><td><strong>Nervous</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>NH</td><td>0.376***<br>(0.010)</td><td>0.210***<br>(0.012)</td><td>0.307***<br>(0.011)</td><td>0.211***<br>(0.012)</td></tr><tr><td>Observations</td><td>10,331</td><td>10,308</td><td>10,301</td><td>10,301</td></tr></tbody></table>

Notes: This table reports the OLS results. Column (2) includes the GIR variables, Column (3) the respective GIR binary indicator and column (4) the categorical one. Standard errors are in parenthesis. P-values: *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

<table><caption>**Table A.5**

Main results with covariates added sequentially</caption><thead><tr><th>Addition of covariates</th><th>(1)<br>Sex</th><th>(2)<br>+ Couple</th><th>(3)<br>+ Age</th><th>(4)<br>+ Income</th><th>(5)<br>+ GIR</th><th>(6)<br>+ Chronic disease</th><th>(7)<br>+ # of helpers</th></tr></thead><tbody><tr><td>ATT Months not in life</td><td>11.640***<br>(0.394)</td><td>10.010***<br>(0.427)</td><td>2.949***<br>(0.563)</td><td>2.800***<br>(0.628)</td><td>-5.711***<br>(0.923)</td><td>-5.871***<br>(0.964)</td><td>-5.994***<br>(1.025)</td></tr><tr><td># Treated</td><td>2938</td><td>2933</td><td>2932</td><td>2768</td><td>2790</td><td>2760</td><td>2757</td></tr><tr><td># Control</td><td>7736</td><td>7736</td><td>7736</td><td>6072</td><td>6217</td><td>6771</td><td>6955</td></tr><tr><td>Observations</td><td>10,674</td><td>10,669</td><td>10,669</td><td>10,663</td><td>10,641</td><td>10,609</td><td>10,609</td></tr><tr><td>ATT Hospital nights</td><td>0.035***<br>(0.009)</td><td>0.011<br>(0.010)</td><td>-0.0073<br>(0.012)</td><td>-0.021<br>(0.014)</td><td>-0.100***<br>(0.019)</td><td>-0.102***<br>(0.020)</td><td>-0.092***<br>(0.020)</td></tr><tr><td># Treated</td><td>3088</td><td>3084</td><td>3084</td><td>2911</td><td>2930</td><td>2888</td><td>2899</td></tr><tr><td># Control</td><td>7729</td><td>7729</td><td>7729</td><td>5941</td><td>6208</td><td>6728</td><td>6742</td></tr><tr><td>Observations</td><td>10,817</td><td>10,813</td><td>10,813</td><td>10,807</td><td>10,785</td><td>10,757</td><td>10,757</td></tr><tr><td>ATT Fell</td><td>0.098***<br>(0.010)</td><td>0.072***<br>(0.011)</td><td>0.003<br>(0.013)</td><td>0.003<br>(0.014)</td><td>-0.126***<br>(0.019)</td><td>-0.120***<br>(0.020)</td><td>-0.116***<br>(0.021)</td></tr><tr><td># Treated</td><td>3095</td><td>3090</td><td>3090</td><td>2915</td><td>2920</td><td>2902</td><td>2917</td></tr><tr><td># Control</td><td>7728</td><td>7728</td><td>7728</td><td>6023</td><td>6254</td><td>6908</td><td>6884</td></tr><tr><td>Observations</td><td>10,823</td><td>10,818</td><td>10,818</td><td>10,812</td><td>10,789</td><td>10,759</td><td>10,759</td></tr><tr><td>ATT Poor health</td><td>0.054***<br>(0.010)</td><td>0.036***<br>(0.010)</td><td>0.028**<br>(0.012)</td><td>0.009<br>(0.014)</td><td>-0.212***<br>(0.019)</td><td>-0.162***<br>(0.020)</td><td>-0.152***<br>(0.021)</td></tr><tr><td># Treated</td><td>3135</td><td>3129</td><td>3129</td><td>2948</td><td>2951</td><td>2930</td><td>2934</td></tr><tr><td># Control</td><td>7736</td><td>7736</td><td>7736</td><td>6076</td><td>6089</td><td>6713</td><td>6843</td></tr><tr><td>Observations</td><td>10,871</td><td>10,865</td><td>10,865</td><td>10,859</td><td>10,836</td><td>10,800</td><td>10,800</td></tr><tr><td>ATT Unhappy</td><td>0.117***<br>(0.010)</td><td>0.175***<br>(0.011)</td><td>0.194***<br>(0.013)</td><td>0.205***<br>(0.015)</td><td>0.316***<br>(0.023)</td><td>0.302***<br>(0.026)</td><td>0.309***<br>(0.026)</td></tr><tr><td># Treated</td><td>3135</td><td>3129</td><td>3127</td><td>2939</td><td>2930</td><td>2922</td><td>2931</td></tr><tr><td># Control</td><td>7121</td><td>7121</td><td>7121</td><td>5686</td><td>6923</td><td>7044</td><td>6841</td></tr><tr><td>Observations</td><td>10,256</td><td>10,250</td><td>10,250</td><td>10,244</td><td>10,221</td><td>10,185</td><td>10,185</td></tr><tr><td>ATT Sad</td><td>0.118***<br>(0.013)</td><td>0.079***<br>(0.013)</td><td>0.062***<br>(0.015)</td><td>0.061***<br>(0.017)</td><td>-0.011<br>(0.021)</td><td>0.002<br>(0.024)</td><td>0.010<br>(0.024)</td></tr><tr><td># Treated</td><td>1818</td><td>1814</td><td>1813</td><td>1706</td><td>1694</td><td>1655</td><td>1672</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table A.5 (continued)**</caption><thead><tr><th rowspan="2">Addition of covariates</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr><tr><th>Sex</th><th>+ Couple</th><th>+ Age</th><th>+ Income</th><th>+ GIR</th><th>+ Chronic disease</th><th>+ # of helpers</th></tr></thead><tbody><tr><td># Control</td><td>7227</td><td>7227</td><td>7227</td><td>5379</td><td>6303</td><td>7092</td><td>6972</td></tr><tr><td>Observations</td><td>9045</td><td>9041</td><td>9041</td><td>9035</td><td>9017</td><td>8994</td><td>8994</td></tr><tr><td>ATT Not relaxed</td><td>0.258***<br>(0.010)</td><td>0.252***<br>(0.010)</td><td>0.233***<br>(0.013)</td><td>0.253***<br>(0.015)</td><td>0.295***<br>(0.027)</td><td>0.276***<br>(0.030)</td><td>0.310***<br>(0.030)</td></tr><tr><td># Treated</td><td>3135</td><td>3129</td><td>3126</td><td>3126</td><td>2959</td><td>2955</td><td>2920</td></tr><tr><td># Control</td><td>7212</td><td>7212</td><td>7212</td><td>7212</td><td>5707</td><td>6972</td><td>7080</td></tr><tr><td>Observations</td><td>10,347</td><td>10,341</td><td>10,341</td><td>10,335</td><td>10,312</td><td>10,276</td><td>10,276</td></tr><tr><td>ATT Nervous</td><td>0.345***<br>(0.010)</td><td>0.351***<br>(0.010)</td><td>0.371***<br>(0.011)</td><td>0.348***<br>(0.013)</td><td>0.296***<br>(0.024)</td><td>0.278***<br>(0.032)</td><td>0.308***<br>(0.026)</td></tr><tr><td># Treated</td><td>3135</td><td>3129</td><td>3126</td><td>2945</td><td>2953</td><td>2916</td><td>2942</td></tr><tr><td># Control</td><td>7243</td><td>7243</td><td>7243</td><td>5759</td><td>6993</td><td>7138</td><td>7020</td></tr><tr><td>Observations</td><td>10,378</td><td>10,372</td><td>10,372</td><td>10,367</td><td>10,344</td><td>10,308</td><td>10,308</td></tr></tbody></table>

