Associate Professor, Netherlands Interdisciplinary Demographic Institute: Rotterdam, Zuid-Holland
t.e.emery@essb.eur.nl
Senior Researcher, Leiden University: Leiden, South Holland, Netherlands
a.bartova@law.leidenuniv.n
This article examines the realisation of short-term fertility intentions in the Republic of Moldova using longitudinal data from the Generations and Gender Survey (GGS) Waves 1 (2020) and 2 (2023). Moldova faces persistent below-replacement fertility and high emigration rates, yet survey evidence indicates that individuals’ desired number of children exceeds the actual number of births. By tracking approximately 650 respondents who intended to have a child within three years in 2020, we assess the extent to which these intentions were realised and identify the factors that facilitated or hindered their realisation. Descriptive and multivariate analyses reveal that only about one-third of intended births occurred within the observation period, reflecting significant unmet reproductive intentions. Partnership status, age, parity, and subjective well-being emerge as key determinants of successful realisation, while economic insecurity, unstable relationships, and migration contribute to unmet plans. The analysis also contextualises these outcomes within Moldova’s recent social and economic shocks as well as major family policy reforms adopted in 2022, which aim to strengthen parental leave, childcare availability, and financial protection around childbirth. The findings highlight substantial structural barriers that limit individuals’ ability to achieve their reproductive goals and underscore the need for sustained policies that enhance family support, gender equality, and economic stability, helping align fertility outcomes with citizens’ aspirations.
Moldova is experiencing sustained low fertility and population decline, driven by both declining birth rates and high emigration rates. The total fertility rate (TFR) has fluctuated around low levels in recent decades. Figure 1 shows that after a modest rise to nearly 1.8–1.9 in the mid-2010s, the TFR has fallen back to about 1.6 in 2023, well below the replacement level of 2.1. This persistent below-replacement fertility, coupled with significant out-migration of young adults, has led to a natural population decline and rapid population ageing (Gagauz et al., 2024).
In contrast, survey data indicate that desired fertility in Moldova remains higher than what is achieved. Many individuals, especially young people, express intentions to have children, despite actual birth rates remaining low. This gap between aspirations and outcomes is a central concern for policymakers and human rights advocates alike, and is something observed in many countries around the world (Badolato, 2025). It suggests that Moldovans may be having fewer children than they would like due to various constraints.
In this context, the Generations and Gender Survey (GGS) provides crucial longitudinal data to examine fertility intentions (i.e. the plans or desires to have a child) and their realisation (actual childbearing). This report analyses GGS Wave 1 (2020) and Wave 2 (2023) data for the Republic of Moldova to assess the extent to which individuals who intended to have a child within three years in 2020 had a birth by 2023. The analysis is grounded in a formal, evidence-based approach suitable for UNFPA and policy stakeholders. We identify key demographic, economic, and social factors influencing the realisation of fertility intentions, and we discuss recent policy measures that may affect these outcomes. The goal is to inform policies that enable people to achieve their reproductive intentions in line with their rights and aspirations, thus bridging the gap between intended and actual fertility.
Fertility decision-making is a complex process shaped by individual desires, partnership dynamics, and broader structural conditions. An expressed fertility intention (such as planning to have a child in the next three years) reflects a respondent’s current aspirations given their life situation (Ajzen & Klobas, 2013). However, not all intentions are realised, due to factors that can either facilitate or impede the move from intention to birth. Prior research in Europe has demonstrated that age, parity, marital/partnership status, and socioeconomic resources are key determinants of whether fertility goals are achieved (Spéder & Bálint, 2024). These factors operate within a life-course and human-rights framework, embodied in the Sustainable Development Goals.
Age is fundamental – there is a biologically limited window for childbearing and strong age norms regarding the timing of parenthood. Very young adults may intend children but postpone until completing education or establishing careers (Neels et al., 2017) whereas older individuals nearing the end of reproductive age may face declining fecundity or health barriers (Shreffler et al, 2016). Number of children also influences realization of fertility intentions: those who are childless or have fewer children might be more strongly motivated to have a child, whereas those who already have two or more children might be more likely to abandon further childbearing plans (Kocourková & Šťastná, 2021). The stability and quality of a partnership is another key factor – having a co-resident partner or spouse greatly increases the likelihood of a planned birth (Rutigliano & Esping-Andersen, 2018). Conversely, being single or experiencing a breakup in a union can derail previously stated intentions, as having a child outside a stable partnership is less common or more challenging (Thomson et al., 2012). Moreover, the gender dynamics within couples matter: if the division of childcare and housework is perceived as fair and supportive (i.e., a more egalitarian sharing of responsibilities), couples may be more likely to follow through on their childbearing plans (Riederer et al., 2019).
