Changing migration patterns and their relevance to population aging and monetary policy in Europe (OeNB Bulletin Q2/25)

Julian Mayrhuber, Anna Katharina Raggl, Paul Ramskogler ¹

JEL classification: F22, F60, J11

Keywords: international migration, population aging, (de)globalization, diversification of migrants

1 Introduction

Geopolitics have always played – and will continue to play – a critical role in shaping and directing migration patterns. After World War II, for instance, the Iron Curtain effectively curtailed significant east-to-west migration for decades. More recently, strong migrant flows along these same corridors have been described as being “weaponized” against the West by countries such as Russia (e.g. Gryzwaczewski, 2021). Meanwhile, emerging challenges like the climate crisis are creating new migration movements, or at least intensifying migration pressures, along certain routes. At the same time, other routes are closing; for instance, Russia's invasion of Ukraine has largely disrupted migration flows from Russia to Europe. Also, the patterns of global migration have changed over the decades (Fitter et al., 2024), and this feeds also into the forces driving changes in migration flows.

However, a crucial problem that researches encounter when trying to analyze bilateral migration patterns is the limited (public) data availability of bilateral age-related data. While data on bilateral migration stocks are sometimes available (at least in industrialized countries with reporting obligations), they are rarely readily accessible. There are large country groups for which there are no data available at all. Thus, the key contribution of this paper is to derive a simple method that can be easily applied to readily available UN data and used for cross-country research. ²

In order to prove the relevance of this question to monetary policy, we will start with a thorough literature review on the link between migration and inflation. The relationship between monetary policy and migration is complex, involving multiple channels. On the one hand, migration impacts demographic trends, which in turn influence aging-related pressures on inflation. However, this is not the only mechanism at play. For example, migration can affect wage bargaining, labor supply and demand, which may influence inflation dynamics. The effects of migration on inflation, particularly at business-cycle frequencies, could vary, depending on these additional factors. We then take a step-by-step approach to look at key insights from the literature on the different channels between inflation and migration. Firstly, we look at the impact migration tends to have on the demographic structure of destination countries. Secondly, we look at the link between population aging and inflation in order to arrive, thirdly, at the link between migration and inflation. In the second part of the paper, we demonstrate the demographic relevance of migration by calculating and comparing zero-migration old-age dependency ratio counterfactuals. We will show that migration indeed slows down population aging quite significantly but is still far from reversing population aging altogether. Finally, to be able to show which origin countries contribute most to the reduction in old-age dependency, we derive an estimation method for bilateral migration flows and show results for the euro area and Austria. The benefit of this method is that it can be easily applied to publicly available data while at the same time exhibiting an almost perfect fit when tested against observed register data for Austria.

2 Effects of migration on inflation – a bird’s-eye view on Europe

2.1 Migration and age structures

Let us start by looking at the impact of migration on population aging. In Europe, we see the highest shares of migrants (relative to the native population) in the age groups from 0 to 10 and from 20 to 40 years (Philipov and Schuster, 2010). Additional evidence supporting this finding comes from data on the EU-28, which also highlight that the share of migrants relative to the native population is particularly high in the 20-to-40-years age group (Poutvaara, 2021). Moreover, estimates suggest that emigration – mostly to the EU – from a sample of non-EU Eastern European countries is predominantly driven by individuals aged 20 to 29 years (Zaiceva and Zimmermann, 2014). In other words, immigration in Europe appears to be concentrated in the working-age population, particularly in the younger segments.

These observations provide the context for the many voices that suggest migration as a solution to the population aging problem in many developed economies (see, e.g., Peri, 2020). However, many studies remain cautious in this regard. Some point to the fact that immigration rates would have to be unrealistically large to be able to halt the population aging process (Zaiceva and Zimmermann, 2014), and simulations indicate that even secondary effects of immigration by “importing” higher fertility rates are likely to evaporate quickly (Marois et al., 2023).

Thus, while immigration tends to have a rejuvenating effect on the population, it is unlikely to reverse the population aging process (United Nations, 2017). The corollary is that migration’s primary demographic effect may be to slow down – but not reverse – broader trends associated with the aging of the native population.

