Spatial Autocorrelation of a Demographic Phenomenon: a Case of One-Family Households and One-Person Households
Abstrakt
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's I and Geary'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.