Economic conditions often act as either enablers or obstacles to having children. Sufficient income, stable employment, and housing security can give individuals the confidence to proceed with childbearing, whereas economic insecurity or job instability may lead to postponement of intended births (Matera et al., 2023). Access to supportive services—such as childcare, healthcare, and parental leave—also plays a critical role. If affordable childcare is available, the perceived opportunity costs of having a child are lower, making it more feasible to act on fertility intentions. In Moldova, reliance on informal support, such as family networks, for childcare has traditionally been high (Buciuceanu-Vrabie & Grigoraș, 2023); lacking such support or having only expensive formal childcare can hinder the realization of intended children. Broader contextual factors, including cultural norms and the policy environment, also influence these decisions.
Moldova’s social and economic context between 2020 and 2023 was significantly impacted by the COVID-19 pandemic and, in 2022, the spillover effects of the war in neighbouring Ukraine, both of which caused economic uncertainty and stress that could affect family plans. Importantly, Moldova has been addressing demographic challenges through new policies (see section 6) aimed at creating a more favourable environment for family formation. High levels of emigration among reproductive-aged adults represent a critical contextual factor as many young Moldovans leave the country for work or better living conditions, which can delay or prevent local childbearing. A recent analysis revealed that a substantial proportion of those planning to emigrate are young individuals who wish to have children (Ivan & Tabac, 2022). This “brain and cradle drain” means that even strong fertility intentions might not translate into births within the country if people move abroad.
This analysis uses data from the Generations and Gender Survey (GGS) in the Republic of Moldova, a nationally representative longitudinal survey on family dynamics and population processes. Wave 1 was conducted in 2020 (with 10,074 respondents), and Wave 2 in 2023, providing a three-year follow-up. The focus of this report is on respondents of reproductive age who, at Wave 1, indicated an intention to have a child within the next three years. In Wave 1, respondents were asked, “Do you intend to have a child in the next three years?” with response options ranging from definitely not to definitely yes. We define as “intenders” those who answered affirmatively (either “probably yes” or “definitely yes”) to that question in 2020. There were roughly 650 individuals in this category at Wave 1. We then track these same individuals in Wave 2 (2023) to determine whether they realised their fertility intention, i.e., had a live birth in the interim period. Births were recorded, along with their dates and other outcomes, during the interview at wave 2.
For the analytical strategy, we combine descriptive statistics (cross-tabulations and figures) with multivariate analysis. We first present descriptive findings on fertility intentions and their realisation rates by various characteristics (gender, age, parity, employment status, etc.). Then, to assess the determinants while controlling for other factors, we estimate a logistic regression model predicting the odds of having had a child by Wave 2 among those who intended to have one at Wave 1. The regression includes key independent variables measured at Wave 1 (e.g., age, gender, partnership status, number of children, socio-economic indicators) and some time-varying factors. We report both logistic coefficients and average marginal effects (AMEs) to aid interpretation. All analysis is conducted in a longitudinal (panel) framework, meaning we explicitly examine changes between 2020 and 2023 for the same individuals.
It should be noted that attrition between waves (e.g., due to migration or non-response) can affect the sample. Most Wave 1 respondents (10,074) were re-interviewed in Wave 2 (5,599, 55.6%), but those who could not be followed (including some who may have left the country) are not represented in the realised outcomes. This could lead to a slight underestimation of realisation rates if, for instance, some intenders moved abroad and had children there (unrecorded by the survey) or an overestimation if those who dropped out were less likely to have children. We proceed with the assumption that attrition bias is minimal, while acknowledging this limitation. The data have been weighted to adjust for sampling design and non-response where appropriate.
