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Anales del Instituto de Act= uarios Españoles

 

4ª Época, Número 3= 1, Año 2025/19-33

DL M-3160-1961 – ISSN 0534-3232 – eISSN 2531-2308<= o:p>

DOI 10.26360/2025_02

 

RADIOGRAFÍA DEL SEGURO DE DECESOS EN ESPAÑA

AN OVERVIEW OF FUNERAL INSURANCE IN SPAIN

 =

= Josep Lledó

Universitat de València. Valencia, España.

ORCID: https://orcid.org/= 0000-0002-7475-8549=

jose= p.lledo@uv.es.

Director del Observatorio de Biometría Actuarial del Centro de Investigación Actuarial de España (CIAE).

(Autor para correspondencia/Corresponding author)

 

Priscila Espinosa

Universitat de València. Valencia, España.

ORCID: https://orcid.org/= 0000-0002-3870-6194=

priscila.espinosa@= uv.es

 

José M. Pavía

Universitat de València. Valencia, España.

ORCID: https://orcid.org/= 0000-0002-0129-726X=

pavia@uv.es= =

 <= /span>

Fecha de recepción/Date of = reception: 25/04/2025

Fecha de aceptación/Date of= acceptance: 28/07/2025

 

ABSTRACT=

 

Funeral insurance is an insurance p= roduct designed to cover the expenses and administrative procedures associated wit= h a person’s death. Its main purpose is to relieve the insured person’s family = of the financial and bureaucratic burdens arising from the funeral. Despite its widespread presence in the Spanish market—currently, more than 22 million people in Spain have coverage under this type of insurance—academic and scientific literature on funeral insurance remains surprisingly scarce. The= aim of this study is to help fill this gap by providing a comprehensive overview= of funeral insurance in Spain, based on the analysis of the sociodemographic profiles of policyholders, including their geographic distribution, income levels, and municipality sizes. The study examines a representative sample = of 2.1 million policies, using the insured individuals’ postal codes of reside= nce. The results reveal an uneven penetration across provinces, income groups, a= nd habitat sizes, with lower rates in urban areas and among higher-income individuals. However, education level, more than income, is the variable wi= th the greatest impact, showing a statistically significant interaction effect with income. The combination of a high level of education and high income reduces the likelihood of holding a funeral insurance policy.

Keywords: Postal code, Ecol= ogical analysis, Negative binomial, Income level, Habitat size, Distribution and market penetration.

RESUMEN

El seguro de dece= sos es un producto asegurador diseñado para cubrir los gastos y gestiones asociado= s al fallecimiento de una persona. Su finalidad principal es aliviar a los familiares de la persona asegurada de las cargas económicas y administrativ= as que se derivan del sepelio. A pesar de su extensa presencia en el mercado español —ac= tualmente en España hay más de 22 millones de personas con cobertura en este ramo—, la literatura académica y científica sobre este tipo de seguro es sorprendentemente limitada. El objetivo de este trabajo es contribuir a lle= nar este hueco realizando una radiografía del seguro de decesos en España a par= tir del análisis de los perfiles sociodemográficos de las personas aseguradas, = incluyendo su distribución geográfica, por niveles de renta o por tamaños de hábitat. = El estudio examina una muestra representativa de 2,1 millones de pólizas, basándose en= el código postal de residencia de las personas aseguradas. Los resultados mues= tran una penetración desigual por provincias, grupos de renta y tamaños de hábit= at. Siendo menores en las zonas urbanas y entre las rentas más elevadas. No obstante, el nivel educativo es, por encima de la renta, la variable con ma= yor impacto, mostrando un efecto de interacción estadísticamente significativo = con la renta. La combinación de un elevado nivel educativo y una renta elevada reducen la probabilidad de tener contratada una póliza de decesos.

Pa= labras clave: Código postal, Análisis ecológico, Binomial negativa, N= ivel de renta, Tamaño de hábitat, Distribución y penetración.<= /p>

1.   INTRODU= CCIÓN

El seguro de decesos es un producto asegurador diseñado para cubrir los gastos y gestion= es asociados al fallecimiento de una persona. Su finalidad principal es alivia= r a los familiares de la persona asegurada de las cargas económicas y administrativas que se derivan del sepelio, incluyendo servicios funerarios, traslados, asistencia legal y otros trámites relacionados. Al igual que en = el resto de productos de seguro, la responsabilidad financiera de los beneficiarios —que se activa cuando se produce el evento—= es transferida a la compañía aseguradora a cambio de una prima. Sin embargo, e= s un producto con características diferenciales. Su principal particularidad rad= ica en que el evento asegurado (el fallecimiento) es, a diferencia de lo que oc= urre en otros tipos de seguros, un hecho cierto; aunque se desconoce el momento = exacto en que ocurrirá. Esta característica implica, en muchos casos, una estructu= ra de primas que se extiende a lo largo de varias décadas.

Aunque hay antecedentes sobre seguros de decesos a lo largo de toda la historia, el seguro de decesos como lo conocemos en la actualidad se consid= era un invento español. Algunas teorías sitúan el nacimiento del seguro en Gali= cia entre finales del siglo XIX y principios del siglo XX, ofreciendo cobertura dentro de los gremios de canteros y pescadores. Su origen gallego viene ava= lado por el hecho de que dos de las principales compañías comercializadoras del seguro de decesos, Ocaso y Santa Lucía, nacen en Galicia con sólo dos años = de diferencia, 1920 y 1922 respectivamente (Cortés Martínez, 2004).=

El seguro de decesos es un seguro muy popular que se adapta a distintas capacidades económicas y necesidades, con opciones tanto básicas como completas. De hec= ho, el seguro de decesos es uno de los seguros más conocidos y con mayor penetración social en España, profundamente arraigado en la tradición de la sociedad española, con más de 22 millones de personas cubiertas (UNESPA, 2024). Este tipo de seguro destaca por ser un producto que, en muchos casos, se transmite de generación en generac= ión, lo que lo distingue claramente de otras modalidades de seguros. Su contratación suele ir ligada a una cultura del ahorro y a una visión tradicional de la previsión familiar, aunque en los últimos años ha evolucionado hacia formatos más flexibles, incluyendo coberturas adicionale= s y opciones de capital reembolsable.

