# Inequalities in access to nursing home places in France

Federico Bietti

► To cite this version:

Federico Bietti. Inequalities in access to nursing home places in France. bsps annual conference 2025, Sep 2025, Swansea, United Kingdom. pp.1-7. hal-05527167

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# Inequalities in access to nursing home places in France: Between service provision structure and territorial characteristics

## BSPS CONFERENCE

Swansea University- September 2, 2025

Federico Bietti

### Introduction

Our research focuses on the territorial deployment of social protection, and more specifically on the provision of long-term care.

In France, among people over the age of 65, 8% are dependent, and this figure rises to 20% for those over 85.

Within this dependent population, part of our work—of which I present an excerpt today— examines inequalities in access to nursing home (EHPAD) places across territories, with particular attention to the rural–urban gap.

This research aims to analyze the availability of nursing home places at the municipal level, with a focus on territorial “solidarities.” In particular, we investigate neighborhood effects between municipalities and the potential pooling of resources, especially in rural areas.

In France, nursing homes are designed to accommodate the most dependent older adults. According to the official national dependency scale, which ranges from 1 (most dependent) to 6 (least dependent), these facilities mainly admit individuals classified between levels 1 and 4. Moreover, the average age of entry into a nursing home is relatively high—around 86 years— meaning that residents are typically very old and highly dependent.

Our team's initial research has shown a transformation in the supply of nursing homes since 2009. The sector appears to be moving toward greater privatization, accompanied by a concentration of operators. This process seems to unfold at two different speeds depending on the type of territory: urban versus rural.

As a result, France seems to juxtapose two models of social protection: a more mixed model in urban areas, with a strong presence of private providers, and a more predominantly public model in rural areas.
## Data

For us, the statistical, demographic, and political unit of analysis is the *commune*. It simultaneously reflects a sociodemographic identity (and a potential demand for care) as well as a type of provision.

We work with administrative data on care facilities produced by the Ministry of Social Affairs. Using this dataset, I aggregated nursing home supply at the municipal level, taking into account the total number of available places, the distribution by provider status, pricing, and the number of places reserved for residents eligible for housing assistance.

We also used territorial data from the national census and INSEE. Our analysis relies on INSEE's typology of territories, which itself is based on Eurostat's population density classification:

* **Urban clusters:** Territories with a population density greater than 1,500 inhabitants per km² and a total population of at least 50,000.

* **Intermediate areas:**

o Second-level urban clusters: territories with a similar density but a population between 5,000 and 50,000.

o Semi-dense urban clusters: contiguous areas with a minimum density of 300 inhabitants per km² and at least 5,000 inhabitants in total.

* **Rural areas:** Classified into three subcategories depending on density and clustering:

o Level 5 – *Rural towns*: communes with a density of at least 300 inhabitants per km² and a population between 500 and 4,999.

o Level 6 – *Low-density rural areas*: isolated rural cells with densities between 50 and 300 inhabitants per km², not included in rural town clusters.

o Level 7 – *Very low-density rural areas*: rural cells with fewer than 50 inhabitants per km².

Beyond population density, these three zones also reflect contrasting levels of demographic ageing, income, sociological profiles, and infrastructure.

For example, ageing can be measured by the ratio between the population aged 65 and over and those under 25.

* Urban areas have an ageing index of **0.53**, meaning there are nearly twice as many people under 25 as there are aged 65 and over (1 / 0.53 ≈ 1.9).

* Intermediate areas have an ageing index of **0.8**, meaning there are about one-quarter (≈ 25%) more people under 25 compared to those aged 65 and older.

* Rural areas have an even higher index, **0.87**, which points to a situation close to parity between the under-25 population and those aged 65 and over. Rural communes are thus characterized by a more advanced ageing process.
<table><thead><tr><td>Category</td><td>Median Income (€)</td><td>Share of Managers (%)</td><td>Area (km²)</td><td>Medical Doctors per 1000 inhab</td><td>Nurses per 1000 inhab</td><td>Number of Jobs</td><td>Aging Index (65+/<25 ans)</td></tr></thead><tbody><tr><td>Urbain</td><td>24 488</td><td>19,5</td><td>12,2</td><td>32,5</td><td>39,6</td><td>17085</td><td>0,534</td></tr><tr><td>Intermédiaire</td><td>24 853</td><td>11,8</td><td>15,6</td><td>5,29</td><td>9,42</td><td>2372</td><td>0,803</td></tr><tr><td>Rural</td><td>23 014</td><td>6,54</td><td>15,9</td><td>0,494</td><td>1,06</td><td>186</td><td>0,868</td></tr></tbody></table>

## Description

### The distribution of care facility places across territories

Keeping in mind the general characteristics of the three zones, we then examined how care facility places are distributed.

In 2024, France has about 609,580 care facility places, divided among public, private, and non-profit operators.

<figure><img src="image_2.png" /></figure>

### The public sector is therefore the main operator of nursing homes.

These places are distributed across 4,947 municipalities, whether urban (588, or 12%), intermediate (1,431, or 29%), or rural (2,928, or 59%).