Finally, to contextualise the findings, we incorporate information on relevant policy changes and conditions in Moldova between 2020 and 2023. In particular, three major family policy reforms (Laws No. 195, 367, and 353 of 2022) were enacted after Wave 1. While the full impact of these laws might not yet be observable in Wave 2, given the short interval, our analysis considers their potential influence on the environment for the realisation of fertility intentions.
We now turn to the core question: to what extent were the intentions expressed in 2020 actually realised by 2023, and which groups were more successful in this regard? Overall, the data reveal a significant gap between intentions and outcomes. Of the ~650 individuals who intended to have a child within 3 years, only about one-third had a child by the time of the Wave 2 survey. In other words, roughly 35% of the short-term fertility intentions were realised, while the remaining ~65% were unrealised within the three-year window. This headline finding confirms that many intended births did not occur as planned, reflecting a considerable unmet realisation of fertility intentions.
It is also informative to look at all births that occurred in the period and see what fraction of them were to those who had intended versus those who had not intended to have a child. Figure 2 - ‘Births between Wave 1 and Wave 2, by prior intention status’ illustrates the timing and composition of births from 2020 to 2023. Each month’s births are separated into two groups: births to respondents who had reported an intention at Wave 1 (“intended births”), and births to those who had said they were not planning a child in that period (“unintended or unplanned births” from the perspective of Wave 1 intentions).
We observe that intended births (dark segments) accounted for the majority of new children born to the panel respondents; however, a substantial share of births occurred to those who initially did not intend to have a child or at least not within the specified time period. This indicates that fertility behaviour is dynamic: some people who did not plan a child ended up having one (perhaps due to changed circumstances or unplanned pregnancies), contributing to the overall birth count. There is a noticeable peak in births around mid-2022, which could reflect timing preferences (as some plans materialise after the pandemic peaked) or policy effects (since new family policies were introduced in 2022, potentially encouraging births). Nonetheless, the consistent presence of “unintended” births highlights that not all fertility is premeditated within a three-year span – life events and surprises also play a role.
The number of unintended births peaks in the period immediately following the survey. This is primarily due to the structure of the question on intentions, which is only asked to those who are not knowingly pregnant. If a respondent was three months pregnant at the time of the wave 1 interview and didn’t know it, you are much more likely not to have intended to have a child. Otherwise, intended births generally outnumbered unintended births in any month.
Figure 3. Fertility intentions at Wave 1 (2020) and Wave 2 (2023) show the distribution of responses to the question about intending to have a child within 3 years for those who were asked the question at two time points. This figure does not accurately reflect the fertility intentions of the population as a whole. It excludes those who are pregnant, infertile, or over the age of 50 for women. At Wave 1, a considerable share of respondents who were asked reported positive short-term fertility intentions: approximately 32% said they “definitely” or “probably” intended to have a child within three years (14% definitely, 18% probably). Around 33% of those asked the intention question replied “definitely not,” with others responding as “probably not” or being unsure. This confirms that fertility intentions were relatively high, consistent with the notion that many Moldovan adults desire children (eventually, most want two children over their lifetime, on average).
By Wave 2 in 2023, the distribution of intentions had shifted. Those who had, in the meantime, become parents (realising their intention) were no longer “in the market” for an immediate new birth, and some others who did not have a child had grown more pessimistic about near-term childbearing. The proportion saying “definitely not” intending to have a child in the next three years rose to 43% (up from 33%). Conversely, the combined share saying “definitely yes” or “probably yes” dropped to about 25%. In fact, only 10% of respondents in 2023 still said “definitely yes” to having a child in the next three years – a decline from the 14% in 2020. This suggests that within three years, many of those who initially planned a child either fulfilled their plan or changed their mind (often not to plan one soon). The increase in definite negations could include individuals who have achieved their desired family size with a birth between waves and thus do not intend to have another soon, as well as those whose circumstances have worsened or who have aged into a stage where they no longer plan to have children.
The overall one-third realisation rate masks considerable heterogeneity across individual characteristics. We examine realisation rates (% of intenders who had a child) across key demographic and socio-economic groups.
There is a notable gender difference in outcomes. Figure 4. ‘Fertility intention realisation by gender’ shows that female respondents were more likely to have realised their intention than male respondents. About 40% of women who intended to have a child succeeded in doing so within three years, compared to roughly 30% of men. This gap (40% vs. 30%) suggests that women’s short-term fertility plans were more often actualised than those of men.