2.2 Population aging and inflation

Research on the fiscal implications of population aging is extensive, but its effects on monetary policy and price trends remain opaque. Studies suggest that demographic changes influence price dynamics in varied ways. For example, an increase in the dependent population (young and old) has been associated with upward pressure on prices (Juselius and Takáts, 2015, 2018, 2021).

Chart 1

Source: Authors’ compilation.

Within the working-age population, younger segments tend to contribute to higher price pressures, while older segments appear to moderate price increases (Mojon and Ragot, 2019; Rai and Garg, 2024).

These effects are driven by consumption patterns. Older populations, where more people belong to the age-dependent segment, allocate more spending to healthcare and nontradable goods, which tend to experience higher price growth (Lis et al., 2020). However, studies like Broniatowska (2019) note that old-age dependency may correlate with reduced price growth in certain contexts, possibly reflecting variations between younger and older dependent populations. Altogether, the demographic impact of migration on price pressures can be depicted in a swoosh-like relation as shown in chart 1.

The differentiation between younger and older working-age populations also helps reconcile regional differences. In Europe, for instance, larger working-age populations are often younger, which may explain observed upward pressures on prices (Lis et al., 2020; Bobeica et al., 2017). Conversely, a shift toward older working-age populations tends to moderate price pressures, as older workers generally save more and spend less.

From a monetary policy perspective, demographic shifts influence the effectiveness of interventions. Leahy and Thapar (2019) find that monetary policy has stronger impacts when middle-aged populations are larger, as they drive business formation and economic activity.

2.3 Migration, demographics and inflation

Migration thus plays a critical role in shaping demographic structures, which, in turn, influence inflation and monetary policy. As shown in the literature and below, migration tends to reduce the old-age dependency ratio (OADR), slowing a society’s aging process (Philipov and Schuster, 2010; Poutvaara, 2021). However, what are the implications of these demographic changes for inflation or monetary policy?

Let us develop our reasoning along three core arguments:

1) Labor supply:

Migration, particularly of younger workers, expands labor supply. For example, migration has been shown to ease the pressure on prices in sectors facing labor shortages, such as healthcare (Goodhart and Pradhan, 2020). If migration reduces labor market tightness, inflationary pressures can moderate. Evidence from Germany, Spain and the US highlights migration’s role in alleviating labor market bottlenecks and stabilizing prices during economic expansions (Dolado et al., 2008; Cohen, 2024).

2) Consumption patterns and aggregate supply:

The age structure of a population influences aggregate consumption and savings behaviors. Younger populations tend to spend more on durable goods and services, increasing aggregate demand and potentially contributing to inflationary pressures. Conversely, older populations save more and spend less, exerting downward pressure on inflation (Juselius and Takáts, 2021).

3) Monetary policy effectiveness:

The effectiveness of monetary policy depends on the demographic structure of the economy. If migration increases the share of younger, economically active individuals, it may enhance the responsiveness of consumption and investment to monetary policy interventions (Leahy and Thapar, 2019).

Given these mechanisms, our proposed methodology for estimating bilateral age-specific migration stocks provides a crucial tool for policymakers. Changes in the demographic structure can have monetary policy implications, and improving our understanding of these can help to better inform central banks. This is particularly relevant in the euro area, where demographic trends are diverging across member states, potentially leading to heterogeneous inflationary pressures. Moreover, studies, such as Salisu et al. (2024), that suggest that over the long term, migration may also lead to upward price pressures due to increased consumption as migrants integrate into the labor market, show that migration’s impact on inflation is not uniform across all contexts. By improving our toolkit for understanding bilateral age-specific migration flows, we provide policymakers with a more accurate tool to assess the demographic underpinnings of inflation dynamics and labor market conditions.

3 Effects of migration on population aging – an attempt to assess contributions

3.1 The aggregate picture

Let us take a broader look at the age distribution of both natives and migrants in Europe in order to show the “observed” effect migration has had on demographic aging. A commonly used measure for assessing aging societies is the OADR (OECD, 2017), which represents the number of people aged 65 and older as a fraction of the working-age population (WAP). A ratio greater than 1 indicates that there are more older individuals than those of working age. Conversely, a smaller ratio implies that a higher proportion of people belongs to the WAP.

