Corresponding author: Nelly S. Smulyanskaya ( nsmulya@mail.ru ) © 2020 Nelly S. Smulyanskaya.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Smulyanskaya NS (2020) Factors of fertility ageing rate. Population and Economics 4(1): 6074. https://doi.org/10.3897/popecon.4.e53039

The current demographic model of most developed countries is characterized by ageing and declining fertility. Despite the fact that this topic has been studied quite thoroughly, the question remains: what national indicators does the rate of ageing fertility depend on in different groups of countries? An analysis of some developed countries between 1990 and 2017 enables concluding that the dynamics of intensity of the first births over the age of 35 in the group of developed countries is negatively influenced by the dynamics of employment and the share of services in GDP, while the maternal age at first birth in the group of former socialist countries depends on the dynamics of the education indicator.
fertility ageing, birth factors, birth models, second demographic transition
Late and low fertility has been one of the characteristics of the demographic model in developed countries over the past few decades. In the more developed countries, this process began in the 1970s. The process found a theoretical justification in D. van de Kaa and R. Lesthaeghe’s theory of the second demographic transition. The main reason for this situation is the change in sociocultural norms in a society in which the individual with his or her personal needs and desires has come to the fore. In macroeconomic terms, the second demographic transition has been accompanied by an increase in the share of employed women (
After major changes in demographic behaviour patterns in the most developed countries, demographic science has multiplied theories explaining the causes of fertility decline and postponement of childbirth. According to some theories, the main factor in fertility decline is partners’ selfesteem of their financial wellbeing and higher parents’ demands for the upbringing of their children (
All of the abovementioned papers study fertility indicators and/or their dynamics, not the rate of change. There are very few works on the study of the determinants of fertility ageing rate among older women in Russian demographic science.
The process of increasing fertility in older ages should be separated from the process of delayed childbearing. These two processes can go in parallel and have a similar result, but different causes and possible development options. In this study the process of increasing fertility is measured by the share of first births over the age of 35 and the postponement of childbearing is measured by the woman’s mean age at first birth (MAB1).
Figure
The dynamics of the fertility ageing indicators over 40 years (1970–2010). Source: Human Fertility Database, Rosstat, author’s calculations
As shown in Figure
If we draw a trend line within each group, we see that for the second group of countries the increase in the mean maternal age and the share of first births over the age of 35 occurred in parallel, while in the more developed countries the increase in the share of first births over the age of 35 occurred with no further growth of the mean maternal age. It is clear that, under current conditions, for these countries, the mean maternal age of 28 is a certain limit (for psychological, physiological or other reasons) after which women are no longer inclined to postpone the childbearing despite all modern socioeconomic challenges. Obviously, there is also a growth limit of the firstbirth rates among women over the age of 35. But so far, even in countries with the latest birth rate, the proportion of late first births does not exceed 13–15% (of the total number of firstborns), although it continues to rise.
In the countries under review, the cumulative firstbirth rates (CBR1) over the age of 35 rose by 2–5 times in most countries between 1990 and 2014. Record growth was observed only in the Czech Republic, where the indicator grew by 8 times. The Czech phenomenon is associated with the “quick start” and a faster second demographic transition. At the same time, as noted above, in the former socialist countries up to 1998 there was some “rejuvenation” of fertility associated with the social and economic crisis after the collapse of the USSR.
Figure
The dynamics of the share of first births in the TFR for women over 35 years of age compared to 1990. Source: Human Fertility Database, Rosstat, author’s calculations.
The mean age at first birth increased in all the countries under review, and the growth varied from 5% over 24 years for the most developed countries in which the ageing process had begun long ago (Japan, Sweden and the Netherlands), up to 25% in the Czech Republic.
Figure
Dynamics of the mean maternal age at first birth compared to 1990. Source: Human Fertility Database, Rosstat, author’s calculations.
The analysis indicates that both the postponement of childbearing and the increase in fertility in older ages occur at different rates in different developed countries. This raises the question: the dynamics of which country’s characteristics affects fertility ageing rate, i.e. the dynamics of the analyzed demographic indicators?