One possible reason could be that women have more direct biological urgency and reproductive agency; another factor is that a man’s intention can only be realised if he finds a partner willing and able to conceive – if an intended father was not in a stable partnership, his plan may have fallen through. Meanwhile, many women who intended to have a child were able to follow through, possibly because they secured the necessary conditions or because, in a couple, the woman’s intention often drives the decision to actually become pregnant.
The likelihood of realising an intention varied non-linearly with how many children a person already had. Figure 5. Fertility intention realisation by parity shows that respondents who had one child at baseline were the most likely to fulfil their intention for another child: nearly 46% of intenders with one child had a new baby by 2023. In contrast, those who were childless in 2020 had a lower realisation rate, about 31%, and those who already had two or more children also realised about 31% of their intentions. This pattern suggests that intending to have a second child was, in this timeframe, more likely to result in a birth than intending to have a first or a third child. A possible explanation for this outcome could be that individuals with one child may have a strong motivation to have a sibling for their child and also possess the experience of parenthood that helps them navigate caring for another child. Many likely timed their second birth soon after the first, which is a common spacing strategy. By contrast, childless intenders include many young people who may have faced obstacles (finding a partner, establishing careers) or ended up delaying their first birth beyond the three-year window. Those with multiple children might have intended another but perhaps found that realities (financial or time constraints) prevented adding to an already large family. However, the descriptive data underline that the jump from parity 0 to 1 is a critical hurdle—one that a minority of intenders clear in a short period—whereas parity 1 to 2 seems, for many, quite attainable in three years (likely reflecting many second-birth plans).
Age is a pivotal factor for fertility timing. Figure 6 indicates an inverted U-shaped relationship between age and realisation chances. Generally, young adults in their late teens or early twenties had relatively low realisation rates—many in this group intended to have a child but did not actually have one by their mid-twenties, possibly due to continuing education or a lack of economic readiness. Mid-career adults in their mid-to-late 20s to early 30s showed higher realisation rates, as this age is often optimal for childbearing (in terms of biology and typical life course timing). Older individuals (mid-30s and above) who stated an intention to have a child in the next three years were somewhat less likely to realise their intentions by 2023. This aligns with biological reality: the youngest intenders might “overshoot” and delay, whereas the oldest intenders may have overestimated their ability to have a child still. For example, a 40-year-old intending to have a child might have only a small chance to achieve it in three years due to reduced fertility. Alternatively, this age category is likely to contain a higher share of parents with two or more children, which was found to be associated with a lower likelihood of fertility intention realisation (see Figure 5).
Economic activity can influence the feasibility of childbearing. Somewhat surprisingly, our data do not show a strong advantage in realisation for those who were employed. In fact, Figure 7. Fertility intention realisation by employment status at Wave 1 reveals a slightly higher realisation rate among those who were not employed at baseline (approximately 32%) compared to those who were employed (28%). This difference is modest and not statistically significant, but its direction is notable.
One might expect that having a job (and thus income security) would facilitate having a child. However, the result may reflect that those not employed include individuals who were intentionally out of the labour force to focus on family (e.g., homemakers or students who planned to start families soon). For example, some women who were not working might have been doing so in preparation for pregnancy or due to being on extended leave; thus, they were in a position to realise their fertility plans. To examine this, we also provide the results by gender, and the difference between genders is indeed larger for the non-employed than employed.
It’s possible that many unemployed female intenders had partners with income or other support, enabling them to proceed with childbearing. In any case, being employed at Wave 1 did not confer a clear advantage in achieving a birth by Wave 2. This finding underscores that job conditions and leave policies (or the lack thereof) can complicate the translation of intentions into reality. It also resonates with the regression outcome where “employed status” had no significant effect on realization (odds ratio not distinguishable from 1).
Digging deeper into economic security, the GGS asked employed respondents about their perceived risk of losing their job (job loss risk). Figure 8. The realisation of fertility intentions in response to perceived job-loss risk (job stability) exhibits a striking non-linear pattern. Respondents who felt very secure in their jobs (“very unlikely” to lose a job) realised about 40% of their intended births, which is relatively high. Those who said job loss was “unlikely” had a lower realisation rate (around 30%). Interestingly, those who were uncertain about their job stability (“unsure” if they might lose it) actually had a fairly high realisation (~45%).