For data on migrants, we use the UN’s stock datasets that are based on individuals born abroad (UN International Migrant Stock, United Nations, 2020) to assess their contribution to these demographic patterns. Data on OADRs are taken from the United Nations’ World Population Prospects (United Nations, 2024). Examining these data for the euro area, we observe how quickly the OADR has increased in Europe since 1990 (see chart 2). In 1990, the OADR still stood at roughly 20%, indicating that for each person over the retirement age, there were five persons of working age in the population ³ . By 2020, the OADR had increased to 33%, meaning that for each older person, there were now fewer than three persons of working age in the population.

Chart 2

The picture changes significantly when we focus exclusively on the migrant population (depicted by the green line in chart 2). Among migrants in the euro area, there are still approximately five people of working age for every person aged 65 or older, and this ratio has remained relatively stable since 1990. The notable exception occurred between 2015 and 2020, when a significant increase was observed. Given that migrants account for roughly 15% of the total population, they help to reduce the overall OADR in the euro area by approximately 2.5 percentage points.

When examining Austria in isolation, the rejuvenating effect of migration becomes even more pronounced (chart 3). In Austria, the average age of migrants has remained relatively low, including during the most recent period, and is comparable to the euro area average. However, migration reduces Austria’s OADR by 3.8 percentage points — approximately 50% more than the reduction seen in the euro area. This stronger impact is largely due to the foreign-born population in Austria accounting for nearly 20% of the total population, compared to only about 15% in the euro area (2020 data). Note that these shares only take into account first-generation (i.e. foreign-born) migrants. Given that “imported” fertility rates among migrants at least initially tend to be higher than those of the native population, the effect would likely have been larger had second-generation migrants also been taken into account.

Chart 3

This discussion highlights two important points. First, as shown by Marois et al. (2023), the data for both the euro area and Austria emphasize that even substantial levels of immigration are not able to reverse overall population aging trends. Second, migration can nonetheless significantly slow the population aging process and ease the policy adjustments that become necessary due to population aging – provided that labor market integration is successful.

3.2 Estimating bilateral migration contributions

The next question we want to address is whether it would make a difference to the observed effects if we were to “shut off” a certain origin country completely, say, due to some kind of fragmentation process yielding effectively closed borders. Put differently, are there origin countries that contribute more strongly to reducing the OADR than others, and to which parts of the WAP do they contribute? Unfortunately, there are no age-specific bilateral migration flow and stock data available for all European countries. ⁴ However, the United Nations provide, in addition to data on bilateral migrant stocks, also age-specific migrant stocks (that have no bilateral dimension).

This means that while we know the age structure of all immigrants to a country j , we do not know the age structure of the immigrants of a specific country i to country j . In the following section, we will introduce a technique that allows us to come up with estimates for all countries under a number of assumptions. Let us start by defining the following notations:

M _cj – age cohort c of migrants in destination j (origin unknown)

IM _ij –bilateral stocks of migrants from i in j (age unknown)

C _Ci – relative age c cohort at origin i (i.e. absolute number of people in cohort c at i /population at i )

MI _cij – initial approximation of migrants from i to j in age cohort c

ME _cij – (final) estimate of the migrants from i to j in age cohort c

In the first step, let us come up with an initial approximation of migrants from i to j .

${MI}_{cij} = {IM}_{ij}*C_{ci}$ (1)

That is, by multiplying IM _ij by C _ci we make an initial approximation of the stock of migrants from i in that age cohort in destination j . We take the absolute value of all migrants from i and multiply it by the proportion of people that are in age cohort c in the origin. The implicit assumption here is that the demographic structure of migrants from i to j is identical to the demographic structure of the population at i . This surely is a strong assumption and will almost certainly be incorrect when considering that emigration is likely to be predominantly concentrated among the younger parts of sending countries’ populations (Gruber and Schorn, 2019). However, this becomes problematic only when comparing situations where the age distribution of migrants from certain origin countries differs more strongly from the age distribution in their home countries than in other countries. ⁵ This could arise if the motives for migration differ significantly among migrants from different countries. While demographics likely will differ less for asylum seekers, the people who come for work reasons will be more likely tilted toward younger segments of the population. However, as long as we can reasonably assume that the migrant population is dominated by one of the two types of migrants, we can consider this as an acceptable workaround.