In several studies analyzing the relationship between macroeconomic indicators and fertility, we can identify some interrelationships between demographic indicators and country’s parameters. However, the impact of GDP on fertility is quite controversial. Thus, some authors consider the decline in fertility as a result of economic crisis, which can be compensated later by a shift in the birth calendar (
The analysis of employment and fertility in developed countries revealed a change in the interdependence from negative to positive (;
The relationship between demographic processes and education is studied from several perspectives of mutual influence. Thus, in several works the relationship between late motherhood and the age of graduation has been proven (
The mentioned papers show the influence of socioeconomic factors on demographic indicators. This study attempts to identify the extent of the impact of changes in national socioeconomic characteristics on changes in the indicators of fertility ageing.
When making hypotheses, attention should be paid to the fact that for the indicator of the intensity of births after the age of 35, they may vary depending on the order of a birth of a child. It should be noted that further analysis was done only for the first birth indicator for the sake of “cleanliness” in relationship identification.
Hypotheses concerning the dependence of the dynamics of the analyzed variables and the dynamics of the indicators of fertility ageing are presented in Table
Hypotheses regarding the dynamics of analyzed variables.
Variable  Impact on the share of births over the age of 35  Effect on the maternal age at first birth 

GDP  The influence is twofold: first births at this age can be the result of a country’s economic development, higherorder births correlate with poverty and lower quality of human capital in the society.  The effect is positive. Later motherhood is usually the result of economic development in the country and the financial sustainability of the household. 
Employment  Greater employment reduces the free time that a woman would alternatively spend on the birth and upbringing of children. The impact is negative.  The effect is positive. Entry into the labour market shifts the life cycle of the woman, including childbearing. 
Infant mortality  The more children die, the greater is the need for additional births. The effect is positive.  The more children die, the sooner it is necessary to begin a maternal life. The impact is negative. 
Education  The influence is twofold: first births at this age can be the result of higher education, then the impact is positive, higher order births correlate with poverty and lower levels of education.  Higher levels of education lead to a delayed family life. If time spent on starting a career is added to the time spent on education, the effect will increase. The effect is positive. 
LE  If we consider LE as an indicator of the quality of life, the impact is positive (higher levels of reproductive health and greater economic opportunities for the birth and upbringing).  The effect is positive. The increase in LE leads to a longer planning horizon and the possibility of later childbearing. 
Share of the services sector  The impact is positive – the greater is the labour market for women, the greater is the likelihood of childbearing after some time of labour activity.  The impact is positive – the greater is the labour market for women, the greater is the likelihood of childbearing after some time of labour activity. 
In this study, a regression analysis of the dependence (leastsquares method) was carried out. The dependent variable are the changes in the TFR indicators for the first births of women over the age of 35 per year (model 1) and the mean maternal age at first birth (model 2). The dependent variable was taken with a oneyear time lag that is required for the childbearing. The data source is the HFD database. Countries for which birth order data are available are selected from the database.
Independent variables are the changes over the year of the following country’s characteristics:
The data source is the World Bank database.
Analyzed period is 19902014 (there are data gaps on a longer period for most countries).
For preliminary analysis of results and identification of the problem of multicollinearity a correlation matrix for indicators of both groups of countries has been constructed (Tables
Correlation matrix between indicators for the first group of countries.
CBR1 35+  MAB1  GDP  Work  Education  LE  Services  Infant mortality  
1  0.00  0.14  0.28  0.13  −0.11  −0.17  −0.15  CBR1 35+ 
1  −0.15  −0.28  0.22  0.00  0.08  −0.07  MAB1  
1  0.40  0.27  −0.05  −0.20  0.03  GDP  
1  0.05  −0.18  −0.07  0.11  Work  
1  −0.03  0.00  −0.15  Education  
1  −0.13  −0.02  LE  
1  0.01  Services  
1  Infant mortality 
Correlation matrix between indicators for the second group of countries.
