Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects


Pronatalist Policies and Fertility in Russia: Estimating Tempo and 
Quantum Effects

Asiya Validova

Abstract: This paper examines the family policy reforms of 2007 in Russia that 
were aimed explicitly at encouraging second and higher-order births, and analyses 
their impact on fertility. The existing empirical fi ndings about population policy 
interventions in transition economies are inconclusive, while the most common 
argument states that policies based on material incentives are insuffi cient to 
signifi cantly raise the real fertility in a population. The study aims to offer a better 
insight to the following research question: was Russian demographic policy 
effective in terms of raising the fertility level in the country or did it merely change 
the timing of births? The objective of the paper is to measure two effects of the 
pronatalist policy in Russia: tempo effect and quantum effect. Using data from 
the Human Fertility Database, I employ the decomposition method to separate 
tempo and quantum effects in the observed total fertility rate, and I estimate 
their relative weight in observed fertility changes. The analysis of period fertility 
indicators confi rmed the prevalence of a tempo effect in observed total fertility 
rate change, but also revealed a quantum effect of the policy measures, although 
this was much smaller. Policy impact varied by birth order. For second parity, the 
tempo effect played a more critical role, while for third parity the quantum effect 
was more important. Another decomposition approach employed to measure the 
contributions of various factors in the increase of the number of births during the 
post-reform period showed the quantum effect which was driven by second and 
third order births. The study provides empirical evidence of the impact of policies 
on fertility behaviour, expands the existing analysis of pronatalist measures taken 
in Russia, and contributes to our understanding of the role of tempo and quantum 
effects in the recent fertility change in Russia. 

Keywords: Fertility Rates · Tempo and Quantum Effects · Pronatalist Policies · 
Births · Russia

Comparative Population Studies
Vol. 46 (2021): 425-452 (Date of release: 18.10.2021)

Federal Institute for Population Research 2021 URL: www.comparativepopulationstudies.de
      DOI: https://doi.org/10.12765/CPoS-2021-15
      URN: urn:nbn:de:bib-cpos-2021-15en7
    



•    Asiya Validova426

1 Introduction

The new population policies introduced by the Russian government in 2007 aimed 
to encourage larger families to address the massive fertility decline that has 
posed a demographic challenge since the 1990s. The effectiveness of pronatalist 
policies depends on their ability to address the major causes of low fertility and 
reverse a trend toward a small family size. While in some low fertility countries, 
childbearing postponement and an increase in childlessness have been among 
the factors driving the fertility decline, in the case of Russia, the prevalence of the 
one-child family has been the most infl uential fertility behaviour feature (Elizarov/ 
Levin 2015). Russia differs from Western low-fertility countries in several aspects: 
its demographic transition occurred later, and the country has undergone a rapid 
institutional transformation caused by the collapse of the Soviet Union. 

In the communist era, the authoritarian political system and pronatalist ideology 
were generally favourable for “early and universal childbearing” (Frejka/Gietel-
Basten 2016). Due to a traditional early marriage and childbearing model associated 
with the system of job-protected parental leave, state-provided childcare and 
housing, the Soviet Union was able to maintain relatively high fertility rates; at the 
same time, female labor force participation rates were also quite high (Frejka/Gietel-
Basten 2016). 

Looking at the historical context, one can conclude that the main tools of the 
pronatalist policies in the Soviet system were monetary incentives ever since their 
introduction in the 1930s (Frejka/Gietel-Basten 2016; Elizarov 2019). Adopted in 
1944, the system of lump-sum benefi ts and monthly childbearing allowances, the 
increase in maternity leave for female employees and expansion of the network 
of childcare facilities, as well as measures for the moral encouragement of having 
many children (the “Maternity Medal” and the title “Heroine Mother”) aimed 
to increase fertility and at the same time were conducive to maintaining a high 
female labor force participation rate (Elizarov 2019). Similarly, the pronatalist policy 
introduced in the 1980s, which was designed to stimulate an increase in fertility, 
was based on fi nancial incentives. Analyses of the impact of family and pronatalist 
measures adopted by the Communist party at the beginning of the 1980s revealed 
that, despite the positive response from the population and increase of the period 
fertility rates, the quantum of fertility remained unchanged, and there was no 
increase in completed fertility of the birth cohorts; there was only a timing effect 
(Frejka/Zakharov 2013). 

Economic and political instability played a role in fertility decline during the 
1990s. This process also coincided with a change in the childbearing pattern from 
early to later childbearing. The societal transformation brought about job insecurity, 
expensive housing, and declining childbearing allowances, but it also meant more 
freedom, leisure, and consumption opportunities. During this time, the ideal of a two-
child family weakened and the age pattern of fertility shifted from early childbearing 
to later ages. Consequently, the number of one-child families became prevalent in 
Russia (Frejka 2008; Sobotka 2011). Unmet demand for formal childcare facilities 
had a negative impact on parents’ intentions to have more children. The 1990s 



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 427

can be described as the period of transformation of fertility behaviour in Russia in 
response to negligible government family support as manifested by the signifi cant 
drop in the family allowance. Analyses of cumulated cohort fertility rates by birth 
order conducted by Frejka and Zakharov (2013) showed that “the main driver of 
the fertility declines among the 1960s and 1970s birth cohorts” had been a loss of 
second order births. 

In the light of those demographic trends, the dramatic 2007 reform of family policy 
in Russia aimed explicitly at encouraging couples to have two or more children. 
The pronatalist agenda included a broad package of family support measures (see 
Appendix A1). The fi rst measure included substantial increases in pregnancy, birth, 
and child allowance, and provided favourable parental leave conditions. Another 
measure which sought to raise fertility directly – giving “maternal capital” (a “baby 
bonus”) to mothers of second and higher-order births – was the most signifi cant 
initiative of the pronatalist policy. Other government measures included child-care 
subsidies and tax benefi ts. In line with the aim of stimulating higher-order births, 
subsidies for child-care services (for state child-care institutions) were differentiated 
by birth order. The provision of tax benefi ts to families with children was another 
measure; however, tax deductions for parents were negligible, except for tax credits 
for parents of children with special needs. Pronatalist policies had an immediate 
effect on the observed period fertility indicators and brought about an increase in 
the number of births in Russia after 2007. 

The purpose of the present paper is to offer a better insight into the effi cacy of a 
pronatalist policy in a country with a transition economy. Russia, largely because of 
the sheer scale of the latest reforms, makes an interesting case study for addressing 
the question of whether government support, motivated by pronatalist arguments, 
affects fertility. The change in demographic policy in Russia was unprecedented 
in the previous twenty-fi ve-year period; a new fi nancial scheme allowed mothers 
of second and subsequent newborns or adopted children to become eligible for 
substantial support. Was Russian demographic policy effective in terms of raising 
the fertility level in the country or did it merely change the timing of the births? In 
demographic research, the tempo effect implies that the change in the timing of 
births (which alters the period fertility rates but does not affect the real fertility 
rates of the cohorts since the completed cohort fertility) remains unchanged. The 
quantum effect occurs only if the level of completed fertility – the number of births 
per woman throughout her reproductive age – changes (Philipov/Kohler 2001). 
Defi ning the investigation of the policy effects on the country’s fertility level as the 
primary purpose of the study, I aim to answer the following research question: was 
the raise in period fertility primarily infl uenced by the change in the level of fertility 
(quantum effect) or by the advancement of childbearing in response to monetary 
incentives provided to mothers (tempo effect)? The study period covers seven 
years before the reform and ten years after the policy came into effect (2000-2017).

The paper is organized as follows: the next section provides a brief overview of 
previous research on the effi cacy of pronatalist measures of family policy, followed 
by the formulation of research hypotheses. Section 3 explains the empirical strategy, 



•    Asiya Validova428

data, and methods adopted for the analysis followed by the results section. The 
paper concludes with a summary and a further discussion of the fi ndings. 