Next, we calculate the factor x in:

$M_{cj} = x_{cj}\sum_{i}^{n}{{IM}_{ij}*C_{ci}}$ (2)

In words: the sum of our estimates of bilateral migrant stocks over all origins i in an age cohort deviates from the actual (total) migrant population in this very age cohort by the factor x . This is a consequence of our strong assumptions. However, since we know $x_{cj}$ we can “force” the distribution of our estimates ($\sum_{i}^{n}{{IM}_{ij}*c_{ci}}$) to follow the distribution of $M_{cj}$ thereby pushing the aggregate error to 0. To do so, let us reformulate x as:

$x_{cj} = \frac{M_{cj}}{\sum_{i}^{n}{{IM}_{ij}*c_{ci}}}$ (3)

All variables on the right-hand side are known. Then we calibrate the sum of our estimates of the bilateral migration age cohorts with $x_{cj}$ to make them consistent with the sum of all migrants in that age cohort ($M_{cj})$: First, we calculate the deviation factor $x_{cj}$ using the sum of the bilateral estimates ($\sum_{i}^{n}{{IM}_{ij}*c_{ci}})$ to quantify $x_{cj}$, which we then use as a scaling factor on our initial estimate in equation (1). This gives us the final estimate of migrants from i in age cohort c :

${ME}_{cij} = x_{cj}*{IM}_{ij}*c_{ci}$ (4)

with the sum of these estimates over origins corresponding to the actual age distribution of cohorts c :

$M_{cj} = \sum_{i}^{n}{ME}_{cij}$ (5)

The idea here is simply to inflate or shrink the bilateral estimate for each origin country i in each cohort c by the same factor x . Note that x does not include an origin country index – only an index for the destination j – implying that the source countries for a given destination and cohort would be scaled equally) so that the sum of the bilateral estimates for each cohort corresponds to the observed total number of migrants in this cohort.

Again, the only assumption we need is that there are no systematic deviations in the age structure differences between migrants and their respective home populations across origin countries. This is admittedly a strong assumption but, in our view, a reasonably practical one to gain some preliminary insights into the age distribution of the migrant population by origin – information that is simply not observable in the data.

3.3 Some cautious findings on bilateral migration contributions

Let us note that first, perhaps not all that surprising, migrants to the euro area appear to be concentrated in the working-age population. While migrants constitute only approximately 14% of the total population, they account for slightly less than 17% of the older working-age population and for slightly more than 18% of the younger working-age population.

Table 1

When we assess the countries of origin within these groups, the following picture emerges: First, migration within the euro area is overwhelmingly dominated by movements originating within Europe. In fact, more than 36% of all migrants in the euro area come from other EU countries, and a total of 56% were born in a European country (either within or outside the EU). Note that if the EU were not an economic area but a country, the 36% of intra-EU migration would be considered as internal migration.

Against this backdrop, it is noteworthy that slightly more than 56% of migrants in the older WAP are likely to be of European origin, which makes this group the oldest among the migrant groups while it still remains younger than the native population on average.

Chart 4

Unsurprisingly, migration from Europe is therefore unlikely to substantially alter the demographic structure within the euro area. This is not to say that this group could not mitigate labor market bottlenecks, but in terms of age structure, it does not have a significant impact.

The second remarkable group of migrants (apart from migrants from European countries) comes from the region broadly summarized as MENA (Middle East and North Africa) ⁶ . This group likely also reflects historical migration routes from former colonies, migration from Türkiye and more recent waves of immigration from countries like Syria and Afghanistan. On a European level, migration in this group is significantly tilted toward younger ages, with a higher share of migrants in the younger segment of the working-age population than in the older segment.

Chart 5

When examining Austria, we observe that some trends evident in the euro area are more pronounced here and that there are also some distinct characteristics. In Austria, people born abroad make up a significant part of the working-age population, roughly 25% of the total WAP. Almost half of these migrants come from other EU countries, three-quarters were born in Europe. For historical reasons, a notable migrant group in Austria comes from the Western Balkans, accounting for 5.5% of the WAP. Their age distribution somewhat mirrors that of other European migrants. Their age profile is skewed toward older ages, according to our calculations, but still younger than that of the native population. Migrants from other EU countries and non-EU countries tend to be younger than those from the Western Balkans.

Meanwhile, migrants from the MENA region have achieved comparable significance within the working-age population, representing 4.4% of the WAP. This group shows a marked skew toward the younger segment of the working-age population in Austria, even more strongly than in the euro area aggregate.