A pesar de su extensa presencia en el mercado, la literatura científica sobre el seguro de decesos es escasa, posiblemente debido a su naturaleza híbrida —aunque se clasifica dentro de los seguros de no-vida, utiliza técn= icas actuariales propias del seguro de vida (Gil et al., 1999; Moreno et al., 20= 05)—, así como a su carácter predominantemente español. Estudios recientes, como = el de Rius y Torra (2022), excluyen expresamente este producto, y solo algunas excepciones, como los informes de UNESPA o el trabajo de Lledó et al. (2025= ), lo abordan. Existe, por tanto, una importante laguna en la literatura. Entre otras cuestiones, se desconoce cómo se distribuye el mercado de seguro de decesos en España desde diferentes perspectivas, incluyendo su distribución geográfica, por niveles de renta o por tamaño de hábitat. El presente traba= jo ofrece una radiografía detallada del seguro de decesos en España mediante el análi= sis de los perfiles sociodemográficos de los asegurados a partir de su código postal. Utilizando una muestra representativa de cuatro compañías nacionale= s, esta investigación contribuye a cubrir el vacío existente.

El estudio analiza tanto la distribución como la penetración del seguro= de decesos en función de la renta, el tamaño del hábitat y el nivel educativo,= a partir de la segmentación del conjunto de pólizas por deciles y mediante modelización. La distribución indica qué proporción del total de pólizas corresponde a cada nivel, mientras que la penetración (también denominada t= asa de incidencia) hace referencia al grado de aseguramiento dentro de cada com= binación de valores de las variables; es decir, a la proporción de personas asegurad= as respecto al total de personas que pertenecen a esa combinación.<= /span>

La distribu= ción se analiza a partir de los diferentes deciles de las variables sociodemográficas, mientras que la penetración o tasa de incidencia se estu= dia mediante la estimación de un modelo binomial negativo con efectos aleatorio= s. Nótese que cuando se utilizan deciles la penetración condicional relativa dentro de cada decil tiene la misma forma funcional que la de la distribución. El enfoque de la modelización permite evaluar = con mayor precisión la influencia de las características sociodemográficas del territorio del asegurado en la tasa de penetración del seguro, alcanzando resultados más robustos y representativos.

Para la realización de los análisis se han explotado diversas fuentes de información. Por un lado, se han empleado los microdatos de cuatro bases de datos proporcionadas por compañías aseguradoras que comercializan seguros de decesos en España, las cuales, en conjunto, representan más de 2,1 millones= de pólizas. La información, que fue facilitada de manera desinteresada por las propias entidades, hace referencia al estado de las carteras con fecha 31 de diciembre de 20= 24. Cada póliza incluye un identificador de georreferenciación (el código postal), q= ue permite vincular la persona asegurada con su zona de residencia. Por otro l= ado, se han utilizado datos del Instituto Nacional de Estadística (en adelante, = INE) que permiten contextualizar la información de las carteras con variables so= ciodemográficas medidas a nivel de sección censal, como son el nivel de renta o el nivel educativo, y el tamaño del hábitat.=

Este trabajo busca contribuir a mejorar nuestra compresión sobre qué factores afectan a = la contratación de seguros de decesos, ofreciendo resultados relevantes para nuestra industria aseguradora. Al entender mejor las dinámicas detrás de la adquisición de este tipo de pólizas, se pueden desarrollar estrategias más efectivas para diseñar nuevas modalidades adaptadas a distintas necesidades= o identificar áreas geográficas (o grupos sociodemográficos) concretos donde = la presencia del ramo tiene más recorrido. Todo ello con el objetivo de amplia= r la cobertura y mejorar la protección financiera de las familias.

El resto del artículo se organiza como sigue. En la sección segunda se detallan las fuen= tes de información utilizadas y la metodología aplicada. Se describe el proceso secuencial seguido para la recopilación y depuración de los datos. En la sección tercera se especifica la construcción de los deciles sociodemográfi= cos y la modelización de la penetración del seguro de decesos en función de distintas variables. Además, se justifica la elección del enfoque metodológ= ico y se explican las técnicas empleadas para garantizar la robustez de los resultados. En la sección cuarta se presentan los principales resultados del estudio, analizándose los patrones de distribución del seguro de decesos. El artículo se cierra con una última sección donde se comentan las principales conclusiones del trabajo.

2.   Datos <= o:p>

2.1.=         Caracte= rísticas de las carteras de seguros

La revolución digital ha transformado profundamente la forma en que se recopilan, almacenan, transmiten y procesan dato= s en distintos ámbitos de la sociedad (Ruggles, 2014; Gutma= nn et al., 2018). Hoy en día, los ordenadores personales permiten manejar gran= des volúmenes de información en tiempos reducidos, lo que facilita el análisis = de fenómenos complejos con un nivel de detalle impensable hace pocas décadas. = En este estudio, los autores han tenido acceso a una muestra representativa del ramo de decesos en España compuesta por aproximadamente un 10 % del t= otal de pólizas del mercado —actualmente existen en España más de 22 millones de contratos en vigor.