On the other hand, people who perceived a likely risk of job loss had a very low realisation rate (only ~15–20%), suggesting that moderate insecurity strongly deterred or prevented them from fulfilling their fertility plans. However, those who said it was “very likely” they would lose their job showed the highest realisation rate of all (exceeding 50%, though based on a small sample, with wide confidence intervals). This U-shaped relationship implies that both high security and very high insecurity were associated with higher chances of having the intended child, while moderate insecurity was associated with the lowest chances.
One possible interpretation is that individuals with very secure jobs felt confident enough to proceed with childbearing (no fear of losing income), whereas those with very insecure jobs may have pre-emptively decided to have a child perhaps because they expected to exit the workforce anyway or wanted to take advantage of existing job protection (for example, a woman who suspects she’ll be laid off might choose to become pregnant and secure maternity benefits before that happens). Meanwhile, those with mild to moderate concerns about job stability may have opted to postpone having a child, waiting for a more stable moment that perhaps did not arrive in time. These results highlight the nuanced ways in which economic uncertainty can influence reproductive behaviour. In essence, clear certainty or clear uncertainty might spur action, while in-between uncertainty breeds caution. It also highlights that policies that ensure job security and protection during childbearing (e.g., protection from dismissal and good maternity leave benefits even in unstable jobs) could help those who are undecided.
Figure 9 examines whether education (e.g., secondary vs higher education) affected realisation. After controlling for age and other factors, education level did not show a significant independent effect on the likelihood of having a child. This suggests that once we account for age (which correlates with education) and employment, differences between, say, those with university degrees and those with lower education in fulfilling fertility plans are minimal.
There might be offsetting influences: higher-educated individuals often delay childbearing (which could lower short-term realisation), but they also tend to have more resources and knowledge to plan births (which could increase realisation if they intend). In our data, these forces appear to balance out. Thus, we did not find a strong direct education gradient in intention realisation. The implication is that policies should address structural factors (such as income, job support) that cut across educational groups rather than assuming that, for example, less-educated people simply follow through more or vice versa.
In summary, the realisation of fertility intentions in Moldova from 2020 to 2023 was limited (approximately one-third overall) and uneven across different groups. Women, those in their late 20s, and those with one prior child had comparatively higher success rates in achieving a birth, whereas men, those very young or older, and those with no prior children or with multiple children had lower success rates. Socio-economic stability played a role: having a secure partnership was crucial (as we will see quantitatively in the next section), and job security had a complex influence.
Many who failed to realise their plans by 2023 still expressed intentions for the future, as evidenced by the fact that among those who did not have the child they intended, a significant share in Wave 2 said they still intend to have a child eventually (while others resigned to “definitely not”). Figure 10. Wave 2 fertility intentions by whether Wave 1 intention was realised illustrate this point. Those who realized their Wave 1 intention (they had a child) mostly did not plan another child in the immediate future – over 40% of them now say “definitely not” to a new child in the next 3 years (having just had one), and only a small minority (perhaps around 12%) say they definitely or probably intend another soon. In contrast, among those who did not realize their initial intention, many remain oriented toward trying in the near future: nearly half express some positive intention (about 21% definitely yes, 25% probably yes), and only one-third say “definitely not” (20%) or “probably not” (13%) for the next 3 years (the rest ~22% are unsure).
This indicates a pattern of postponement rather than abandonment for many; they still want a child, just not accomplished yet. However, some have likely become discouraged or faced new obstacles, which has led to a rise in definite “no” responses in that group as well. Overall, the descriptive findings highlight key barriers and facilitators that we will explore in a more rigorous, multivariate manner next.
To quantify the influence of various factors on the likelihood of realising a fertility intention, we estimated logistic regression models. Table 1 presents the detailed results of three models; here, we discuss the key findings from the most comprehensive specification. In addition, Figure 11. The following visualises the average marginal effects of selected predictors on the probability of having had a child (realising the intention) by Wave 2. These results help identify which conditions significantly increase or decrease the chances of fulfilling a fertility plan, holding other factors constant.