2 Literature review

Theoretical approaches linking policies and demographic behaviour stem from the 
works of various scholars (e.g., Becker 1960, 1965, 1981; Schultz 1973, 1981; Willis 
1973; Cigno 1991) who contributed to resolving the problem of analysing fertility 
as a form of economic behaviour and, in doing so, formulated the new economic 
approach to family behaviour. This neoclassical economic theory of fertility is 
based on the postulate that a decision to have a child is a rational decision made 
by parents as a result of weighing the costs and benefi ts of childbearing against 
their income, career preferences, and own perceptions concerning the quality of 
care provided to children, etc. (Becker 1981; Cigno 1991). These basic assumptions 
of the economic theory of fertility provide the interpretative framework of how 
policies might impact reproductive behaviour. Such measures as public subsidies 
that reduce the cost of childrearing or government transfer payments that raise 
income are very likely to increase the demand for children (Gauthier/Hatzius 1997). 
By directly reducing the cost of having a child for working women, public policy can 
encourage a woman to have a child she would not have had otherwise, resulting in 
an increase in fertility. Women may also decide to have a child earlier than planned 
because of this reduction in cost. In other words, two components of fertility may 
be affected by the introduction of the policy: the tempo effect (concerns the timing 
of births) and the quantum effect (relates to the total number of children born, 
including childlessness). 

Despite theoretical explanations of the role of public transfers in stimulating 
fertility, there is an active debate about how to apply the economic framework to 
improve our understanding of policy as one of the determinants of fertility. It is hard 
to quantify the effect of any such policy because prevailing demographic trends in 
the relevant period can substantially affect the fertility behaviour of the population 
in the country/region. Such phenomena as being in a transition economy, changes 
in the status of women (e.g., more extended education and higher employment), 
changing perceptions of what constitutes a family (e.g., cohabitation, divorce), 
postponement of marriage and childbearing that had taken place in developed 
countries all had an adverse effect on fertility in recent decades. 

As for empirical research, there is no clear-cut evidence regarding the impact 
of public policies on overall fertility rates. A body of literature that looked at the 
effects of explicitly pronatalist policies pointed to the success of such measures and 
their positive effect on fertility (Hoem 1993; Hoem et al. 2001; Lalieve/Zweimü ller 
2009). These concluded that larger family benefi ts are associated with higher 
levels of fertility. However, an extensive literature review by Gauthier (2007) shows 
mixed evidence regarding demographic policy effectiveness. It is also debatable 
whether such policies lead to the removal of barriers to higher fertility; they may 



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 429

well infl uence the timing of fertility, but not the completed family size (Blanchet/
Ekert-Jaffe 1994; Gauthier 2007). 

There has been far less analysis of family policy interventions in post-communist 
countries which underwent enormous societal, economic and political transformation 
in the late 1980s and 1990s. Most studies based on Central and Eastern European 
(CEE) countries investigate the change in fertility trends and fertility transition that 
occurred in the region. Their focus was on investigating variation in fertility over 
time and determining the driving force behind the fertility decline in this group of 
countries. The possible causes for the sharp fall in fertility after the end of a centrally 
planned economy include the following: economic and political turmoil associated 
with high unemployment and economic uncertainty (Ranjan 1999; Kohler/Kohler 
2002; Kreyenfeld et al. 2012), and also demographic causes such as the second 
demographic transition and postponement transition (Zakharov/Ivanova 1999; 
Zakharov 1997; Billingsley 2010). 

Cross-national and national analyses of population policy interventions in 
transition economies are quite scarce and inconclusive; the evidence of the 
policy effectiveness is mixed. Frejka and Gietel-Basten (2016) linked the trends 
in fertility behaviour – such as postponement of childbearing – to different state 
intervention approaches in CEE countries by providing the fi rst systematic analysis 
of the relevant family policies since the 1990s, although they underlined their 
general ineffectiveness. On the other hand, some case-studies on post-communist 
countries’ interventions which had been introduced to fi ght the decline in fertility 
after the fall of the communist system showed a signifi cant and differentiated effect 
on fertility. For example, an increase in the length of child-raising allowance (“full-
time motherhood”) in Hungary had a positive effect on fertility among women with 
low educational attainment, while a generous tax-relief reform increased higher-
order birth risks of educated mothers (Spéder et al. 2017).

Although the meta-analysis of fertility trends in transition economies sheds 
light on the common patterns of fertility behaviour in this group of countries, 
CEE countries varied substantially in terms of socio-economic development and 
country-specifi c demographic trends, as seen by the variation in fertility rates 
across the region. The total fertility rates (TFR) and women’s mean age at birth 
after 1990 imply that Russia had a different aggregate fertility pattern compared 
to other countries in the region (Kohler/Kohler 2002). The causes of fertility decline 
in Russia include economic, socio-cultural and policy-based factors (Kohler/Kohler 
2002; Gerber 2006). For the Russian population, the transition period appeared 
to apply substantially more constraints to childrearing (compared to other CEE 
countries); this argument is supported by the evident decrease in the quantum of 
fertility (Philipov/Kohler 2001). 

Most studies that cover the last wave of reforms in demographic policy in Russia 
are generally quite sceptical about the effectiveness of the measures introduced in 
2007. Many authors point to the short-term increase in fertility rate, but attribute 
this increase predominantly to coincidence with the 1980 cohort (the period of high 
fertility and a high proportion of higher-order births) reaching its peak reproductive 
age (Frejka/Zakharov 2013; Elizarov/Levin 2015; Arhangel’skij et al. 2016). Such 



•    Asiya Validova430

arguments suggest that demographic measures in contemporary Russia may lead 
to similar outcomes as those introduced in the 1980s – mainly causing a lowering 
of the age at childbirth and lessening of birth intervals – but they are very unlikely 
to have a signifi cant effect on real fertility. For example, Frejka and Zakharov (2013) 
state that family policy measures in Russia designed to encourage higher-order 
births have little impact on women’s decision to give birth to a second and third 
child; the authors view the policy based on material incentives as insuffi cient to 
raise signifi cantly the real fertility in the population. They base their estimates on 
the Gender and Generation Survey for Russian Federation (the 2011 round) and 
argue that the 2011 wave of the survey shows that “intentions to have another child 
were virtually the same as in the 2004 or 2007 rounds” (Frejka/Zakharov 2013). 
Other scholars who examined the use of the “maternal capital” programme and the 
attitudes to this family support measure point to the distrust of the policy by Russian 
women and families as well as to the many bureaucratic obstacles that discourage 
its utilization (Borozdina et al. 2016). Most demographers who specialize in Russian 
population policy note that it is too early to make a conclusion about the outcome of 
the policy of 2007, since the data on completed cohort fertility is not yet available. 
But the importance of evaluating the family policy early on is well known. This also 
has advantages, especially in terms of future policy implications. 

The inconclusiveness of the existing results on the effi cacy of pronatalist policies 
is associated with many challenges demographers and public economists face 
when estimating the long-term effects of public policy on population reproductive 
behaviour. First, most policies are introduced nationwide at the same time, which 
makes it diffi cult to determine a causal effect with no information on the potential 
outcomes in the absence of the policy. Second, the impact of a policy is especially 
hard to assess in countries that are undergoing changes in demographic behaviour 
due to demographic, economic and social factors: advancement of childbearing 
age, childbirth postponement in response to economic uncertainty or societal 
transformations, changes in perceptions about family structure, marriage behaviour, 
gender roles and labor market participation. So, the main obstacle here arises from 
the fact that the timing of births as a result of the policy may also coincide with 
the progression of the tempo transition (Goldstein et al. 2009). Finally, it can be 
complicated to differentiate between short-term and long-term outcomes of family 
policy. Often a temporary shift in fertility behaviour may occur in response to the 
policy, however, it will not necessarily lead to real changes in reproductive behaviour 
and have the desired long-term effect.1

The present paper aims to contribute to the body of empirical studies that 
investigate the impact of policies on fertility behaviour and to expand the existing 
preliminary analysis of pronatalist measures taken in Russia during the past decade. 