La muestra contiene más de 2,1 millones de pólizas anonimizadas, proporcionadas de forma desinteresada por cuatro compañías aseguradoras con= el único fin de contribuir al desarrollo científico. Este conjunto de datos constituye un recurso especialmente valioso para analizar la situación actu= al del seguro de decesos en España. El periodo de referencia cubre un único ejercicio, el año 2024, y la fecha de extracción de la información fue el 3= 1 de diciembre de dicho año.

Las cuatro bases de datos se han integrado en un único conjunto, garantizando en todo momento el anonimato de los registros y eliminando cualquier posibilidad de identificación individual. Una de las principales fortalezas de la base de datos es la inclusión del código postal de residen= cia del asegurado, lo que permite (i) asociar a cada póliza variables socioeconómicas del área de residencia del asegurado y (ii) segmentar geográficamente la cartera en áreas más pequeñas, realizando un análisis ecológico. Disponer de esta variable georreferenciada permite vinc= ular cada registro individual con variables sociodemográficas disponibles a nive= l de sección censal o municipal, posibilitando análisis espaciales y grupales. E= sta granularidad territorial amplía el alcance analítico, permitiendo la identificación de patrones espaciales y sociodemográficos de contratación y= el estudio de la heterogeneidad en el comportamiento asegurador en función del entorno geográfico y el contexto socioeconómico.

España cuenta con aproximadamente 11.000 códigos postales, etiquetados = por un código numérico de cinco dígitos, donde los dos primeros dígitos (del 01= al 52) informan sobre la provincia o ciudad autónoma donde se ubica la zona geográfica que identifica el código postal. Esta variable, ampliamente utilizada para optimizar la distribución del correo y la paquetería en sect= ores como la logística y el transporte, resulta especialmente valiosa en estudios territoriales.

Para caracterizar el perfil de quienes contratan un seguro de decesos y elaborar una radiografía de este mercado a nivel nacional, complementamos la información disponible en las carteras con el nivel de renta, el tamaño del hábitat y el nivel educativo, medidas a nivel agregado. Estas variables han sido seleccionadas debido a su influencia en las decisiones de contratación= de seguros, ya que reflejan tanto la capacidad económica como el contexto socioespacial en el que residen los asegurados. Estas variables ya han sido estudiadas en el contexto asegurador, mostrando, por ejemplo, la incidencia= que las mismas tienen en seguros de vida (Pavía et al., 2025).

 

2.2.=         Nivel de renta

El nivel de renta se ha obtenido del Atlas de Distribución de Renta de = los Hogares del INE, utilizando el último año disponible (2022). Se ha tomado como referencia la renta media por unidad de consumo (INE, 2024), un indicador ajustado por tamaño y composición del hogar que permite realizar comparacio= nes más adecuadas entre individuos que la simple renta per cápita, al tener en cuenta las economías de escala que aparecen en los hogares. La unidad territorial más pequeña en esta variable es la sección censal, y su unidad = de medida son miles de euros. En los casos en los que no se disponía de valores específicos de renta para una sección censal, se imputó el valor correspondiente del distrito, municipio o, en última instancia, de la provincia.

Los datos disponibles de renta a nivel de sección censal fueron transferidos a nivel de código postal utilizando la función sc2cp disponibl= e en el paquete de R sc2sc (Pérez y Pavía, 2025) para datos de tipo “average”. Este proceso permitió asignar a cada código postal y, por extensión, a cada póliza de nuestra base de datos un nivel de= renta asociado.

2.3.=         Nivel educativo

La dimensión educativa se ha obtenido del Censo Anual de Población (Educación y relación con la actividad) del INE, tomando el último año disponible (2022). Este censo proporciona información sobre la distribución= de la población según su nivel de formación a nivel de sección censal.

Para estandarizar la clasificación educativa, utilizamos la Internation= al Standard Classification of= Education (ISCED), un marco global que permite la comparabilidad entre sistemas educativos mediante la organización de los niveles formativos según su duración y complejidad. En la literatura, exist= en diversas aplicaciones de esta clasificación. Por ejemplo, Pérez-Miguel y Trias-Llinós (2025) analizan las desigualdades en mortalidad por enfermedades cardiovasculares con una clasificación en cuatro niveles educativos: Bajo (ISCED 0-1), Medio-Bajo (ISCED 2), Medio-Alto (ISC= ED 3-4) y Alto (ISCED >5). Enroth et al., (2022= ), Luy et al., (2019) y Németh et al., (2021) adoptan = un enfoque similar, agrupando la educación en tres categorías: Baja (ISCED 0-2= ), Media (ISCED 3-4) y Alta (ISCED 5-8), aplicándolas al estudio de la mortali= dad y la esperanza de vida en distintos países.

En nuestro estudio, utilizamos la Clasificac= ión Nacional de Educación (CNED, 2014), adaptada a los cambios del sistema educativo español y alineada con la ISCED. Establecemos tres niveles: Estud= ios primarios (educación primaria e inferior[1]), Es= tudios secundarios (primera etapa de educación secundaria, segunda etapa de educac= ión secundaria y educación postsecundaria no superior) y Estudios terciarios (e= ducación superior, que abarca estudios universitarios de grado, máster y doctorado). Esta simplificación facilita el análisis y mantiene la coherencia con estándares internacionales. Cabe señalar que cuando se hace referencia al n= ivel educativo de una persona, se considera únicamente el nivel máximo alcanzado= . Es decir, aunque todo individuo haya superado niveles previos, a efectos analíticos solo se toma en cuenta el nivel educativo más alto completado. A cada uno de estos niveles se le asigna un número —1 para estudios primarios= , 2 para secundarios y 3 para terciarios— y se construye un indicador de nivel educativo medio del código postal ( ) como media ponderada de la población residente en el código postal con cada nivel de estudios.