In absolute terms, depending on the type of municipality, we observe a distribution that favors rural areas, which concentrate nearly half of the total supply of places.
Distribution of total places by municipality type

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

However, when considering the number of places per municipality, rural areas appear to have the fewest places per municipality: on average, 82 places per rural municipality, 141 per intermediate municipality, and 288 per urban municipality.

Supply by municipality type

To also capture the specific characteristics of the supply, we have two combined pieces of information: the number of places according to the operator's status and according to the type of municipality.

Distribution of places by operator status and municipality type

<figure><img src="image_4.png" /></figure>

This aligns with what we mentioned earlier: a two-speed liberalization.
Urban areas, with a large potential clientele (30% of the 75+) due to population density and accessibility, are more attractive for the development of these “social protection markets” (Gingrich, 2011).

We observe that in urban areas, the public sector is underrepresented compared to the national average, whereas this trend reverses in intermediate and rural territories.

In rural municipalities, more than 60% of the supply belongs to the public sector.

## Prices

When analyzing pricing, we observe higher average prices in urban areas and more moderate rates in intermediate and rural territories.

This suggests a potential relationship to explore: the inverse relationship between local territorial supply and price accessibility.

<table><thead><tr><th>Type of Municipality</th><th>Operator Status</th><th>For Profit</th><th>Non For Profit</th><th>Public</th><th>All</th></tr></thead><tbody><tr><td>Urban</td><td></td><td>113</td><td>77.9</td><td>70.5</td><td>89.7</td></tr><tr><td>Intermediate</td><td></td><td>99.5</td><td>72.2</td><td>65.0</td><td>76.5</td></tr><tr><td>Rural</td><td></td><td>93.0</td><td>68.9</td><td>64.0</td><td>70.2</td></tr></tbody></table>

We observe that the average cost of nursing home places varies considerably depending on the type of municipality and the operator's status. In urban areas, all types of operators charge higher fees, with an average accommodation fee of €113 for for-profit private operators, €77.9 for non-profit private operators, and €70.5 for public operators, resulting in an overall average of €89.7.

Intermediate municipalities show more moderate fees, while rural municipalities display the lowest prices, regardless of the operator's status.

Urban and rural areas thus differ in the structure of the offer, which includes this pricing component: although rural communes have fewer places per municipality — we mentioned an average of 82 places per rural commune — this limited offer is accompanied by, or offset by, this pricing stratification characteristic of the public sector.

To begin exploring this hypothesis, we tested the correlation between these two phenomena before moving on to more complex models. We obtained a value of -0.12. This indicates a negative correlation between distance and price; however, the magnitude of the correlation is not particularly high.
## The question of distances when there are no available places

The previous descriptions concerned municipalities that have nursing home places.

The picture becomes more complex when considering all municipalities in France.

In metropolitan France, there are 34,826 municipalities, of which 4,947 have nursing home places. In other words, 29,879 municipalities have no EHPAD places at all.

Among these 29,879 municipalities:

* 178 are urban (0.6%)

* 1,922 are intermediate (6.4%)

* 27,755 are rural (93%)

If we relate the stock of places to the area of each type of territory, we observe a density of 0.5 places per km² in rural areas, compared to 3.8 in intermediate areas and 17.7 in urban areas.

For these municipalities, I also calculated, for example, the minimum distance to the nearest municipality with available places, distinguishing between municipalities that border and those that do not border municipalities with available places.

<table><thead><tr><td>Type of Municipality</td><td>Distance with Neighboring effect (km)</td><td>Distance without Neighboring effect (km)</td><td>Total</td></tr></thead><tbody><tr><td>Urban</td><td>2.3</td><td>4.2</td><td>2.3</td></tr><tr><td>Intermediate</td><td>3.2</td><td>5.2</td><td>3.4</td></tr><tr><td>Rural</td><td>4.8</td><td>8.0</td><td>6.3</td></tr></tbody></table>

For example, living in a rural area without facilities translates into a distance almost three times greater to reach the nearest municipality with available nursing home places.

Moreover, although this distance might seem small, accessibility is often complicated by the need to rely on a car in rural areas due to the lack of public transport networks.
In conclusion...

This descriptive data table suggests the existence of an equipment imbalance between rural and urban areas, which deserves further investigation to better understand the specificity of these inequalities in relation to the potentially affected population.

Furthermore, when municipalities do not have available places, this raises the issue of residential mobility for these populations at the time of entry into a nursing home. Such mobility, driven by dependency and aging, often implies moving away from local support networks embedded in the municipality of residence. The likelihood of having to change municipality upon entering a nursing home is particularly high for rural residents.

Additionally, we observed a difference in access costs between urban and rural areas. This suggests that the further the distance to the nearest available places, the lower their price tends to be. In this sense, distance is somewhat offset by lower pricing, which is characteristic of an offer predominantly provided by the public sector.

Going forward, it will be important to test a spatial model to understand the effect of neighboring municipalities on the availability of places, also taking into account variables that describe the three zones: aging index, area, provision of doctors and nurses, number of jobs in the municipality, share of executives, and nearby place prices.