Two research hypotheses are formulated as follows:

1 An example of this are the pronatalist policies in the German Democratic Republic during the 
1970s and early 1980s (cf. Kreyenfeld 2004).



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 431

Hypothesis 1: The pronatalist policy of 2007 in Russia had two effects: tempo 
and quantum; the tempo effect was prevalent, but the quantum effect was also 
evident. 

Hypothesis 2: The variations in the fertility indicators over the post-reform period 
are likely to be the result of women having second and third births.

In formulating the hypotheses I was guided by the fact that such an unprecedented 
increase in family support by government could have had an impact, and to some 
extent could have encouraged an increase in the real fertility in the country. 
Moreover, my analysis of the Gender and Generation Survey 2011, which examined 
women’s decision to have a second and third child, also supported the plausibility 
of this hypothesis (see Appendix A2).

I claim that the quantum effect could be distinguished by means of adjusted 
period fertility indicators that better approximate the cohort fertility than the total 
fertility rate. Separation of these two effects enables us to determine the primary 
reason for the rise in observed fertility, which in turn helps us to assess the 
effectiveness of the policies in question. Along with this objective, it seems relevant 
to differentiate these effects by birth order. Since the most important development 
in the Russian demographic policy concerns second and higher order births, the 
predominant focus in the paper is on higher parity births. 

3 Data and Methods

As stated above, the empirical analysis of how policies infl uence reproductive 
behaviour is prone to methodological challenges that complicate the assessment 
of the specifi c contribution of the policy change. Since Russian demographic policy 
reforms were applied nationwide, I cannot compare populations affected by the 
reforms with populations that were not (control group). Moreover, the reforms took 
effect at the same time for all Russian regions, and although regional family support 
was also introduced, the magnitude of federal government spending on creating 
pronatalist incentives for families was signifi cantly larger. Regional family policies 
varied substantially across the Russian regions in terms of timing, remuneration and 
eligibility requirements which, along with many demographic and socioeconomic 
regional differences, makes it methodologically diffi cult to evaluate the effects 
of regional policies. For this reason, only the federal programme is the object of 
primary interest. 

Owing to the universal nature of the demographic policy, all women of 
reproductive age were affected by these policies. I rely on period fertility rates since 
I do not have data to compare the completed fertility of affected cohorts with the 
fertility of cohorts born in 1960 in order to estimate the change in fertility before 
and after the policy implementation. I base the calculations on period fertility data 
for the Russian Federation obtained from the Human Fertility Database (HFD) of the 
Max Planck Institute for Demographic Research (Germany) and the Vienna Institute 
of Demography (Austria). The analysis covers the period 2000-2017, thus examining 
a ten-year period after the reform took place. From a cohort perspective, different 



•    Asiya Validova432

cohorts were affected by the new family policy: older cohorts (1970s, 1980s birth 
cohorts), who are likely to have already started their family formation process at 
the time of the reform, and younger cohorts (cohorts born in the 1990s), who had 
to make all their fertility decisions taking the post-reform fi nancial incentives into 
account. Some cohorts experienced peak fertility before the reforms took place 
(birth cohorts of 1970-1982) and could not respond to the policy by making changes 
in their fertility behaviour; others include younger cohorts – whose fertility peak 
falls in the period of the new policy (birth cohorts of 1983-1986), thus shaping their 
reproductive behaviour. 

The central question in this study is to determine the most appropriate way to 
measure fertility level by using period indicators. The sensitivity of conventional 
fertility indicators such as age-specifi c fertility rates (ASFR) and TFR to the changes 
in the timing of births – postponement or advancement – inspired extensive literature 
devoted to the construction of indicators that adjust those fertility measures for 
this distortion. Moreover, the distortion of the order-specifi c fertility rates may be 
caused by the parity composition of the population (Kohler et al. 2002). Therefore, 
age and parity-specifi c childbearing probabilities and intensities can be employed to 
measure the period fertility more accurately (Sobotka 2003). A summary indicator, 
analogous to TFR, which controls for age and parity was established as parity-age-
TFR (PATFR) (Rallu/Toulemon 1994). Although it removes the parity composition bias 
which is present in the order-specifi c TFR, PATFR does not ignore the changes in the 
timing of childbearing among women (Sobotka 2003). As suggested by Bongaarts 
and Feeney (BF) (1998), a measure of fertility – tempo-adjusted total fertility rate 
(TFR*) – overcomes this limitation and allows the separation of tempo and quantum 
effects in changes of the total fertility rate.2

The main novel aspect of the empirical strategy applied in this paper lies in 
employing several different approaches to estimate the tempo and quantum effects 
of the policy. First, I adjust the observed fertility measures for various distortions 
and present a graphical analysis of various indicators used to measure fertility. 
Second, I decompose tempo and quantum components and estimate their weight 
in observed fertility rates, adopting a method proposed by Sobotka (2003). A further 
decomposition approach is also employed and the contributions of various effects 

2 Considerable caution is needed regarding the interpretation of tempo-adjustment of TFR. It 
should be noted that the BF formula relies on the assumed invariant shape for fertility schedule 
and no cohort effects. By comparing the completed fertility of cohorts with the weighted 
average of the adjusted TFRs during the reproductive years of these cohorts, Bongaarts and 
Feeney found relatively close correspondence between the two fertility indicators, and thus 
validated the adjustment (Kohler/Philipov 2001). This simplifying BF assumption was criticized 
by a number of scholars (Kohler/Philipov 2001; Van Imhoff 2001). Kohler and Philipov (2001) 
addressed the shortcoming by proposing an extension of the adjusted TFR that includes 
variance effects – TFR adjusted to fertility schedules with changing variance (Kohler/Philipov 
2001). However, the sensitivity analysis of Yi and Land (2001) showed that the BF method is 
generally robust because the annual changes in the shape of the fertility schedules are usually 
more or less constant, except for very rare cases of extremely large changes (Yi/Land 2001). 
Moreover, as pointed by Kohler and Phillipov (2001), in Russia the cohort effects are of a very 
small magnitude, and do not affect the BF-adjusted TFR.



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 433

(with a focus on tempo and quantum) to the increase of the number of births during 
the post-reform period are measured. 

For the fi rst part of the demographic analysis, I use fertility indicators such 
as the mean age at birth (MAB), TFR, and the tempo-adjusted total fertility rate 
(TFR*) suggested by Bongaarts and Feeney (1998) on the Russian Federation data 
in the Human Fertility Database (HFD) (see 4.1. in the Results section). The MAB 
is calculated from the age-specifi c fertility rates across the entire range of the 
reproductive period, and can be also computed for each birth order. Measuring 
the mean age at birth gains its importance as childbearing postponement becomes 
a characteristic of the second demographic transition (Philipov/Kohler 2001). 
Postponement leads to a rise in the mother’s mean age at birth, thus decreasing 
the observed period ASFR and TFR because women delay childbearing until later in 
life. This has particular relevance for the Russian Federation where postponement 
of childbearing started in the mid-1990s (about 30 years after the typical pattern for 
western European countries). The mother’s mean age at birth in Russia has been 
steadily increasing since the 1990s; the age group with the highest fertility rate 
changed from 20-24 years in the Soviet Union to 25-29 years in the post-Soviet 
period. At the same time, pronatalist policies can cause women to advance the time 
of the birth in response to the monetary stimulus, which would have the opposite 
effect: a reduction of the mean age at birth and a temporary increase in period TFR. 
The use of order-specifi c total fertility rates during the period of the advancement 
of childbirth will thus overestimate the short-run effect of the rise in fertility rates. 