Para poder calcular los indicadores <= !--[if gte msEquation 12]>IEc  necesario primero transferir la información de sección censal a código postal. De manera similar a como se realizó en el caso de las rentas medias, esta tarea se realizó mediante el paquete sc2sc (Pérez y Pavía, 2025), pero en esta ocasión utilizando la opc= ión para datos de tipo “counts”. Una vez obtenido el número de individuos en cada nivel de estudios en cada código postal, el ín= dice se ha calculado como: , donde  es el porcentaje de individuos con estudios primarios en el código postal ,  es el porcentaje de individuos con estudios secundarios en el código postal , y  es el porcentaje de individuos con estudios terciarios o superiores en el código postal . Este ín= dice proporciona una medida representativa y comparable a nivel nacional del niv= el educativo en cada código postal.

2.4.=         Tamaño = de hábitat

El tamaño del hábitat se ha determinado a partir del Censo Anual de Población del INE, correspondiente al 2022. Aunque existen datos más recien= tes, como los de 2024, se ha optado por utilizar el año 2022 para garantizar la coherencia temporal con el resto de las variables sociodemográficas, cuya última actualización también corresponde al año 2022. Se ha considerado la población total de los más de 8.000 municipios de España, lo que permite examinar en qué medida la contratación del seguro varía en función del tama= ño del hábitat del lugar de residencia. En este caso, la unidad territorial de referencia es el municipio, y la variable se mide en términos de población (número de personas).

Con el fin de asignar tamaños municipales a códigos postales, y por extensión a pólizas, hemos (i) empleado las cartografías disponibles por mu= nicipios (derivada a partir de datos oficiales del INE) y por códigos postales —basa= da en Goerlich (2022)—, (ii) calculado la intersección entre ambas cartografías y (iii= ) seguido una metodología inspirada en Pérez y Pavía (2024) para distribuir las póliz= as con un mismo código postal entre tamaños de hábitat. Las intersecciones per= miten identificar si un código postal intersecta con = uno o diversos municipios y en qué medida, permitiendo repartir las pólizas de ca= da código postal entre tamaños de hábitat de manera proporcional al porcentaje= de intersección.

La expresión matemática utilizada para el cálculo de las áreas de intersección ha sido , donde <= !--[if gte msEquation 12]>Am  es la superficie del municipio m ,  es la superficie del código postal , y  es el área del código postal  que cae dentro del municipio . En los ca= sos en los que un código postal está completamente integrado dentro de un municipi= o, la ratio es igual a 1. En aquellos casos en los que un código postal se comparte entre varios municipios, la ratio disminuye. Este proceso de intersección garantiza la trazabilidad espacial entre municipios y códigos postales, como se ejemplifica en la Tabla 1 para los códigos postales 01013= y 01001, que intersectan con los municipios de Vitoria-Gasteiz y Arratzua-Ubarrundia. Por ejem= plo, si tenemos 100 pólizas en el CCPP 01013, aproximadamente 3,99 se asignarán = al tamaño de hábitat que corresponda al municipio 01008 y 96,01 al municipio 01059, que se sumarán a las pólizas que se asignen provenientes de otros códigos postales, si es el caso, con los que intersect= en esos municipios.

CCPP

Code_Prov

Code_Mun

Nom_Municipio

Ratio

01013

01

008

Arratzua-Ubarrundia=

14.536.801,3

579.910,9

0,0398926

059

Vitoria-Gasteiz

13.956.891,3

0,9601074

01001

01

059

Vitoria-Gasteiz

700.376,4

700.376,4

1

Tabla 1<= !--[if supportFields]>: Intersección entre municipios y códigos postales. Fuente: Elaboración propia.

 

 

2.5.=         Cartera= s de seguros integradas con variables socioeconómicas

No se dispone de datos socioeconómicos individualizados de las personas aseguradas, sino que a cada póliza se le asignan estas características en función del código postal de residencia de la persona asegurada. Por tanto,= el análisis parte del supuesto de la existencia de cierta homogeneidad interna, dentro de las secciones censales, y heterogeneidad externa, entre secciones= y municipios. Un supuesto coherente con los últimos estudios (e.g., Mazorra Rodríguez, 2024), que apuntan hacia una segregación creciente de la población española, con desigualdades sociales y económicas aumentando entre áreas. Esta estrategia permite aproximarse al perfil medio de los asegurados según su contexto geográfico, aunque no perm= ite capturar posibles diferencias individuales dentro de cada sección censal o municipio.

Antes de integrar las variables sociodemográficas en las carteras de seguros, fue necesario verificar la validez de los códigos postales disponi= bles en la base de datos. Dado que esta información suele introducirse manualmen= te no es descartable que puedan producirse errores derivados del proceso de recopilación, procesamiento o almacenamiento. Entre los más frecuentes se encuentran datos faltantes o inconsistentes. Se parte del supuesto de que l= as dos primeras cifras del código postal, correspondientes a la provincia, est= án correctamente consignadas; en caso contrario, los registros afectados se excluyen del análisis. Cuando se identificaron códigos postales no válidos,= se procedió a su corrección redistribuyendo las pólizas implicadas de forma proporcional a la población residente en cada código postal dentro de la provincia correspondiente. Este proceso afectó a menos del 9% de las póliza= s de la base de datos, concretamente alrededor de 200.000 pólizas. Los resultados obtenidos son cualitativamente similares cuando estas pólizas son excluidas= de los análisis.