Notes: This table reports the average treatment of the treated (ATT) from the propensity score matching estimates. We add covariates sequentially as it follows: sex, being in couple, age, income, GIR, chronic disease and number of helpers. Standard errors are in parenthesis. P-values: *p < 0.1, **p < 0.05.

***p < 0.01. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

<figure><img src="image_13.png" /><figcaption>**Figure A.1.** Distribution of control and treated</figcaption></figure>

Notes: This figure reports the kernel distribution of treated and control individuals. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

<caption>**Table A.6**</caption>

Descriptive statistics: average differences

<table><thead><tr><th rowspan="2">Means</th><th colspan="3">Raw</th><th colspan="3">Matched(ATT)</th></tr><tr><th>Treated</th><th>Untreated</th><th>StdDif</th><th>Treated</th><th>Untreated</th><th>StdDif</th></tr></thead><tbody><tr><td>Age</td><td>86.356</td><td>75.101</td><td>1.269</td><td>86.419</td><td>86.419</td><td>0.000</td></tr><tr><td>GIR</td><td>2.808</td><td>5.251</td><td>-1.963</td><td>3.119</td><td>3.119</td><td>0.000</td></tr><tr><td>Female</td><td>0.747</td><td>0.611</td><td>0.296</td><td>0.773</td><td>0.773</td><td>0.000</td></tr><tr><td>Income</td><td>18914.3</td><td>27625.8</td><td>-0.483</td><td>18636.49</td><td>18784.68</td><td>-0.008</td></tr><tr><td>Couple</td><td>0.127</td><td>0.487</td><td>-0.848</td><td>0.114</td><td>0.140</td><td>-0.060</td></tr><tr><td>#Helpers</td><td>0.943</td><td>0.617</td><td>0.307</td><td>0.983</td><td>1.309</td><td>-0.309</td></tr><tr><td>Chronic disease</td><td>0.695</td><td>0.762</td><td>-0.15</td><td>0.683</td><td>0.788</td><td>-0.237</td></tr></tbody></table>