To overcome this limitation, I calculate the tempo- and parity-adjusted fertility 
rate (TFRp*)3, constructed by Bongaarts and Sobotka (2012), which is based on 
order-specifi c fertility but takes into consideration the changing parity distribution 
of women of reproductive age. Demographers acknowledge that TFRp* yields 
considerably more consistent results because it eliminates several distortions in 
the conventional TFR (Bongaarts/Sobotka 2012). It therefore shows the quantum 
component of the total fertility rate, controlling for the biases discussed above.4 

Graphical representation of the adjusted fertility indicators along with the MAB 
for the period in question provides some very valuable insights regarding fertility 
patterns, but it does not allow us to differentiate between timing and quantum 
effects in fertility, which facilitates a better understanding of how a demographic 
policy infl uences fertility behaviour. 

Consequently, in order to evaluate the policy effects more precisely, the second 
part of the analysis section breaks the fertility measures down into tempo and 

3 TFRp* was calculated using the following formula (Bongaarts/Sobotka 2012):
This indicator is the tempo-adjusted version of TFRp(t) – 
the total fertility rate derived from rates of the fi rst kind 

(Bongaarts/Feeney 2006). See Bongaarts and Sobotka (2012) for details about fertility indicators.
4 Owing to a lack of data, Kohler and Ortega adjusted PATFR (adjPATFR) which allows separation 

of “the infl uence of timing change (‘mean tempo effect’). Parity composition (‘parity composition 
effect’) on the total fertility rate” (Kohler/Ortega 2002) was not included in the analysis, and 
TFRp* was used instead.

itr
itap

itTFRptTFRp
i

 



•    Asiya Validova434

quantum components and estimates their relative weight in observed fertility 
changes. For this purpose, I relied on the method described by Sobotka (2003), albeit 
making all necessary case-specifi c modifi cations. The rationale of the above study 
is to combine different period fertility indicators that serve as the best approximates 
of the cohort fertility for every birth order and to estimate fertility quantum in 
periods when the postponement of childbearing took place. Sobotka (2003) used 
the constructed hybrid indicator (comTFR) which “combines adjusted parity-age 
TFR (adjPATFR) indicator for birth order 1 and 2 with the TFR for birth order 3+”. 
The method used in my work has several modifi cations that are motivated primarily 
by the difference in the fertility trend between Russia and Eastern European 
countries in these two periods, and, secondly, by the limited availability of data. 
In the Russian case, the advancement of births has been observed for the period 
following pronatalist reforms. At the same time, childbearing postponement has 
also been practised since the mid-1990s, and is particularly pronounced for parity 
one which is consistent with the demographic transition theory. To understand if 
an increase in observed TFR was driven by the quantum changes in fertility or by 
the advancement of the births (tempo effects), I use TFRp* for all births combined 
and for each birth order instead of employing the comTFR hybrid indicator which 
combines two different indicators that make interpretation diffi cult. 

The third approach used here to separate different effects of the pronatalist 
policy in Russia is the method developed and described by Sobotka, Lutz and 
Philipov (2005). This method separates period changes in the total number of births 
for the period 2006-2017 into a series of effects: changes in the “mean generation 
size” of women in their reproductive age, changes in fertility timing in tandem with 
changes in the parity distribution of women (tempo effect) and changes in the level 
of fertility (quantum effect).5 The decomposition method used here allows us to 
determine the weight of each factor to the absolute change in the number of live 
births during the post-reform period.

The essence of this method is as follows: I decompose the change in the total 
number of births into three main components for the period from 2006 to 2017 (t0 
and t1 respectively) using three indices IG, IT and IQ (results presented in Table 4 
in the Results section). 2006 is chosen as a reference year – the year preceding 
the start of the new policy. The mean generation size (G) is a concept which was 
introduced by Calot (1984) as an indicator that accounts for the annual number of 
potential mothers (Sobotka et al. 2005). It links the recorded number of births B in 
a particular year with the period total fertility rate in that year.6 IT is the index of 
tempo distortion, and it is estimated for each year considered using the following 
formula: IT = TFR ÷ TFRp* (as has been noted, I use TFRp* instead of adjPATFR). 
Since I do the decomposition of change for a period of time, ideally for a benchmark 

5 In the paper authors state that “a framework of such decomposition has, however, been 
suggested earlier” by Ortega and Kohler (2002) and Kohler and Ortega (2004) (Sobotka et al. 
2005).

6 The mean generation size is calculated as follows: G = B ÷ TFR (Sobotka et al. 2005).



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 435

year (t0), I would ideally choose a year free from tempo effects, so that B0 (number 
of births) remains unaffected by tempo distortions. In this case, however, it is 
impossible – there is no year with no postponement/advancement of births. I 
therefore use an alternative method and standardize IT to capture tempo distortions 
in comparison with the reference year (IT(STAND) = IT(t1) ÷ IT(t0)) to “estimate the 
hypothetical number of children born, assuming no tempo effects during the period 
of observation (BT ’)” by using the formula (Sobotka et al. 2005). Thus, B may be 
expressed as B = G × TFRp* × IT. Applying the methodology used in the paper I 
referred to, it is possible to distinguish between three components of the change 
in the total number of births by introducing two more indices that contribute to 
this change: IG, which stands for the relative change in the mean generation size G 
between the years t0 and t1 (IG(t1) = G(t1) ÷ G(t0)), and IQ, which stands for the relative 
change in fertility quantum between the same years (IQ(t1) = TFRp*(t1) ÷ TFRp*(t0)). 
Therefore, I can express the observed total number of births B in 2017 as a function 
of the observed number of births in 2006 and the indices of change for this period; 
it will show us the relative contributions of timing effect, quantum effect and mean 
generation size effect in the changes in total number of births over this period (see 
Table 3 in the Results section).7

In addition to the decomposition of total indicators of fertility tempo, quantum 
and mean generation size, I distinguish the changes in these components by birth 
order, paying particular attention to birth orders 2 and 3 (presented in Table 4 in the 
Results section). Lastly, I use the extension to the basic decomposition following the 
method of Sobotka et al. (2005), and decompose the index of tempo distortion (IT). 
This incorporates the mutual effect of tempo effect and shift in parity composition 
into the index of genuine tempo distortion, Iτ, and the parity composition index, ID 
(IT = ID ∙ Iτ), using the non-adjusted fertility indicator PATFR based on age-parity 
fertility tables for the calculations of the indices: IDi = TFRi / PATFRi and IQi = PATFRi 
/ adjPATFRi.

My main focus is on the role of the pronatalist policy in Russia that had different 
effects related to changes in fertility level and timing; the advancement of births is 
assumed to be the prevailing effect. Therefore, to separate out the effects of the 
changing tempo and quantum of period fertility in the total number of births in 2007-
2017, it is important to know how many births were “gained” due to changes in the 
timing of births as result of the family policy. As far as the data are concerned, such 
statistics as the number of births by women’s age and birth parity, and the fertility 
indicators, were extracted from the Human Fertility Database (HFD). Similarly, I use 

7 B(t1) = B(t0) ∙ IG(t1) ∙ IQ(t1) ∙ I(STAND, t1) = B0(t0) ∙ IG(t1) ∙ IQ(t1) ∙ (It(t1) ⁄ IT(t0)), and ∆B(t0,t1) = ∆BG 
+ ∆BQ + ∆BT + ∆Bgqt. Each indicator ∆B is computed from the corresponding index of change 
I and the initial number of births B0 in year t0. ∆Bgqt represents all the interactions between 
them. The direct effect of changes in the magnitude of tempo distortion is ∆BT (t0, t1) = (IT(STAND, 
t1) – 1) · B0. For the detailed theoretical elaboration of the method see Sobotka et al. (2005)



•    Asiya Validova436

TFRp* to estimate the adjusted fertility rates, which are computed using the formula 
of Bongaarts and Sobotka (2012).8

4 Results

4.1 Mean age at birth (MAB) and adjusted fertility measures 

I start the investigation of the interplay between the tempo and quantum effects by 
analysing a variety of measures graphically. The main overall trends are summarized 
in the fi gures below. The mean age at birth (MAB) alongside the observed TFR and 
the adjusted indicators (TFR* and TFRp*) are shown in Figures 1 and 2. 