<= span style=3D'mso-ansi-language:ES'>La Tabla 2 recoge, a modo ilustrativo, varios códigos postales junto con las variables sociodemográficas asociadas. Se incorpora también el código del municipio ya que, como se detalla en la sig= uiente sección, este dato es utilizado para crear las particiones por tamaño de hábitat.

 

CodMun=

CCPP<= /p> Renta=

T.Hábitat

IE

01059=

27028=

36014=

Tabla 2: Ejemplo de datos socioeconómicos por código postal.=

Fuente: elaboración propia

3. METODOLOGÍA

3.1&= nbsp;       Deciles= socioeconómicos y demográficos.

Para analizar la distribución del seguro de decesos resulta necesario segmentar la población en grupos con un cierto grado de homogeneidad intern= a. Con este objetivo, se ha optado por estratificar las variables objeto de estudio (renta, nivel educativo y tamaño de hábitat) en deciles ponderados = por población. Esto significa que cada decil contiene aproximadamente la misma proporción de la población total, el 10%, lo que permite comparaciones más equilibradas entre grupos (y que la distribución por deciles informe asimis= mo de la penetración relativa del seguro en cada grupo). Aplicar este criterio a = todas las variables garantiza una base común para el análisis sociodemográfico. E= ste enfoque contribuye a reducir sesgos, mejorar la representatividad y facilit= ar comparaciones más ajustadas entre grupos con estructuras socioeconómicas distintas, independientemente del tamaño de la población en cada código pos= tal particular.

Los deciles de renta y nivel educativo se calculan ordenando los códigos postales de menor a mayor en función de los valores en estas variables y calculando los deciles ponderando por la población del código postal. Para = el tamaño del hábitat se han ordenado los municipios según su tamaño poblacion= al y agrupado municipios de menor a mayor creando grupos conteniendo el 10% de la población. A diferencia de las variables anteriores, que se asignan a nivel= de código postal, el tamaño del hábitat se vincula al municipio de residencia = del asegurado. Aunque el número de municipios en cada grupo puede variar, esta aproximación permite comparar patrones de contratación del seguro de decesos entre localidades con distintos tamaños poblacionales, desde núcleos urbanos densamente poblados hasta pequeños municipios rurales.

Una vez definidos los deciles, se asigna a cada registro de la base de datos el decil correspondiente según su código postal o municipio de residencia. Esta asignación permite analizar cómo se distribuye el seguro de decesos en función de las diferentes variables. Por ejemplo, si se observa = una mayor concentración de pólizas en deciles superiores de la renta, esto indi= cará un comportamiento asimétrico de la variable hacia la izquierda, asociado con una mayor presencia relativa de (o una mayor penetración entre) personas con capacidad económica elevada. En cambio, una mayor concentración de pólizas = en los deciles inferiores de renta reflejará una distribución asimétrica posit= iva, vinculada a una mayor presencia relativa de (o una mayor penetración entre)= personas con menor nivel adquisitivo.

3.2&= nbsp;       Modeliz= ación de la penetración del seguro de decesos

Para evaluar la influencia de las variables socioeconómicas en la concentración de pólizas por habitante hemos aplicado a nivel de código pos= tal un modelo de regresión binomial negativo (Booth et= al., 2003; Hilbe, 2011) con efectos aleatorios por provinc= ia, donde la variable respuesta es el número esperado de pólizas de decesos contratadas por código postal; ver ecuación (1). La elección del modelo no = es casual, por un lado, usar una distribución binomial negativa como distribuc= ión de probabilidad para la variable respuesta permite capturar la sobredispersión que presenta esta comparada con si se emplea un modelo log-lineal clásico, con respuesta Poisson. Por otro lado, = dado la enorme cantidad de niveles implicados en nuestro estudio, el trabajar con efectos aleatorios permite una estructura suficientemente parsimoniosa para= ser estimada. Además, para eliminar el efecto del diferente número de residentes por código postal el modelo incorpora esta variable mediante un término off= set. Esto permite interpretar los efectos de las covariables como factores asoci= ados a la tasa de contratación por habitante, ajustando por las diferencias en población entre códigos postales.

Matemáticamente, denotando por  el código postal y por  la provincia donde se encuentra el código postal, tenemos que la función de densidad P= Ycp=3Dy=3DΓ(y+θ)Γ(y+1)Γ(θ)θ+μcpθμcpθ+μcpy , donde <= !--[if gte msEquation 12]>Ycp  c  de la provincia ,  es la función gamma,  es el número esperado de pólizas y =  es el parámetro de dispersión que captur= a la variabilidad adicional. Dado que el número esperado depende del tamaño del código postal, el número esperado de pólizas en cada código postal será igu= al al tamaño del código postal, , por la propens= ión a contratar (tasa de incidencia= ) como función de las características de las personas residentes, es decir:  —donde  IEcp  representan los niveles medios de ingres= os y educativos de los residentes en el código postal—, con  y efecto aleatorio , que permite que cada provincia tenga un nivel de referencia específico de contratación de seguros, capturando las diferencias sistemáticas entre provincias.

Este modelo es adecuado cuando existe sobredispers= ión en los datos y, además permite incorporar efectos aleatorios para capturar variabilidad no explicada. Así, podemos obtener estimaciones más precisas y robustas sobre la relación entre la renta y nivel educativo con la tasa de = incidencia o penetración del seguro de decesos.