The regression confirmed many patterns noted descriptively, while controlling for interrelated factors:
The most decisive factor was having a partner at Wave 1. Being married or cohabiting in 2020 dramatically raised the odds of realising the intention by 2023. The logistic coefficient for being partnered was 1.18 (Model 2), corresponding to roughly 3.2 times higher odds of having a child compared to being single, while holding other factors equal. In terms of marginal effects, this translates to about a +20-percentage point higher probability of realisation for those who had a partner at baseline (Figure 10).
Consistent with the parity-specific rates, having more children at Wave 1 significantly reduced the likelihood of another birth. Each additional child one already had was associated with a lower chance of realising an intention for an additional child (coef = -0.29 in Model 2, p<.01). For example, a person with two children in 2020 was much less likely to have their intended third child than a person with no children was to have their intended first. This effect likely reflects both declining desire for very large families and greater practical constraints (time, financial, housing) with each additional child. It is noteworthy, however, that the descriptive analysis showed the highest realization at parity 1; the linear parity effect in the regression captures the overall downward trend but may understate the unique challenge of going from 0 to 1 child.
Age is represented in the model as both a linear and a quadratic term, indicating a significant non-linear relationship. The positive coefficient on age and negative on age² indicate increasing odds of realisation up to a certain age, then decreasing at older ages (in line with the earlier discussion). The estimated turning point is around the late 20s. For example, going from age 20 to 30 (holding other factors constant) substantially increases the probability of realising the intention (peaking around 28–30), but beyond the early 30s, further ageing starts to reduce the probability. In Model 2, age had a strong positive effect (coef ~0.30 per year, p<.001) and age² had a small negative effect. By Model 3 (with partner-only sample), age effects became statistically insignificant (perhaps due to a narrower age range or collinearity), but the qualitative pattern remains. In sum, mid-reproductive ages (late 20s to early 30s) are the most favourable for intention fulfilment, whereas being very young or older reduces the chances, all else being equal.
Interestingly, once controlling for partnership and other factors, the gender effect diminished and lost significance. In Model 2 (full sample controlling for partnership), the coefficient for being female (relative to male) was a negligible -0.05 (not significant). This suggests that the raw gender gap we observed (women’s higher success) can be explained by other variables; for instance, women intenders were more likely to be in a partnership or to have different age profiles than male intenders. When restricted to partnered individuals (Model 3), the coefficient for women became significantly negative (-0.45, p < .05), suggesting that, among those in a union, women may have been slightly less likely than their male counterparts to experience the intended birth. This could be due to health or biological issues (some women may have had difficulty conceiving even with intent), or perhaps because some partnered women changed their minds or faced career constraints, whereas if the man was intent and in partnership, the couple proceeded (this is speculative). The main conclusion is that gender, in itself, is not a robust predictor once circumstances are taken into account.
We included multiple measures of the economic and housing situation. Employment status at Wave 1 had a negative coefficient (indicating that employed people were less likely to realise than non-employed individuals), but it was not statistically significant, echoing the descriptive finding of little difference. Home ownership and housing space (in terms of the number of rooms) were also not significant predictors. These might serve as proxies for economic stability; their null effects suggest that, within this relatively short period, those factors did not directly constrain the decision to have a child, or it could be that nearly all intenders had adequate housing, such that variation in housing was not a critical factor. Housing satisfaction was similarly insignificant.
One economic indicator that did show significance was overall life satisfaction (a 1–10 scale of subjective well-being). Higher life satisfaction at Wave 1 was associated with a slightly higher probability of realising the fertility intention (coef ~0.13 per point, p<.01 in Model 2). This implies that individuals who felt happier and more content with their lives were more likely to follow through on their plans to have a child. Life satisfaction can capture optimism, stress levels, and perhaps unobserved advantages; those who are generally satisfied may have more substantial social support or personal efficacy that help achieve life goals, including childbearing. The marginal effect is modest (on the order of a few percentage points per additional point of life satisfaction), but it is meaningful: for example, someone who rated their life satisfaction 9/10 had a higher chance of fulfilling their intention than someone who rated it 5/10, controlling for objective factors. This finding aligns with a broader concept that emotional well-being and confidence are essential for assuming the responsibility of a child.