There are two possible reasons for the increase in the mothers’ mean age at 
birth (MAB) that is shown in Figure 1 (panel a). One scenario resulting in an increase 
of the mothers’ mean age is due to the postponement of births – a trend that has 
affected Russia since the beginning of the 1990s. Another possible explanation for 
an upward change in the mothers’ mean age could be the introduction of the policy 
measures that gave women in older age groups incentives to have another child. 
This phenomenon, however, is generally quite rare because younger women tend 
to be more responsive toward monetary stimuli in the demographic policy. In the 
case of Russia, until 2006 one can observe an increase in the mean age of mothers 
for all births combined due to birth postponement. Starting from 2007, the mean 
age at fi rst birth still demonstrates the continuing postponement process, while an 
increase in mean age at second and third births throughout 2007-2017 is different 
in nature: older women with one or more children respond positively to the fertility 
stimulus.

The lowering of the mothers’ mean age can also be infl uenced by a range of 
factors. The MAB decreases as the fertility of younger women increases; it may 
happen if women of younger ages respond to the public policy more actively than 
older women, or react positively to favourable economic conditions (economic 
growth). In this case the advancement takes place; women have children earlier than 
they initially planned (timing effect). Another plausible explanation of the decrease 
in the mean age at birth lies in the more active response to the demographic policy 
by younger age groups (age-specifi c quantum effect). The graphical analysis of 
the mothers’ mean age at different birth orders illustrates how in 2010 there was 
a downward movement for birth order 2 and 3 while MAB1 exhibited a gradual 
increase during the same period (Fig. 1 (panels b, c, d)). As for MAB1, I attribute 
the slight increase to the continuing birth postponement; women who had not had 

8 The paper of Sobotka et al. (2005) that employs this decomposition method bases the calculation 
of adjusted fertility indicators on a simplifi ed version of the adjustment method proposed by 
Kohler and Ortega in 2002 and combines them with TFR for birth order 3 and higher. I do not 
have the detailed data necessary for the computation of the Kohler-Ortega adjustment and 
therefore replace them with TFRp* for all parities; I consider it to be the best tempo- and parity-
adjusted measure, and I have all the necessary data for its computation.



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 437

children during the economic turmoil of 2008 due to the higher economic uncertainty 
postponed their fi rst childbirth, but the fi gures recuperated after the country started 
to come out of recession in 2010.

Regarding the changes in mean age changes for higher parities, Figure 1 (panels 
c, d) shows that MAB2 and MAB3 had been stagnating since 2010; the reason might 
be the fact that advancement of childbirth for parity two and three contributes to this 
trend (timing effect). Had that not been the case, one would have seen an increase 
in MAB2 and MAB3 caused by the overall trend of birth postponement in Russia. 
However, I assume that the quantum effect is also present here: at the outset of 
the economic crisis, the youngest subgroup of women with one or more children 
reacted more actively to the pronatalist policy (“maternal capital” in particular), 
therefore providing downward pressure on the mothers’ mean age at second and 

Fig. 1: Mean age at birth for women by parity and combined in Russia (2000-
2018)

25

26

27

28

29

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

2
0

1
8

Year

(a) Mean age at birth combined

Age

(b) MAB at parity 1

(c) MAB at parity 2 (d) MAB at parity 3

23

24

25

26

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

2
0

1
8

Year

Age

27.0

27.5

28.0

28.5

29.0

29.5

30.0

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

2
0

1
8

Year

Age

30.0

30.5

31.0

31.5

32.0

32.5

33.0

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

2
0

1
8

Year

Age

Source: Constructed based on data from HFD



•    Asiya Validova438

third births. This observation is consistent with my second explanation for the cause 
of decrease of mean age at birth. 

As for fertility rate measures (presented graphically in Fig. 2), my predominant 
focus is on the analysis of changes of TFRp* for all births combined and TFRp* 
for parity 1, 2 and 3. Bongaarts and Sobotka (2012) highlighted the usefulness of 
this indicator for its ability to control for the composition of women by parity and 
to assess the undistorted quantum of period fertility. Additionally, I will refer to 
the patterns of change of two other fertility measures – TFR and TFR* – where 
appropriate. 

Following the demographic policy reform, an increase in all fertility measures 
can be seen for several years: TFR, TFR* and TFRp* dynamics suggest an immediate 
effect of the policy implementation. TFR* and TFRp* are considerably higher than 

Fig. 2: Observed and adjusted total fertility rates by parity in Russia (2000-
2017)

1.2

1.4

1.6

1.8

2.0

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

Year

TFR

TFR*

TFRp*

(a) All births combined

Fertility indicator

(b) Parity 1

(c) Parity 2 (d) Parity 3

0.6

0.7

0.8

0.9

1.0

1.1

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

Year

TFR

TFR*

TFRp*

Fertility indicator

0.3

0.4

0.5

0.6

0.7

0.8

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

Year

TFR

TFR*

TFRp*

Fertility indicator

0.05

0.10

0.15

0.20

0.25

2
0

0
0

2
0

0
1

2
0

0
2

2
0

0
3

2
0

0
4

2
0

0
5

2
0

0
6

2
0

0
7

2
0

0
8

2
0

0
9

2
0

1
0

2
0

1
1

2
0

1
2

2
0

1
3

2
0

1
4

2
0

1
5

2
0

1
6

2
0

1
7

Year

TFR

TFR*

TFRp*

Fertility indicator

Source: Data combined from HFD and author’s calculations based on HFD data



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 439

TFR from 2007 to 2009, just as they were for the years before the launch of the new 
policy (Fig. 2). This trend indicates the existence of birth postponement throughout 
the period driven by birth postponement for the fi rst parity: the mean age at birth for 
all births combined has been increasing. TFR grew from 1.3 in 2006 to 1.78 in 2015 
and then declined to 1.57 in 2018. At fi rst, TFR* and TFRp* also increased in line with 
an upward trend established at the end of 2006. Remarkably, the adjusted fertility 
indicators moved in the opposite direction in 2009-2011; the visual representation 
of the variables’ dynamics shows that both TFR* and TFRp* diverged and fell during 
this period, and only afterward did a growth trend emerge. My explanation is based 
on several factors (examined below) that affect changes in MAB and on adjusted 
fertility indicators for birth orders of different parity.

As stated above, changes in TFR1 have not been infl uenced by the introduction 
of the pronatalist policy package. For that reason, and due to the absence of data 
concerning the motivation to have children by this group of women during the 
period of analysis, one cannot accurately estimate the extent of tempo and quantum 
changes in TFR1. Examining the fertility rate for birth order 1, I turn the focus to TFR1 
because, due to the increase of MAB1, the TFR1 dynamics appear highly distorted. 
As we know, the birth postponement trend is more pronounced for the lower birth 
orders. Figure 2 (panel b) demonstrates the increase in TFR1* throughout 2005-
2009, presumably caused partially by economic growth in the country and partially 
by the effects of the demographic policies. Overall, this increase in TFR1* was 
followed by a sharp drop in the indicator, as Russia had been hit by the world-
wide Great Recession of 2008; TFR1* continued to decrease through 2011. A rapid 
increase of MAB1 in 2008-2010 indicated that women had reacted to the economic 
crisis by postponing their fi rst birth; however, improvement in the economic 
conditions applied downward pressure to MAB1 over the post-crisis period (Fig. 1, 
b). From 2012 onward, the overall tendency towards postponement of the fi rst child 
continued, and, starting from 2015, there was a considerable decline in all three 
fertility indicators. 