Pcp  hace referencia a la población existente= en el código postal y es introducida en el modelo como offset con el fin de modelar las relaciones entre la variable respuesta y los predictores sin qu= e el tamaño de la población distorsione los posibles resultados o su interpretac= ión. Con el offset de población, el modelo predice la tasa de incidencia o penetración, , entendiendo és= ta como la proporción de pólizas en una determinada área geográfica dada sus características en términos de renta per cápita, composición educativa y provincia; ver ecuación (4). Con todo lo anterior, el modelo binomial negat= ivo de efectos aleatorios se puede expresar mediante:

         =                        (1)

                                             (2)

                                                     =   (3)

Donde β0=  es el intercepto del modelo,  β2=  son los coeficientes que miden el impact= o de las variables  y  y  es el offset del modelo; es decir= , reordenando la ecuación (1), la expresión matemática quedaría como sigue:

                                        (4)

En la aplicación empírica hemos estandarizado las variables independientes para una mejor interpretación de los coeficientes e incluido un efecto interacción entre r= enta y nivel de estudios para captur= ar dependencias entre ambas variables que no pueden explicarse adecuadamente mediante efectos aditivos.

4. RESULTADOS

El enfoque detal= lado en las secciones anteriores permitió la construcción de una base de datos enriquecida con las variables socioeconómicas y demográficos correspondient= es a los deciles de nivel de renta, nivel educativo y tamaño del hábitat, además= de los valores asignados a cada póliza correspondientes a la renta media y niv= el educativo promedio de la sección de residencia de la persona asegurada. En = esta sección se presentan los resultados derivados de los análisis implementados= con el objetivo de proporcionar una radiografía del mercado del seguro de deces= os en España.

4.1.=       Distrib= ución espacial del seguro de decesos

Con el objetivo = de analizar la distribución territorial del seguro de decesos, preservando la anonimidad de la información, hemos construido un indicador de cobertura de decesos a nivel de provincia que permite comparar los niveles de penetración entre provincias. Para ello, hemos calculado la ratio entre el número de pólizas y la población total en cada provincia y estandarizado el valor res= ultante, restando la media y dividiendo por la desviación típica, empleando la distribución del conjunto de valores provinciales. El indicador obtenido, q= ue varía entre -1,5 y 2,5, sirve para situar la densidad de pólizas en una com= paración entre provincias. La Figura 1 muestra los resultados obtenidos, donde valor= es más elevados indican un nivel relativo de penetración mayor.

Figura 1: Grado de penetración del seguro de decesos entre provincias.

Fuente: elaboración propia

 

La Figura 1 muestra diferencias claras en el indicador de cobertura de decesos entre provincias. Se observan valores cercanos a 0, lo que indicaría provincias con índice de cobertura similar a la media nacional, estas tomar= ían tonalidades intermedias. Los valores negativos (hasta -1,5) indican provinc= ias con menos pólizas por habitante que la media nacional. Esto refleja la baja cobertura de la provincia en comparación con el resto del país, es decir, h= ay menos pólizas en proporción a su población, como es el caso de Galicia, el noroeste de Castilla y León y algunas provincias del este de la península. = Por otro lado, valores positivos (hasta 2,5) indicaría que hay provincias con m= ás pólizas por habitante que la media nacional, como es el caso de Castilla-La Mancha. Cuanto mayor es la proporción de pólizas en relación con la poblaci= ón mayor es el indicador de cobertura provincial por habitante.

4.2&= nbsp;       Distrib= uciones por niveles de renta, educativos y de tamaño de hábitat

En primer lugar, para tener una idea clara de las características socioeconómicas y demográf= icas de las pólizas incluidas en cada decil, la Figura 2 muestra, en el panel superior izquierdo, los extremos (mínimos y máximos) de las variables analizadas que definen los intervalos de cada uno de los deciles. Obviament= e, los niveles más bajos de las variables corresponden a los primeros deciles y los más elevados a los últimos deciles. Respecto a la renta, se observa que= , a excepción de los deciles extremos, el rango entre las rentas mínimas y máxi= mas en cada decil se mantiene cercano a los mil euros, aunque las diferencias e= ntre ambos extremos tienden a aumentar en los deciles superiores, especialmente = en el último, lo que refleja una desigualdad creciente en la distribución de la renta.

En relación con e= l índice de nivel educativo (columnas  y ), donde puntuaciones más bajas reflejan poblaciones mayoritariamente menos formada,= y valores más altos indican una mayor proporción de personas con estudios superiores. Se observa que en el primer decil los valores se sitúan por deb= ajo de valor de 2, lo que sugiere una predominancia de niveles educativos básic= os, principalmente educación primaria. En contraste, en el décimo decil, el índ= ice supera los 2,5, lo que indica una presencia mayoritaria de población con formación superior. Los deciles intermedios se asocian con niveles educativ= os medios, sin alcanzar el umbral de 2,5 hasta el último decil.

Por último, respe= cto el tamaño de hábitat, los grupos presentan una estructura heterogénea, con desigualdades significativas importantes entre los deciles más bajos (1-3) y los deciles más altos (9 y 10).

Figura 2: Distribución del seguro de decesos en función de la renta, hábitat y estudios. Fuente: elaboración pro= pia.

El panel superio= r derecha y los paneles inferiores de la Figura 2 muestran las distribuciones del seg= uro de decesos por deciles en función de las tres variables analizadas. Asimism= o, dado que cada grupo contiene un porcentaje de población similar, las gráfic= as también informan de las diferencias de penetración entre grupos. Obviamente= los porcentajes serán diferentes, pues en media alrededor del 45% de la poblaci= ón residente en España tiene contratado un seguro de decesos.