We attempted to capture major life changes through a variable for “moved between waves” (indicating a change of residence, possibly abroad or to another location). This variable had no significant effect. Its coefficient was positive in Model 2 but very imprecise, and in Model 3 (partnered sample), it was almost zero. The lack of significance is likely because moving is a highly heterogeneous category – some moves may be for better housing (which could encourage childbearing), while others may be due to emigration or disruptive moves (which could inhibit childbearing). Given the available data, we cannot definitively determine how migration influenced the realisation. However, since some potential parents undoubtedly migrated and did not have a child in Moldova (and thus counted as “not realised” in our data), one could infer that out-migration contributed to some of the unrealised intentions. Broader evidence confirms that the departure of many potential parents from the country results in a loss of births domestically.
In Model 3, limited to those who had a partner at baseline, we added relationship satisfaction and an index of household task egalitarianism. Higher relationship satisfaction had a positive effect on realisation (coef = 0.22, p<.05). This means that among couples, those who reported being more satisfied with their partner relationship were more likely to follow through with having a child. A supportive, harmonious relationship likely fosters the mutual decision and readiness to have a baby. This is important from a gender and rights perspective: it’s not just being partnered, but the quality of that partnership that matters for jointly undertaking parenthood. On the other hand, the egalitarian division of housework index (where a higher value presumably means a more equal sharing of domestic duties) did not show a significant impact (coef = -0.03, n.s.). The negative sign is counterintuitive.
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| Age | 0.388*** (0.103) | 0.305*** (0.112) | 0.228 (0.168) |
| Age squared | -0.007*** (0.002) | -0.005*** (0.002) | -0.004* (0.003) |
| Gender | 0.284 (0.178) | 0.104 (0.197) | -0.236 (0.256) |
| Employed at wave 1 | -0.169 (0.200) | -0.259 (0.248) | |
| Partnered at wave 1 | 1.030*** (0.257) | 0.000 (.) | |
| Number of biological children at Wave 1 | -0.130 (0.124) | -0.372** (0.150) | |
| Owns their own home at wave 1 | -0.075 (0.220) | -0.225 (0.279) | |
| Number of rooms in the house | 0.034 (0.075) | -0.033 (0.094) | |
| Satisfied with housing | 0.015 (0.054) | -0.039 (0.069) | |
| Life satisfaction (1–10) | 0.126** (0.059) | 0.112 (0.078) | |
| Moved between waves | 0.149 (0.718) | -0.298 (1.069) | |
| Relationship satisfaction (1–10) | 0.217* (0.124) | ||
| Household Task Egalitarianism Index | -0.120 (0.244) | ||
| Constant | -6.195*** (1.577) | -6.650*** (1.936) | -3.843 (3.265) |
| Observations | 632 | 631 | 390 |
Realisation of fertility intentions does not occur in a vacuum; it is embedded in Moldova’s broader socio-economic and policy context. The period between GGS Wave 1 (2020) and Wave 2 (2023) saw significant upheavals and also major policy responses aimed at supporting families. In this section, we outline the key contextual challenges and the policy measures in place or recently implemented, examining how they relate to the determinants identified above.
Moldova faced persistent economic insecurity in the early 2020s. Many young people grapple with low incomes, job instability, and limited opportunities, which in turn affect decisions about starting or expanding a family. The COVID-19 pandemic (2020–2021) exacerbated uncertainty – some couples may have delayed childbearing due to health concerns or economic setbacks during the lockdowns. In 2022, the war in Ukraine led to an influx of refugees and economic ripple effects (energy price spikes, inflation) in Moldova, further straining household budgets. These crises likely contributed to some fertility plans being postponed. The GGS data indeed suggest that those with concerns about job security had lower realisation, and more people became pessimistic by 2023 about near-term childbearing.
As noted, youth emigration is a critical factor. Moldova has one of the highest emigration rates in Europe, with many working-age individuals (including would-be parents) moving abroad (Tabac, 2021). Moreover, it is often the more educated and economically active young families – precisely those who might intend children- who are more likely to emigrate. This represents a loss of “reproductive potential” for the country. If an intending couple migrates, they may either delay having children until they are settled abroad or have children outside Moldova (which would not be counted as realised in our data). Thus, out-migration may be one of the structural reasons that account for some of the gap between intentions and local realisations. Tackling the root causes of migration (improving jobs, governance, services) is beyond the scope of family or reconciliation policy per se, but it is central to demographic resilience. Indeed, experts emphasise that improving the quality of life and opportunities in Moldova would encourage young people to “stay and create families in the Republic of Moldova,” thereby bolstering fertility.
