<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Research background</h3>
<p>The phenomenon of low fertility in Poland is a vital subject of demographic analysis. In recent years, not only have there been changes in procreative and family models, but also in the age structure of society. This is particularly significant in the context of population ageing, which is becoming increasingly evident and brings numerous challenges such as increased burden on healthcare systems, a decrease in the active workforce, and the need to secure adequate retirement funds. Despite the desire to have children, many individuals refrain from making such a decision, and the reasons for this choice are diverse. Therefore, it was essential to conduct an analysis of the factors determining fertility in Poland, considering both the economic and social aspects. Understanding how the economic situation, labour market conditions, and changes in social structure impact on the decision-making process regarding childbearing is essential.</p>
</sec>
<sec><h3>Purpose of the paper</h3>
<p>The objective of this article was to analyse fertility rates in Poland for the period 2004-2020. The conducted research identified the factors influencing the observed state of low generational replacement and determining their intensity.</p>
</sec>
<sec><h3>Methodology/Methods/Data sources</h3>
<p>The data used in this article were sourced from the Central Statistical Office and covered the years 2004-2020. The study was based on literature concerning demography and econometrics. Three statistical methods were applied in the analysis of fertility in Poland: the Classical Method of Least Squares (CMLS) model, the Fixed Effects (FE) estimator, and the Random Effects (RE) estimator. Fertility analysis was conducted at regional level by dividing Poland into 16 administrative units (voivodeships). A panel model was employed for the analysis, and the results were subjected to Wald, Breusch-Pagan, and Hausman tests to compare the outcomes obtained from different models.</p>
</sec>
<sec><h3>Findings</h3>
<p>The results of the analysis indicate that the economic situation and the labour market significantly influence the decision to have children in Poland. The trend of low fertility, although showing some increase, is still characteristic of the country compared to other EU nations. The analysis of the factors determining fertility is vital for understanding the decisions of young generations of Poles regarding parenthood.</p>
</sec>
</abstract>

Research background
The phenomenon of low fertility in Poland is a vital subject of demographic analysis. In recent years, not only have there been changes in procreative and family models, but also in the age structure of society. This is particularly significant in the context of population ageing, which is becoming increasingly evident and brings numerous challenges such as increased burden on healthcare systems, a decrease in the active workforce, and the need to secure adequate retirement funds. Despite the desire to have children, many individuals refrain from making such a decision, and the reasons for this choice are diverse. Therefore, it was essential to conduct an analysis of the factors determining fertility in Poland, considering both the economic and social aspects. Understanding how the economic situation, labour market conditions, and changes in social structure impact on the decision-making process regarding childbearing is essential.

Purpose of the paper
The objective of this article was to analyse fertility rates in Poland for the period 2004-2020. The conducted research identified the factors influencing the observed state of low generational replacement and determining their intensity.

Methodology/Methods/Data sources
The data used in this article were sourced from the Central Statistical Office and covered the years 2004-2020. The study was based on literature concerning demography and econometrics. Three statistical methods were applied in the analysis of fertility in Poland: the Classical Method of Least Squares (CMLS) model, the Fixed Effects (FE) estimator, and the Random Effects (RE) estimator. Fertility analysis was conducted at regional level by dividing Poland into 16 administrative units (voivodeships). A panel model was employed for the analysis, and the results were subjected to Wald, Breusch-Pagan, and Hausman tests to compare the outcomes obtained from different models.

Findings
The results of the analysis indicate that the economic situation and the labour market significantly influence the decision to have children in Poland. The trend of low fertility, although showing some increase, is still characteristic of the country compared to other EU nations. The analysis of the factors determining fertility is vital for understanding the decisions of young generations of Poles regarding parenthood.

Demographic Challenges in Poland: Understanding Low Fertility

Econometrics

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

{"article-title":"Demographic Challenges in Poland: Understanding Low Fertility"}

Research background
The phenomenon of low fertility in Poland is a vital subject of demographic analysis. In recent years, not only have there been changes in...

Demographic Challenges in Poland: Understanding Low Fertility

Publié en ligne: 13 mars 2024

Pages: 1 - 14

DOI: https://doi.org/10.15611/eada.2023.4.01

Mots clés
ertility, population ageing, demographic analysis, total fertility rate (TFR)

© 2023 Anna Tatarczak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Demographic Challenges in Poland: Understanding Low Fertility

Publié en ligne: 13 mars 2024

Pages: 1 - 14

DOI: https://doi.org/10.15611/eada.2023.4.01

Mots clésertility, population ageing, demographic analysis, total fertility rate (TFR)

© 2023 Anna Tatarczak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Mots clés
ertility, population ageing, demographic analysis, total fertility rate (TFR)