CBR1 35+  MAB1  GDP  Work  Education  LE  Services  Infant mortality  
1  −0.05  0.36  0.29  −0.10  −0.05  −0.32  −0.20  CBR1 35+ 
1  −0.28  −0.17  −0.01  −0.03  0.12  −0.01  MAB1  
1  0.30  0.11  −0.13  −0.34  −0.32  GDP  
1  −0.02  −0.04  −0.11  −0.16  Work  
1  −0.25  0.23  0.10  Education  
1  0.09  −0.24  LE  
1  0.18  Services  
1  Infant mortality 
According to results presented in the correlation matrix, there are no significant relationships between the variables at all. On the one hand, this reduces the risk of multicollinearity. On the other hand, low correlation indicates a high risk of lacking significant links between the dynamics of the analyzed indicators.
Models were separately built for the former socialist countries (Ukraine, Russia, Lithuania, Hungary, Czech Republic, Bulgaria, Belarus and Slovakia) and for the rest of the capitalist countries. An attempt was also made to build a common model with a dummy variable for the socialist group of countries. In such a model, the dummy variable was insignificant.
Based on the results of econometric modelling the conclusions presented in Table
The results show that the dynamics of childbearing intensity over the age of 35 in the former socialist countries are not affected by any factor under review. In the group of developed countries, the negative impact of employment and the share of services in GDP (as sectors with a high share of female labour) are significant. The alleged explanation is that young women are willing to give birth sooner (the birth of the first child after the age of 35 is gradually becoming the norm in these countries rather than an exception) in the absence (or a significant reduction) of the alternative of work.
Dependent variable – dynamics of the birth rate of the first child for women over 35 years old.
Country  const  GDP  Employment  Education  Infant mortality  LE  Share of services  R^{2} 
Former socialist countries  0.2  
Developed capitalist countries      0.0  
All countries  +  +  0.1 
Dependent variable – the change in average maternal age at first birth.
Country  const  GDP  Employment  Education  Infant mortality  LE  Share of services  R^{2} 
Former socialist countries  +  0.1  
Developed capitalist countries  0.0  
All countries  0.0 
For all countries, the constant and the dynamics of infant mortality are significant, which is contrary to the hypothesis. The inability to explain this dependence calls the robustness of the model into question.
Unfortunately, low R^{2} indicates that a large share of changes in the intensity of births after 35 years is not attributable to the factors considered.
The analysis shows that the change in the birth calendar in the former socialist countries depends on education and this corresponds with the hypothesis. Other variables are insignificant.
Unfortunately, models with average maternal age at first birth as the dependent variable also have a very low R^{2}.
It can be concluded that country’s characteristics do not explain a large share of changes in demographic indicators. This may suggest that the postponement of childbirth is rather inertial, and the process that started at some point (women’s active participation in the labour market, their high level of education and the growth of its duration) has almost no reverse movement regardless of socioeconomic fluctuations in the country. The average age of childbearing in the former socialist countries confirms this statement. The dynamics of fertility ageing indicators may depend both on the accumulated effect of several previous years and on the situation in which the country was at the beginning of the study (“quick start” etc.).
The dynamics of childbirth intensity in the group of developed capitalist countries is negatively influenced by the dynamics of employment and the share of services in GDP. And the dynamics of the maternal age at first birth in the group of former socialist countries depends on the dynamics of the education indicator.
As a result, it can be concluded that the change in the average maternal age at first birth is also more “sensitive” to the change in the share of women with higher education in the former socialist countries, and to change in labour market participation in developed capitalist countries. However, the models obtained explain only a small proportion of the changes in the analyzed demographic indicators. A possibly important factor influencing the dynamics of demographic indicators, but not included in the model, may be the stability of social traditions and stereotypes in the country, but this indicator is quantitatively immeasurable.
Nelly Stanislavovna Smulyanskaya, Ph.D., Lomonosov Moscow State University. Email: nsmulya@mail.ru
Model 1. All observations, dependent variable – delta of TFR for first births of women over the age of 35.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .374^{a}  .140  .112  .07757 
ANOVA^{a}
Model  Sum of squares  df  Mean square  F  Sig. 