Notes: This table reports the differences in mean without (Raw) and with matching procedure (Matched ATT). Standard are reported. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

<caption>**Table A.7**</caption>

Descriptive statistics: T-test before and after for covariates

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th>Months not in life</th><th>Hospital nights</th><th>Fell</th><th>Poor health</th><th>Unhappy</th><th>Sad</th><th>Not relaxed</th><th>Nervous</th></tr><tr><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="2">Age</td><td>ATT</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td rowspan="2">GIR</td><td>ATT</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td rowspan="2">Female</td><td>ATT</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td rowspan="2">Income</td><td>ATT</td><td>0.779</td><td>0.852</td><td>0.791</td><td>0.842</td><td>0.760</td><td>0.153</td><td>0.782</td><td>0.776</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr></tbody></table>

(continued on next page)
<table><caption>Table A.7 (continued)</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th>Months not in life</th><th>Hospital nights</th><th>Fell</th><th>Poor health</th><th>Unhappy</th><th>Sad</th><th>Not relaxed</th><th>Nervous</th></tr><tr><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th><th>p-value</th></tr></thead><tbody><tr><td rowspan="2">Couple</td><td>ATT</td><td>0.059</td><td>0.076</td><td>0.053</td><td>0.063</td><td>0.069</td><td>0.000</td><td>0.085</td><td>0.068</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td rowspan="2">#Helpers</td><td>ATT</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td rowspan="2">Chronic disease</td><td>ATT</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>NATE</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr></tbody></table>

Notes: This table reports p-values from T-test for each variable and outcomes before and after matching. Source: Authors' calculation based on CARE Data, years 2015/2016, information on mortality available from Census up to 2021.

## Appendix A.2. Sensitivity analysis: Ichino et al. (2008 method)

The assumption of the analysis is that the conditional independence assumption no longer holds given the set of covariates X but it holds given X and an unobserved binary variable U. This means that as long as U is not observed, the outcome of the control individuals cannot be used to estimate the counterfactual outcome of the treated individuals. We assume that U may impact both the treatment and the outcome and that the distribution U can be fully characterized by four probabilities $p_{ij}$ given the treatment T and the outcome Y:

$$p_{ij}(ij) = P(U=1|T=1, Y=j)$$

with $i, j \in 0, 1$, which give the probability that $U = 1$ in each of the four groups defined by the treatment status and the outcome value. Given arbitrary values of the parameters $p_{ij}$, a value of U is attributed to each individual according to its belonging to one of the four groups defined by the treatment status and the outcome value. U can then be treated as any other observed covariate and is included in the set of variables used to estimate the propensity score and to compute the effect of the treatment. The difference $d = p_{01} - p_{00}$ can be interpreted as a measure of the effect of U on the untreated outcome, and the difference $s = p_{11} - p_{01}$ as a measure of the effect of U on the selection into treatment. The expression $p_1$ and $p_0$ correspond to the probability of being treated given the value of U and controlling for the set of covariates X: $p_1 = P(T=1|U=1, X)$ and $p_0 = P(T=1|U=0, X)$.

(Ichino et al., 2008) define the selection effect $\Lambda$ as the effect of U on the relative probability to be assigned to the treatment and the outcome effect $\Gamma$ as the effect of U on the relative probability to have a positive outcome in the absence of treatment.

$$\Lambda = \frac{\frac{P(T=1|U=1, X)}{P(T=0|U=1, X)}}{\frac{P(T=1|U=0, X)}{P(T=0|U=0, X)}}$$

and

$$\Gamma = \frac{\frac{P(Y=1|T=0, U=1, X)}{P(Y=0|T=0, U=1, X)}}{\frac{P(Y=1|T=0, U=0, X)}{P(Y=0|T=0, U=0, X)}}$$

By measuring the two effects $\Gamma$ and $\Lambda$, one can characterize the simulated confounder U an outcome effect of $\Gamma > 1(< 1)$ means that the unobserved U positively (negatively) affect the outcome. Similarly if the selection effect $\Lambda > 1(< 1)$, it means that the unobserved U increases (decreases) the probability to be treated.

In order to pick the parameters $p_{ij}$, we follow (Ichino et al., 2008) and assume that the distribution of the unobserved variable U is similar to the empirical distribution of important binary covariates, we can fix $p_{ij}$ according to their values for a set of covariates used in the propensity score model. If this does not confound our results and the ATTs are very close to the ones presented without this “unobserved and hypothetical” binary variable U, then the exercise supports the robustness of the estimates derived under the conditional independence assumption.

## Data availability

The authors do not have permission to share data.

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