El panel superio= r derecho ilustra la distribución del seguro de decesos según deciles de renta. Se observa una mayor concentración de pólizas en los deciles intermedios (4 a = 6), donde los valores superan el 10%, lo que también indica que es en este segm= ento se contrata con mayor frecuencia este tipo de seguro. En contraste, el últi= mo decil, correspondiente a los niveles de renta más altos, presenta la menor proporción de contratación, lo que sugiere una menor predisposición entre l= os hogares con mayores ingresos. Esta pauta podría explicarse por una percepci= ón diferenciada de las necesidades de planificación financiera: mientras que l= os hogares con rentas medias y medias-bajas recurren al seguro de decesos como= un instrumento accesible de previsión, aquellos con rentas medias-altas podrían optar por otras alternativas, como productos de ahorro o inversiones personalizadas.

En relación con el tamaño del hábitat (panel inferior izquierdo, Figura 2), se observa una mayor concentración de pólizas en municipios de tamaño intermed= io y grande, aunque no en los más poblados. Los deciles medios y altos, en particular el decil 5, 7 y 9, son los grupos con un mayor porcentaje de pól= izas y son también los que presentan los mayores porcentajes de contratación. En contraste, la menor incidencia se da en los extremos, especialmente en el ú= ltimo decil, que agrupa los hábitats de mayor tamaño (las ciudades de Madrid y Barcelona) y registra la cifra más baja de contratación, junto al decil 6, = que agrupa básicamente a poblaciones entre 60.000 y 100.000 habitantes. Estas p= autas sugieren que, en entornos intermedios y grandes, aunque no metropolitanos, = la demanda del seguro de decesos podría estar asociada a una mayor presencia de oferta aseguradora o una mayor concienciación sobre su utilidad. Por el contrario, en los hábitats más grandes y metropolitanos, se observa una men= or contratación, que podría explicarse por varios factores como el elevado pre= cio de la vivienda, que actúa como filtro socioeconómico, concentrando en estos entornos a hogares con mayor poder adquisitivo, donde la propensión a contr= atar este tipo de seguros es menor. Así, mientras que en hábitats intermedios y grandes (pero no muy grandes) se observan niveles más altos de contratación= , en los entornos metropolitanos esta disminuye notablemente.<= /p>

Por último, en lo que respecta al nivel educativo, la distribución de pólizas se muestra relativamente homogénea en la mayoría de los deciles, excepto en los deciles más extremos, especialmente en los niveles educativos más altos, y = con mayor intensidad en el último decil. Este patrón muestra un menor porcentaj= e de pólizas en este grupo y sugiere que las personas con mayor nivel de formaci= ón presentan una menor propensión a contratar este tipo de seguros. Este comportamiento puede estar relacionado con distintos factores, como un mayor nivel de renta (que permite acceder a otras formas de planificación financi= era), una menor percepción de necesidad o un conocimiento más amplio sobre alternativas al seguro de decesos.

4.3&= nbsp;       Factore= s que influyen en la tasa de incidencia del seguro de decesos

Una vez analizada la distribución de las pólizas según los deciles sociodemográficos, el análisis se orienta hacia el estudio de los factores = que inciden en la penetración o tasa de incidencia del seguro de decesos en la población. Para ello, empleamos el modelo binomial negativo detallado previamente. Este modelo incorpora un offset que tiene en cue= nta el número de residentes en cada código postal y contempla efectos aleatorios a nivel provincial. Los resultados= de la modelización se presentan en la Tabla 3.

 

Variables

Coeficientes

Error estimación

p-valor

Efectos fijos

Intercepto

-3.18

0.06

<0.001***

Renta

-0.02

0.03

0.52

Índice educativo

0.11

0.02

<0.001***

Interacción:

-0.06

0.01

<0.001***

Efectos aleatorios=

Intercepto 3: Binomial negativo con efecto aleatorio provincial.

Fuente: elaboración propia.

 

La Tabla 3 muest= ra que una vez incorporada el nivel educativo y su interacción con la renta, la variable renta no aparece como estadísticamente significativa para la prope= nsión a contratar un seguro de decesos (p-valor =3D 0.52). Su efecto, que aparece= como significativo cuando el nivel educativo no es considerado queda subsumido p= or este último cuando está presente. En el lenguaje de relaciones causales de Judea Pearl (Pearl, 2009) la relación entre la tasa de incidencia y la renta sólo aparece mediada y en interacción con el nivel educativo. =

De hecho, dado q= ue (i) el índice educativo (  y su interacción con la renta (I·IE)  sí aparecen como significativos (p-valor= < 0.001), con signos positivos y negativos, respectivamente, y (ii) trabajamos con valores tipificados de las variabl= es, los resultados del modelo se interpretan cómo que: en presencia de la interacción entre  e , el coeficiente positivo de  indica que su efecto sobre la tasa de incidencia es significativo cuando , es decir, cuan= do  toma su valor medio, mientras que el coeficiente negativo de la interacción sugiere que dicho efecto disminuye a medida que aumenta . Por otro lado,= la falta de significación del coeficiente de  refleja que, cuando , es decir, cuan= do toma su valor medio,  no presenta un efecto lineal significati= vo sobre la tasa de incidencia, aunque su efecto sí varía en función del valor= de . Esto implica q= ue, en la renta media, la probabilidad de contratar este seguro crece con el nivel educativo. En términos cuantitativos, un aumento unitario en el nivel educa= tivo, manteniendo la renta en su nivel medio, incrementa la tasa de incidencia en= un 11.6% .