1 Regression  .215  7  .031  5.098  .000^{b} 
Residual  1.324  220  .006  
Total  1.538  227 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .002  .017  .108  .914  
GDP  .329  .168  .224  1.956  .052 
Labour  .290  .282  .083  1.029  .305 
Educ  .078  .094  .068  .831  .407 
LAB  .816  1.491  .098  .547  .585 
Services  .263  .166  .113  1.599  .113 
Mortality  .007  .044  .034  .159  .874 
Countype  .039  .013  .223  3.014  .003 
Model 2. All observations, dependent variable – delta of average maternal age at first birth.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .148^{a}  .002  .009  .01424 
ANOVA^{a}
Model  Sum of squares  df  Mean square  F  Sig. 
1 Regression  .001  7  .000  .708  .666^{b} 
Residual  .045  220  .000  
Total  .046  227 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .001  .003  .324  .746  
GDP  .008  .031  .031  .255  .799 
Labour  .027  .052  .044  .516  .606 
Educ  .023  .017  .115  1.326  .186 
LAB  .192  .274  .135  .702  .484 
Services  .006  .030  .015  .192  .848 
Mortality  0.002  .008  .065  .289  .773 
Countype  .003  .002  .095  1.202  .231 
Model 3. Countries of the first group, dependent variable – delta of average maternal age at first birth.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .155^{a}  .024  .016  .01725 
ANOVA^{a}
Model  Sum of squares  df  Mean square  F  Sig. 
1 Regression  .001  6  .000  .592  .737^{b} 
Residual  .043  145  .000  
Total  .044  151 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .003  .002  1.708  .090  
GDP  .031  .070  .099  .446  .656 
Labour  .052  .093  .071  .565  .573 
Educ  .031  .026  .143  1.217  .226 
LAB  .465  .507  .318  .917  .360 
Services  .025  .071  .035  .347  .729 
Mortality  0.004  .014  .120  .291  .771 
Model 4. Countries of the first group, dependent variable – delta of TFR for first births of women over the age of 35.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .213^{a}  .045  .006  .08227 
ANOVA^{a}
Model  Sum of squares  Df  Mean square  F  Sig. 
1 Regression  .047  6  .008  1.148  .338^{b} 
Residual  .981  145  .007  
Total  1.028  151 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .039  .010  4.094  .000  
GDP  .328  .336  .213  .975  .331 
Labour  .023  .443  .007  .053  .958 
Educ  .174  .122  .166  1.425  .156 
LAB  2.033  2.418  .288  .841  .402 
Services  .054  .340  .016  .158  .875 
Mortality  .016  .068  .093  .229  .819 
Model 5. Countries of the second group, dependent variable – delta of TFR for first births of women over the age of 35.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .466^{a}  .217  .149  .06730 
ANOVA^{a}
Model  Sum of squares  Df  Mean square  F  Sig. 
1 Regression  .087  6  .014  3.188  .008^{b} 
Residual  .312  69  .005  
Total  .399  75 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .080  .017  4.681  .000  
GDP  .310  .178  .221  1.740  .086 
Labour  .557  .355  .187  1.661  .101 
Educ  .107  .153  .081  .696  .489 
LAB  .477  1.804  .031  .264  .792 
Services  .278  .175  .191  1.589  .117 
Mortality  .123  .226  .064  .542  .590 
Model 6. Countries of the second group, dependent variable – delta of average maternal age at first birth.
Model’s report
Model  R  R^{2}  Adjusted R^{2}  Standard error of estimate 
1  .333^{a}  .111  .034  .00376 
ANOVA^{a}
Model  Sum of squares  Df  Mean square  F  Sig. 
1 Regression  .000  6  .000  1.437  .213^{b} 
Residual  .001  69  .000  
Total  .001  75 
Coefficients^{a}
Model  Unstandardized coefficients  Standardized coefficients  T  Sig.  
B  Std. error  Beta  
1constant  .007  .001  7.792  .000  
GDP  .022  .010  .301  2.218  .030 
Labour  .016  .019  .100  .832  .408 
Educ  9.738E7  .009  .000  .000  1.000 
LAB  .094  .101  .116  .932  .355 
Services  .003  .010  .045  .354  .724 
Mortality  .016  .013  .156  1.234  .221 