The rise in divorce and later marriage ages means some people do not have a stable partner when they initially intend a child, leading to non-realisation. The GGS showed that partnership status was paramount. Encouraging and supporting family formation could involve policies such as housing support for newlyweds, relationship education, or facilitating access to reproductive technologies for singles who wish to have children (although this is not currently common in Moldova). Culturally, non-marital childbearing is not rare in Moldova, but usually a cohabiting or married partnership is still the norm for childbearing. Hence, interventions that support couples or reduce financial barriers to maintaining a relationship could indirectly increase fertility realisation.
Historically, access to affordable childcare and flexible work arrangements has been limited in Moldova. Formal daycare for children under the age of 3 is scarce, prompting parents to rely on grandparents or private babysitters. Maternity and parental leave have been generous in length (up to the child’s 3rd birthday), but this can also lead to long career breaks for mothers and to employers’ hesitancy to hire women of childbearing age. Even though women can work whilst also receiving parental leave benefits since 2023, the insufficient number of childcare services for children under 3 years of age is likely to still represent a barrier to maternal employment. There were also gaps in engaging fathers in caregiving, and policies did not strongly incentivise paternal leave uptake. These issues create a context in which having a child can impose high costs on a family, potentially deterring those who intend to have one but are unsure if they can manage.
Recognising these challenges, the Moldovan government, with support from UNFPA and others, introduced several family-friendly policy reforms in 2022. Notably, three laws were passed to support fertility intention realisation:
Together, these three laws (195, 367, 353) represent a considerable pro-family policy package designed to create a more supportive environment for young couples to have children and better combine parenthood with paid work. They address some of the precise barriers our analysis identified: e.g., job insecurity (Law 195 gives flexibility and encourages sharing leave, which could reduce employer discrimination and mothers’ career costs), childcare access (Law 367 helps after the baby is born), and financial concerns (Law 353 bolsters maternity pay). These reforms came into effect primarily in late 2022 and 2023, meaning that by the time of GGS Wave 2, their effects might just be starting to take hold.
The Generations and Gender Survey data from Moldova convey a clear message: while many Moldovans desire to have children, a range of obstacles – including economic uncertainties, a lack of a partner, insufficient support systems, and broader societal challenges – often prevent those intentions from becoming a reality within the expected timeframe. Between 2020 and 2023, only about one in three intended births actually occurred among those who said they planned to have a child. This highlights a significant unmet need for support in reproductive decision-making. Women in stable, satisfying partnerships and in their late 20s were most likely to succeed in having the child they intended, whereas those lacking a partner or facing insecurity often had to defer their plans.
The policy implications are profound. To close the gap between desired and realised fertility, Moldova is taking steps in the right direction by implementing progressive family policies that promote work-family balance, gender equality, and financial support for childbearing. These measures, along with broader improvements in living standards and opportunities for youth, will be crucial in creating an environment where individuals can exercise their reproductive choices freely and confidently. It’s not about encouraging people to have more children than they want, but about empowering them to have the children they do want.
In closing, aligning Moldova’s fertility outcomes with its citizens’ intentions is an important component of the country’s demographic resilience and human development strategy. It requires sustaining momentum on policy reforms, investing in youth and families, and continuously engaging with data and research (such as the GGS) to evaluate progress. If successful, Moldova can ensure that no one is forced to relinquish their dream of parenthood due to remediable barriers, and in doing so, the country can gently raise its fertility level in line with the genuine aspirations of its people. This will help mitigate population decline while upholding the rights and welfare of individuals and families. The path forward is one of enabling choices: a Moldova where individuals who plan to have a child can welcome that child into the world with the support of their community and the state.
This paper was prepared under contract for the United Nations Population Fund (UNFPA).
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Conceptualisation: Alzbeta Bartova, Thomas Emery
Methodology: Alzbeta Bartova, Thomas Emery
Formal analysis: Thomas Emery
Investigation: Thomas Emery
Writing – original draft: Alzbeta Bartova, Thomas Emery
Writing – review & editing: Alzbeta Bartova, Thomas Emery
Supervision: Thomas Emery
Project administration: Thomas Emery