Spatial regression model of households: a case of the Czech Republic

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F22%3A10454468" target="_blank" >RIV/00216208:11310/22:10454468 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dW_Y.BRVaN" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=dW_Y.BRVaN</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4000/eps.12307" target="_blank" >10.4000/eps.12307</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Spatial regression model of households: a case of the Czech Republic

Popis výsledku v původním jazyce

In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as well as a reduction of family numbers in general as a result of the low fertility rate and the postponement of childbearing in Europe. On the other hand, there has been a significant increase in the proportion of one-person households and families without children among people at an older age because of population ageing. The research question is to what extent these changes in the structure of households are spatially homogeneous and whether the proportion of households of a given type in a given territory can be explained by other demographic variables such as age, education, marital status or economic activity. The above-mentioned principles can be demonstrated by the example of data for households, whose detection and subsequent analysis is an integral part of population censuses. To solve this problem, it is possible to use spatial data analysis methods, which can be defined as a quantitative data analysis, in which the explanation is dependent on explicit spatial variables when predicting the investigated phenomenon based on spatial autocorrelation. The assumption of spatial regression is the existence of autocorrelation. The results of both Moran&apos;s I and Geary&apos;s C show that the autocorrelation for both types of households was found to be statistically significant and increases as the distance between adjacent elements (i.e. municipalities) decreases. Age is an important factor affecting the structure of households. The results for both types of households show that the age groups with the greatest influence on the creation of one-person households or one-family households can also be used to create a spatial model. A similar claim applies to the average age. Education showed that the share of persons with primary education has no influence on spatial regression, unlike the share of persons with secondary or university education. In the case of marital status, there is a statistically significant spatial regression for one-person households, but not clearly for one-family households. Economic activity or employment is statistically significant for simple regression even in a small territory such as the Czech Republic. For the solution, it is possible to use several types of models offered by (econometrics) theory. In the case of households, the spatial Durbin model (SDM) is relatively widely used, because of the inclusion of both endogenous and exogenous interaction effects, based on the criteria chosen (Log Likelihood, AIC, SBC). However, the results for other models (SAR, SEM, SDEM) are not significantly different.

Název v anglickém jazyce

Spatial regression model of households: a case of the Czech Republic

Popis výsledku anglicky

In recent decades, household and family patterns have changed significantly. On one hand, one can notice a smaller number of children living in families, as well as a reduction of family numbers in general as a result of the low fertility rate and the postponement of childbearing in Europe. On the other hand, there has been a significant increase in the proportion of one-person households and families without children among people at an older age because of population ageing. The research question is to what extent these changes in the structure of households are spatially homogeneous and whether the proportion of households of a given type in a given territory can be explained by other demographic variables such as age, education, marital status or economic activity. The above-mentioned principles can be demonstrated by the example of data for households, whose detection and subsequent analysis is an integral part of population censuses. To solve this problem, it is possible to use spatial data analysis methods, which can be defined as a quantitative data analysis, in which the explanation is dependent on explicit spatial variables when predicting the investigated phenomenon based on spatial autocorrelation. The assumption of spatial regression is the existence of autocorrelation. The results of both Moran&apos;s I and Geary&apos;s C show that the autocorrelation for both types of households was found to be statistically significant and increases as the distance between adjacent elements (i.e. municipalities) decreases. Age is an important factor affecting the structure of households. The results for both types of households show that the age groups with the greatest influence on the creation of one-person households or one-family households can also be used to create a spatial model. A similar claim applies to the average age. Education showed that the share of persons with primary education has no influence on spatial regression, unlike the share of persons with secondary or university education. In the case of marital status, there is a statistically significant spatial regression for one-person households, but not clearly for one-family households. Economic activity or employment is statistically significant for simple regression even in a small territory such as the Czech Republic. For the solution, it is possible to use several types of models offered by (econometrics) theory. In the case of households, the spatial Durbin model (SDM) is relatively widely used, because of the inclusion of both endogenous and exogenous interaction effects, based on the criteria chosen (Log Likelihood, AIC, SBC). However, the results for other models (SAR, SEM, SDEM) are not significantly different.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50402 - Demography

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Espace-Populations-Societes

ISSN

0755-7809

e-ISSN

2104-3752

Svazek periodika

Neuveden

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

27

Strana od-do

12307

Kód UT WoS článku

000778804900003

EID výsledku v databázi Scopus

2-s2.0-85130706460