A closer look at the variations of fertility rates for higher-order births in Russia 
reveals interesting fertility behaviour trends (Fig. 2 (panels c, d)). The increase in all 
fertility indicators in combination with a growing mean age at birth for second and 
third parity until 2010 could indicate a small quantum effect during the period (as 
women of a young age group also contributed to changes in TFR2). Interestingly, 
the mean age stopped rising in 2010, pointing to a policy-related birth advancement 
trend (tempo effect). As for fertility rates, in 2010-2011 adjusted indicators for higher 
parities showed virtually no change and even a slight decrease (crisis effect), but 
continued their upward trend in 2011-2015 (2011-2016 – for the third parity) which, 
according to my proposition, points to the presence of a small quantum effect. The 
rapid decline in fertility from 2015 as shown by the decrease in TFRp* provides 
evidence that the policy measures are indeed short-lived and not substantial 
enough to increase real fertility, especially when other macro factors such as 
sluggish economic growth, a growing proportion living in poverty, plunging real 
wages, and overall economic stagnation are also discouraging people from having 
more children. 



•    Asiya Validova440

To sum up, a fall in the TFR* and TFRp* in 2009-2011 could be explained by 
different factors: fi rstly, the TFR* for fi rst parity decreased in 2009-2011 as MAB1 in 
2008-2010 increased (due to birth postponement during the crisis); secondly, there 
was no growth in the TFR* for higher-order births and even decreased slightly. 
Combined, these factors lowered the TFR* and TFRp*. This observation confi rms 
the hypothesis about the timing effect’s prevalence in response to the pronatalist 
measures: despite the overall trend of childbearing postponement, in the post-
intervention period, the lowering of the mean age at second birth narrows the 
interval between fi rst and second births. In other words, women decided to have 
their second child earlier than they would have in the absence of the policy. 

4.2 Estimation of tempo and quantum effects in TFR

Using the framework developed by Sobotka (2003) for childbirth postponement, I 
estimate tempo and quantum elements of fertility change in Russia for the period 
which followed the introduction of the family policy. In Table 1 I compare order-
specifi c and overall TFR for 2017 with TFRp* by birth order and for all births 
combined. In order to represent the quantum effect – the real increase in the fertility 
level – I fi rst constructed the index of change of quantum, which is computed as 
a ratio of TFRp* in 2017 to TFRp* in 2007, and I then computed a coeffi cient (IQ-1) 
that is used to establish the quantum part of the difference between the observed 
TFR in 2017 and 2007. The difference between the change in observed TFR in 2007 
and 2017 and the calculated quantum effect estimates the timing effect. To enable 
comparison of the timing and quantum effects for pre- and post-reform periods I 
also reproduced these calculations for the period prior to the policy intervention 
(2000-2006) (Table 2).

The results for the post policy period demonstrate that estimated tempo and 
quantum components vary by birth order (Table 1). The quantum effect for parity 
1 was negligible because the family policy reforms targeted those who already 
had at least one child. Therefore, the change in TFR1 is mostly due to the timing 
of births. For higher-order births we see a different picture: the tempo effect for 
parity 2 is prevalent; however, there is a small quantum effect for the second birth 
order (13 percent). With increasing parity, the quantum effect becomes the more 
important factor. For birth order 3, 42 percent of the increase in TFR3 is driven 
by the change in the real level of fertility. The TFR results for all births combined 
indicate the prevalence of the tempo effect, suggesting that overall TFR increased 
to a considerably larger extent due to the advancement of births. There was also 
a change in the quantum and was estimated at 9 percent for TFR. Results for the 
time period which preceded the policy, presented in Table 2, indicate virtually no 
quantum effect in the TFR. Even for the second and third parities, the quantum 
effect is very small, which is consistent with the observation that it did not boost 
TFR2 and TFR3 prior to the reforms.



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 441

4.3 Decomposition of tempo and quantum effects in the total number 
of births

The results of decomposing the quantum, tempo and mean generation size effects 
in the total number of births are presented in Table 3. One can see positive effects of 
fertility quantum and fertility advancement in the change of the number of births; but 

Tab. 1:  Quantum and tempo effects in TFR by birth order in Russia between 
2007 and 2017*

 Birth order 1 Birth order 2 Birth order 3 Total

1 TFR 2007 0.761 0.475 0.125 1.416
2 TFR 2017 0.704 0.605 0.223 1.621
3 Reduction in TFR(2)-(1) -0.057 0.130 0.098 0.205
4 TFRp* 2007 0.871 0.525 0.141 1.590
5 TFRp* 2017 0.861 0.595 0.200 1.728
6 IQ (5)/(4) 1.012 1.133 1.418 1.087
7 IQ-1 0.012 0.133 0.418 0.087
8 quantum effect(3)*(7) -0.001 0.017 0.041 0.018
9 tempo effect(3)-(8) -0.056 0.113 0.057 0.187

10 quantum effect % 1 13 42 9
11 tempo effect % 99 87 58 91

Tab. 2:  Quantum and tempo effects in TFR by birth order in Russia between 
2000 and 2006

 Birth order 1 Birth order 2 Birth order 3 Total

1 TFR 2000 0.702 0.358 0.092 1.196
2 TFR 2006 0.753 0.409 0.101 1.305
3 Reduction in TFR(2)-(1) 0.051 0.051 0.009 0.109
4 TFRp* 2000 0.900 0.492 0.119 1.560
5 TFRp* 2006 0.871 0.466 0.109 1.492
6 IQ (5)/(4) 0.968 0.947 0.916 0.956
7 1-IQ 0.032 0.053 0.084 0.044
8 quantum effect(3)*(7) 0.002 0.003 0.001 0.005
9 tempo effect(3)-(8) 0.049 0.048 0.008 0.104

10 quantum effect % 3 5 8 4
11 tempo effect % 97 95 92 96

* Estimation of tempo and quantum components in TFR change was made largely based 
on the method suggested in Sobotka (2003: 188-189).

Source: Author’s calculations based on HFD data

Source: Author’s calculations based on HFD data



•    Asiya Validova442

they were partially offset by a decrease in mean generation size over the observed 
period. That happened because the large birth cohort of women born in the 1980s 
started to leave the fertility peak reached in the early 2000s. Table 3 contains an 
evaluation of births “gained” due to the advancement of childbirths during the whole 
period since the launch of the new family policy. To assess the relative long-term 
contribution of each considered factor, I estimated mean annual births “gained” due 
to those factors by relating them to the hypothetical number of births in 2006 had 
the family policy not taken place and advancement of childbearing not occurred. 
Results summarized in this table show the overall effects of three components of 
change on the “additional” number of births.

Overall, two features stand out regarding the major effects. First, the tempo 
effect increases the number of births only by 1.4 percent in comparison with 
2006. Second, the change in fertility quantum over the analysed period had an 
even stronger impact on the increase in the number of births (15.8 percent). Mean 
generation size was the important factor and was negative for this period, which 
means that it went in the opposite direction and contributed to the decline in fertility 
rates. Overall, these results support the conclusion that the examined pronatalist 
policy could lead to both tempo and quantum effects; change in quantum and 
tempo distortions had a positive infl uence on the absolute number of births after 
the reform had taken effect. 