As a result, at the current juncture, migrants from European countries are likely to be relatively more concentrated in the older portion of the age distribution, i.e. the group that is more strongly associated with decreasing wage pressures, but are also already strongly concentrated in the older dependent segment. Like the in the EU, migrants from other European countries tend to be concentrated in the older segment of the WAP in Austria as compared to other migrant groups. However, their share in this group is smaller than the EU average. As in the broader EU context, migration from the MENA region significantly contributes to reducing the OADR in Austria but these migrants are concentrated in younger parts of the WAP.

Following the discussion above, it would appear that intra-European migration is indeed most strongly concentrated in older segments of the WAP while migration from MENA – the other significant migrant group – is relatively more strongly represented in the younger segment of the labor market. If these groups are successfully integrated into the labor market, it is more likely that in the short run, migrants from Europe – on average – will be in age groups in which household savings tend to exceed expenditures; for migrants from MENA, however, this is likely to only happen in the medium run.

3.4 Cross-checking the Austrian approximations with administrative data

As stated above, we do not have data on the age distribution of bilateral migrant stocks for all countries at the European level. However, for Austria, data on the bilateral stock of migrants and their respective ages are available, making it possible to get an idea about the error introduced by our approximation approach. These data are taken from the so-called register census (Registerzählung from Statistics Austria), which is a complete survey of the entire Austrian population and its characteristics. These statistical characteristics are not collected from the people themselves via questionnaires but are taken exclusively from administrative registers.

Let us start by looking at our initial estimate ${MI}_{cij}$ of migrants from origin i in age cohort c . In fact, the fit of this (“unscaled”) approximation is not too bad, with a correlation coefficient of 0.94 with the actual value derived from administrative data as can be seen in the chart below.

Chart 6

However, let us look at the fit after distributing the error x equally over age cohorts in order to evaluate whether our suggested correction method improves the fit. Indeed, it does, the fit of our approximation improves remarkably. Chart 7 below clearly shows a high correlation of our estimate with the register-based Austrian data. This results in a correlation coefficient of roughly 0.99, which leads to a far better fit, as can be seen from comparing chart 7 with chart 6.

Chart 7

This implies that our approximation method described above very closely matches the data in Austria’s official registers. When comparing the pyramid above with the one in the administrative data, we can only see minor mismatches. For instance, we overestimate the share of migrants from the EU in the older WAP while at the same time underestimating the share of young WAP migrants from the EU. The opposite is true for the MENA region. The reason for this might be that the proportion of migration for work or study reasons is larger in the EU than in the MENA region, where political aspects may also play a role.

Chart 8

Overall, the very small remaining error and the comparison with Austrian register data underscore that the proposed method might be useful and reliable in estimating the bilateral age distribution of migration flows. This could also be helpful for further analyses undertaken at the bilateral level in the future.

4 Conclusions

We have shown that the impact of migration on population aging is evident, particularly when comparing old-age dependency ratios (OADRs). Migration substantially reduces the OADR, with a notably stronger effect in Austria than in the euro area as a whole, due to much higher migration to Austria. However, while migration has been slowing the population aging process in the past few decades, it has not reversed the overall trend, despite quite significant immigration rates. Migration somewhat delays the effects of population aging, but it does not seem to be able to alter the picture in the longer run.

Assessing the demographic impact of different origin countries/regions of migrants requires several strong assumptions, therefore our results should be interpreted with caution. Even so, the aggregate picture is clear: The most significant origin of migrants in both Austria and the euro area are other EU countries. However, this group of EU migrants is composed of relatively older cohorts, with an age distribution similar to that of the native population, hence offering limited relief in addressing demographic challenges. The primary drivers of OADR reduction, both in terms of age structure and migrant numbers, are immigrants from MENA countries. This group contributes much younger age profiles than migrants from other countries, and it is much larger than migrant groups from more remote origins.

Two key conclusions emerge from this analysis. First, migration has the potential to mitigate the effects of population aging, albeit temporarily, and to help economies to adjust to changing demographic realities. However, again, this reasoning is linked to a framework that necessitates the successful integration of migrants into labor markets. This applies not only to future migration flows but also to the existing migrant population in the euro area. Additionally, the demographic potential of migration is shaped by the age structure of migrants’ countries of origin, further influencing its overall impact.

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