Asimismo, dado q= ue la relación entre la renta y la educación no es aditiva con un efecto interacción significativo (p-valor < 0.001), se tiene que el impacto de = la renta sobre la tasa de incidencia varía en función del nivel educativo. En áreas = de menor renta, un nivel educativo relativamente mayor incrementa significativamente la tasa de incidencia de estos seguros. En cambio, en ár= eas de renta más alta, este efecto se reduce. En términos de cambios porcentual= es, un incremento en la interacción de ambas variables disminuye la tasa de incidencia en un 5.8% . Para ilustrar = mejor el efecto conjunto de renta y nivel educativo sobre la tasa de incidencia d= el seguro de decesos, la Figura 3 presenta cómo varía la relación entre ambas variables según distintos niveles de formación y renta. Se observa que, en contextos con niveles educativos relativamente bajos, el efecto de la renta sobre la incidencia es prácticamente nulo o incluso ligeramente positivo, lo que sugiere una influencia limitada de la renta en la contratación del segu= ro. Sin embargo, a medida que aumenta el nivel educativo, la pendiente asociada= a la renta se vuelve negativa, indicando que, en poblaciones más formadas, un mayor nivel de renta se asocia con una menor penetración del seguro de dece= sos. Esta tendencia se acentúa en los niveles educativos más altos, donde además= se incrementa la variabilidad de la estimación. Este aumento en la dispersión puede deberse a una menor densidad de pólizas en códigos postales con alta formación académica, lo que introduce mayor incertidumbre en las estimacion= es.

Figura= 3: Efecto interacción I x IE. Fuente: elaboración propia

Los resultados d= el modelo presentados en la Tabla 3 evidencian también la existencia de diferencias territoriales en la contratación del seguro de decesos. En particular, los efectos aleatorios a nivel provincial indican que cada provincia presenta una tasa base distinta, lo que sugiere la presencia de factores no observados que influyen en la demanda de este tipo de seguros a escala territorial. La varianza del efecto aleatorio es de 0,21, con una desviación estándar de 0,46, lo que refleja una heterogeneidad considerable entre provincias. El parámetro de dispersión estimado (0,95) confirma la presencia de sobredispersión en los datos, es d= ecir, la varianza observada es mayor que la esperada bajo un modelo de Poisson. E= sto justifica el uso del modelo binomial negativo, que permite capturar mejor e= sta variabilidad.

5. CONCLUSIONES<= /h2>

Este estudio realiza una caracterización sociodemográfica del seguro de decesos en España a partir de una muestra representativa de pólizas contrat= adas (2,1 millones de pólizas), ofreciendo una aproximación robusta, aunque no exhaustiva, del mercado de este ramo del seguro. A partir del análisis realizado, se constata que, a pesar de su enorme penetración entre la pobla= ción española, el seguro de decesos mantiene una presencia con notables diferenc= ias tanto territoriales como sociales.

Desde la perspectiva del perfil de los asegurados, se identifican patro= nes claros en función de variables sociodemográficas. En cuanto a la renta, se observa que la contratación es más común entre los deciles medio-bajos, lo = que sugiere que estos grupos perciben este seguro como una herramienta útil para evitar cargas económicas inesperadas a sus familias. En contraste, los hoga= res con mayores ingresos muestran menor propensión a su contratación, probablem= ente porque no consideran necesario cubrir este tipo de gastos mediante un seguro específico.

En relación con el nivel educativo, los resultados reflejan un comportamiento similar al observado con la renta: a mayor formación, menor contratación. Esto sugiere que tanto la renta como el nivel educativo están estrechamente relacionados con la percepción de necesidad y utilidad de este tipo de seguros, además de ser variables que muestran una alta correlación entre ellas.

En relación con el tamaño del hábitat, la mayor concentración de póliza= s se observa en municipios de tamaño intermedio y grande, pero, en general, fuer= a de las grandes áreas metropolitanas. Esta distribución podría explicarse por u= na mayor presencia de oferta aseguradora o una mayor concienciación en estos entornos. Por el contrario, en las zonas metropolitanas la contratación es menor, quizás debido a la menor presencia relativa de oficinas físicas, lim= itada por los elevados costes del alquiler, y/o a la mayor presencia de perfiles socioeconómico y educativos más altos, que tiende a recurrir con menor frecuencia a este tipo de seguros.

Desde el punto de vista de la modelización estadística para el estudio = de la penetración, uno de los resultados más relevantes es la interacción entre renta y nivel educativo. El modelo binomial negativo con efectos aleatorios muestra que, cuando ambos factores alcanzan niveles altos, la probabilidad = de contratación disminuye de forma clara. Esta interacción negativa indica que= no es solo la renta o la educación por separado lo que influye, sino su combinación la que modifica significativamente la propensión a adquirir el producto.

6. AGRADECIMIENTOS

Agradecemos los datos suministra= dos a las cuatro compañías, sin ellos esta investigación no habría sido posible. Queremos agradecer asimismo el aval de la Generalitat Valenciana (Conseller= ia de Educación, Cultura, Universidades y Empleo; proyectos CIGE/2023/7 y CIAC= O/2023/031) y al Ministerio de Ciencia e Innovación (proyecto PID2021-128228NB-I00) su financiación. Por último, agradecemos los comentarios recibidos durante el proceso de evaluación. Las sugerencias de uno de los evaluadores han contribuido a mejorar la presentación del artículo.

 

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[1] Esta categoría engloba a personas analfabetas, estudi= os primarios incompletos y estudios primarios completos.

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J. Lledó, P. Espinosa, J.M. = Pavía

Radiografía del Seguro de de= cesos en España

14

9<= /span>

                                  4ª Época, Número 31, Año 2025/19-33

                     =                                                           DL M-3160-1961 – ISSN 0534-3= 232 – eISSN 2531-2308

DOI 10.26360/2025_02

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