Table 4 illustrates how the contribution of the major factors that infl uence the 
changes in the number of births varies for each parity. As for mean generation size 

Tab. 3: Decomposition of changes in the total number of births and relative 
contribution of the main factors from the beginning of the pronatalist 
policies in Russia (2007-2017)

Reference year t0 2006
Analyzed period t1 2007-2017
Births in the reference year t0 1,479,637
Hypothetical births in t0 (without tempo effects) 1,692,949
Mean annual births in t1 1,774,870
Mean annual gained births in t1 81,921
Of which due to  

Tempo effects 1,147
Quantum changes 12,944
Mean generation size G -3,441
Interaction 1,106

%
Tempo effects 1.40%
Quantum effects 15.80%
Mean generation size G -20.32%
Interaction 1.35%

Source: Author’s calculations based on HFD data



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 443

factor, it differs by birth order but makes a negative contribution for all three parities. 
This observation is supported by the fact that a large cohort of women are ageing 
and leaving their fertility peak. Generally, the effect of IG for all births combined 
remains relatively stable at 0.95. 

The infl uence of the quantum component is characterized by marked differences 
by birth order: the positive effect of the fertility quantum index (IQ) is the largest 
for parity 3 and becomes smaller for parity 2, whereas for parity 1 it is just under 1 
(0.99). Subsequently, the fertility quantum increased strongly for birth order 3 and 
moderately for birth order 2, thus indicating the difference in the interpretation of 
changes in the total number of births caused by including order-specifi c indices in the 
analysis. The positive total effect of the fertility quantum supports the assumption 
of the existence of the quantum effect. The standardized index of tempo distortion 
is at its maximum for parity 3 (1.2); while its infl uence on parity 2 is smaller, and 
for parity 1 turns negative. The index of tempo distortion IT captures the collective 

Tab. 4:  Decomposition of change in the number of births in Russia by parity 
(2017 compared with 2006) and decomposition of the index of tempo 
distortion IT in Russia in 2017*

Parity 1 Parity 2 Parity 3 Total

Births(B(t0)) in 2006 880,026 448,659 150,950 1,479,635

Births(B(t1)) in 2017 656,607 654,548 250,969 1,664,615

B(t1)/B(t0) 0.746 1.459 1.663 1.125

TFRi(t0) 2006 0.753 0.409 0.101 1.305

TFRi(t1) 2017 0.704 0.605 0.223 1.532

TFRp* 2006 0.872 0.466 0.109 1.490

TFRp* 2017 0.861 0.595 0.200 1.728

G(t0) 2006 1,168,693.227 1,096,965.770 1,494,554.455 1,133,819.923

G(t1) 2017 932,680.398 1,081,897.521 1,125,421.525 1,086,563.316

IG (t1) 0.798 0.986 0.753 0.958

IQ (t1) 0.987 1.277 1.835 1.158

IT (t0) 2006 0.864 0.878 0.927 0.875

IT (t1) 2017 0.818 1.017 1.115 0.887

IT STAND(t1) 2017 0.947 1.159 1.203 1.014

IT decomposition in 2017:     

PATFRi 2017 0.819 0.532 0.190 1.636

IT (t1) 2017 0.818 1.017 1.115 0.887

ID (t1) 0.860 1.137 1.174 0.936

Iτ(t1) 0.951 0.894 0.950 0.947
* Calculations of the decomposition indices in the following tables have been made 

employing the methodology of Sobotka et al. (2005) used for Hungary, Poland and 
Spain in the cited paper.

Source: Author’s calculations based on HFD data



•    Asiya Validova444

infl uence of tempo effects and the shifts in parity composition (Sobotka et al. 2005). 
Here the tempo index is decomposed into two components: index of genuine tempo 
distortion (Iτ) and index of parity composition (ID). All the indices discussed here are 
presented in Table 4. For parity 2 and 3, parity composition had a positive effect, 
while genuine tempo distortion was less than 1. The results for all birth combined, 
which capture the negative effects of both indices for parity one, show that tempo 
distortion was explained to an equal extent by the genuine tempo effect (0.95) and 
by the parity composition (0.94).

To sum up, the application of such decomposition for different parities uncovered 
marked differences in the weights of the tempo and quantum components. 

5 Conclusions and Discussion

This paper explores the effectiveness of population policy. Its main focus is on a 
set of pronatalist reforms introduced in Russia in 2007, the intention of which was 
to encourage mothers to have more births and raise further children. The bulk of 
empirical evidence with regard to policy effectiveness in different countries to date 
provides no plausible link between an increase in long-term fertility and government 
policies. In the case of Russia, the new round of pronatalist benefi ts had a positive 
effect on observed period fertility indicators, demonstrated by the increased number 
of births and the steady upward trend in the period total fertility rate in Russia 
following the implementation of the policy. However, a mere rise in observed fertility 
rates does not mean that the family policy efforts actually affect completed fertility; 
therefore, an analysis of the timing of births is of particular importance. Given that 
reforms were based on creating monetary incentives for families, I investigated the 
following research question: was the raise in period fertility primarily infl uenced 
by the change in the level of fertility (quantum effect) or by the advancement of 
births in response to monetary incentives provided to mothers (tempo effect)? This 
study, therefore, aimed to contribute to our understanding of the role of tempo and 
quantum effects in the recent fertility change in Russia, which has not yet been 
suffi ciently explored in the existing literature.

The main novel aspect of the present evaluation of the family policy is the 
application of a multi-method approach, which makes it possible to investigate the 
nature of change in several fertility indicators and enables the results obtained to be 
compared in methodologically different ways. 

In the fi rst part of the study, I provided a descriptive analysis of such indicators 
as mothers’ mean age at birth, TFR, and alternative fertility indicators (TFR* and 
TFRp*) that eliminated common distortions. Adjusting for timing and parity revealed 
differences in the patterns of change of the indicators for different parities. Higher 
order fertility thereby increased due to possible effects of the policy, while fi rst 
parity rates and mean age at birth of the fi rst child did not change and only varied 
due to the economic crisis and the continued trend of birth postponement. As for 
the tempo-quantum interplay, changes in adjusted fertility rates and the lowering of 
the mean age at birth for parity 2 and higher generally supported the “advancement 



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 445

of births” hypothesis, but stabilization of MAB2 and MAB3 from 2011 combined with 
an increase in adjusted fertility rates for those parities suggested the possibility of 
a quantum effect. 

Further, I presented two ways to estimate the contribution of tempo and quantum 
effects in the incr ease seen in the period fertility indicators that followed the 
pronatalist policy launch in Russia. Although the contribution of tempo effects has 
been extensively studied in the context of birth postponement and the reduction 
of fertility rates in Europe (Kohler et al. 2002; Lutz et al. 2003; Sobotka 2004), 
decomposition of the tempo and quantum effects in the context of policy-induced 
advancement has not yet been thoroughly examined. Furthermore, the infl uence of 
these effects on total births in Russia has not been investigated either so far. The two 
methods employed in the analysis largely followed the approach of Sobotka (2003), 
whereas those of Sobotka, Lutz and Philipov (2005) were applied in the case of birth 
advancement in Russia. The fi rst approach separated tempo and quantum effects in 
observed total fertility rate over the analysed period, thus allowing an assessment 
of the role of the advancement of births provoked by policy incentives in the period 
fertility rise. As for total fertility rate for all births combined, the results support the 
widely-held view that the tempo effects are the primary driving force behind the 
ensuing increase in period fertility. But the results also identifi ed a quantum effect 
(9 percent) supportive of the fi rst hypothesis of the current study. Differentiation 
by birth order reveals a different picture: while second-time mothers changed the 
timing of birth (tempo effect prevalent), the fertility rates for third births changed 
due to both tempo and quantum effects. The quantum effect for the fi rst parity was 
negligible, which supports the second hypothesis regarding the main role of second 
and third births in the period fertility increase.

The second approach taken in this paper also provided evidence of how the 
interpretations of change in fertility are modifi ed once order-specifi c components 
are taken into consideration. In that part of the analysis, I measured different effects 
of policy on the total number of births by separating the overall change in births 
into three components: changes in the “mean generation size” of women in their 
reproductive age, changes in the level of fertility (quantum effect), and changes in 
fertility timing combined with changes in the parity distribution of women (tempo 
effect) (a method proposed by Sobotka, Lutz and Philipov (2005)). As for the relative 
contribution of the fertility quantum component, this increased the number of 
third births, while for birth order two its weight was shown to be less pronounced. 
Overall, the estimated size of the effects on the change in the total number of 
live births consistently supported the presence of both tempo (1.4 percent) and 
quantum effects (15.8 percent); and the fertility quantum impact was stronger than 
the impact of the tempo effects. The mean generation size had a negative effect, 
indicating a decrease in the number of women of primary reproductive ages. This 
fi nding supports the proposition discussed in the literature about the temporary 
fertility increase which coincided with the 1980 cohort (the period of high fertility 
in response to pronatalist measures introduced in the 1980s) reaching its peak 
reproductive age (Frejka/Zakharov 2013; Arhangel’skij et al. 2016)



•    Asiya Validova446

In summary, the main conclusions with regard to fertility dynamics since Russia 
introduced its new policy in 2007 are as follows: along with the rise of period 
measures of fertility such as total fertility rate, indicators adjusted for timing and 
parity nevertheless demonstrate an increase in the level of fertility. Two methods 
utilized in the study used very different decomposition approaches for timing 
and quantum effects, and, therefore produced different results. However, both 
supported the existence of the quantum effect, which was the general research 
question of the paper. Some expected results emerged regarding the infl uence of 
the pronatalist policy on the timing of births; family policy based on birth-related 
monetary incentives did change fertility behaviour in terms of an advancement of 
births, especially during the fi rst years of the new policy regime. The order-specifi c 
decomposition of tempo and quantum effects revealed a clear difference in the 
relative contribution of these effects to second and third births. 

However, the rapid decline in fertility rates observed from 2015 suggests that the 
analysed demographic policy cannot be classifi ed as successful in the long run. This 
supports the widely held view that explicit pronatalist policies generally infl uence 
the timing of fertility and not the completed family size (Gauthier 2007). The presence 
of a quantum effect, as found in this study, indicates that the goal of increasing the 
level of fertility has been achieved to some extent, but it may still be quite short-
lived. The present empirical analysis leads to the conclusion that although the policy 
signifi cantly raised benefi ts from a previously relatively low level, it did not have a 
clearly positive effect on long-term fertility. The most plausible explanation of the 
phenomenon is the fact that, in Russia, the principal tools for reaching a demographic 
goal remain fi nancial. International evidence and the results of this study show that 
the most effective family policy model is one which not only offers fi nancial benefi ts 
to families, but where there is also comprehensive support through social changes 
towards gender equity and family-friendly policies that help women combine work 
and childrearing, as well as accessible institutional childcare.

A fi nal word of caution concerns the limited analysis of fertility from the period 
perspective. As has been noted, I did not have enough data to look at completed 
fertility for all relevant cohorts. Although attempts have been made to eliminate 
biases in TFR by adjusting it for tempo distortions, there is room to improve the 
results, for example by incorporating Kohler-Ortega adjustment. More detailed 
investigation of how family policies are linked to cohort fertility is therefore left for 
future exploration. 

Acknowledgements
I express the deepest appreciation to Joachim Singelmann, Ph.D. Professor Emeritus 
at the University of Texas at San Antonio for his valuable comments and guidance in 
the process of working on this article.



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 447

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Date of submission: 21.12.2020  Date of acceptance: 28.07.2021

Asiya Validova (). University of Texas at San Antonio. San Antonio, United States. 
E-mail: asiya.validova@utsa.edu



Pronatalist Policies and Fertility in Russia: Estimating Tempo and Quantum Effects    • 451

Appendix 

A1 Description of the pronatalist policy measures introduced in Russia 
in 2007

1) Increase in pregnancy, birth and child allowance. The law extended monthly child 
benefi ts to nonworking women (previously, they were available only to working 
women on maternity leave). In addition, the birth related allowance included a 
payment for pregnant women who register during early pregnancy at a local 
medical practice, and a lump-sum payment at the time of childbirth.

2) Provision of favourable parental leave conditions (in terms of remuneration 
and leave length). The leave included maternal leave (70 calendar days before 
and after childbirth with payment of 100 percent of average earnings for 12 
months prior to leave, with a set ceiling for all employed women, and also a 
small payment for unemployed), paid parental leave until the child reaches 18 
months (40 percent of average earnings with a ceiling and minimum payment), 
and unpaid leave until the child reaches 36 months.

3) “Maternal (family) capital”. The maternal capital is provided to mothers of second 
and higher order births and is given as a certifi cate and may not be transformed 
into cash. At the time of its introduction in 2007, the sum was 250,000 roubles 
($9,423). Until 2015 the payment was annually adjusted for infl ation, but in 2016 
this adjustment was cancelled. Within the ten-year period from 2007 to 2017 
the amount of “maternal capital” grew from 250,000 roubles ($9,423) to 453,000 
roubles ($7,530), representing an 81.2 percent increase in national currency. 
However, it should be noted that the price level increased by 139.6 percent within 
the period, as the Russian rouble also lost substantially in value throughout the 
last decade. 
“Maternal capital” can be used for several purposes listed in the Federal Law 
upon a child reaching the age of 3 (except for housing conditions improvements 
where the baby-bonus can be used immediately); as a result, the fi rst payments 
were initiated in 2010. More specifi cally, maternal capital can be used for the 
following purposes:

• acquisition of housing or improvement of living conditions (buying an 
apartment, building a house, paying a mortgage, undertaking basic repairs 
and renovation etc); 

• children’s education (money can be used to pay for the education of all 
children in the family in different forms); 

• mother’s retirement (by investing the maternal capital money in the private 
pension fund to increase the level of the mother’s retirement income).

4) Child-care subsidies and tax benefi ts. Subsidies for child-care services (for state 
child-care institutions) were differentiated by birth order: while the federal budget 
covered 20 percent of the preschool fee for the fi rst child, the subsidy increased 



•    Asiya Validova452

to 50 percent for the second and 70 percent for the third and subsequent 
children. Tax deductions (effective from 2012) for parents were insignifi cant, 
with the exception of tax credits for parents of children with disabilities.

A2 Gender and Generation Survey 2011 – Graphical Analysis

A question in the Gender and Generation Survey 2011 was specifi cally designed 
to decompose the timing and quantum effects of the family policy of 2007. The 
exact nature of the policy is not specifi ed in this questionnaire, the question is 
rather broad and captures the respondents’ (both male and female respondents are 
sampled) perceptions of the effect of the policy on their decision to have children 
(without specifi cation of a child order). However, the design allows a choice of three 
possible answers to the question of whether the family policy affected their decision 
to have another child: 1) positive effect, had a child earlier than planned (timing 
effect); 2) positive effect, had a child that was not initially planned (quantum effect); 
3) no effect, had our children as planned. Across all age groups, the percentage 
of respondents who stated that they had a child that was not planned prior to the 
demographic policy was larger than that of women who had their child earlier than 
planned. Older respondents demonstrated a clearer dominance of the quantum 
effect over the timing effect, but the overall positive effect of the government’s 
measures was reported by a very low percentage of respondents (Fig. A1).

Fig. A1: Did the introduction of demographic policy in 2007 affect your decision 
to have additional child?

0

10

20

30

40

50

60

70

80

90

100

20-24 25-29 30-34 35-39 40-45

Age group

Positive effect, had a child earlier than planned

Positive effect, had a child that was not initially planned

No effect, had children as planned

in percent

Source: Own calculations from Gender and Generation Survey Russia 2011



Published by
Prof. Dr. Norbert F. Schneider

Federal Institute for Population Research 
D-65180 Wiesbaden / Germany

 2021

Managing Editor 
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Dr. Katrin Schiefer

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Layout
Beatriz Feiler-Fuchs

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Marc Luy (Vienna) 
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Zsolt Spéder (Budapest)
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www.comparativepopulationstudies.de

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