Population

Older and younger municipalities: ages, births and deaths

Lithuania's municipalities are not ageing at the same pace. In some, more than one resident in four is of retirement age, while the districts beside the biggest cities have a much younger profile. This analysis sets the age structure beside birth and death rates to see where the oldest and youngest populations live and how closely births and deaths follow the age profile.

A wide range of ages across the country

The typical municipality has 23.3% of its residents in the retirement-age group and a median age of 47.5 years. Behind those medians the range is wide: the share of older residents runs from 14.4% in Klaipėda District Municipality to 29.2% in Visaginas Municipality.

Visaginas Municipality is the oldest municipality by this measure, followed by Birštonas Municipality, at 28.5%, and by Ignalina District Municipality, at 27.8%. Visaginas Municipality also has the highest median age, 53.0 years, a level that no other municipality reaches.

Large cities do not all look alike. Vilnius City Municipality stands at 17.5% and Kaunas City Municipality at 22.1%, under the median. The regional cities Panevėžys City Municipality and Alytus City Municipality stand at 25.7% and 25.6%, above it.

The youngest profiles are all found in district municipalities beside the biggest cities. Klaipėda District Municipality has the smallest share of older residents, 14.4%, followed by Kaunas District Municipality, with 15.4%, and by Vilnius District Municipality, with 15.5%.

Share of population aged 65 and over: top 8 municipalities

Median across the 60 municipalities: 23.3%

Median age from the capital to the spa towns

The median age gives another view of the same divide. The capital stands at 40.0 years, while Visaginas Municipality stands at 53.0 years. The other big cities lie in between: Kaunas City Municipality, Klaipėda City Municipality and Šiauliai City Municipality share a median age of 43.0 years.

Spa and seaside towns sit at the older end of the table. Birštonas Municipality has a median age of 52.0 years, Druskininkai Municipality has 50.0 years and Palanga City Municipality has 49.0 years. The figures show who lives there, not why: a resort can keep its older residents or attract new ones, and the data do not separate the two.

In two of the three big-city pairs, the district is younger than the city: the median age is 43.0 years in Kaunas City Municipality against 39.0 years in Kaunas District Municipality, and 43.0 years in Klaipėda City Municipality against 39.0 years in Klaipėda District Municipality. Around the capital the order is reversed, with 40.0 years in Vilnius City Municipality and 41.0 years in Vilnius District Municipality.

Median age of the population: 10 municipalities side by side

Median across the 60 municipalities: 47.5 years

Older populations and higher death rates

Death rates follow the age profile fairly closely. The municipalities with the largest shares of retirement-age residents tend to record the highest death rates, which is what an older population would suggest.

The highest crude death rate is found in Ignalina District Municipality, at 23.2 ‰, followed by Varėna District Municipality, at 21.8 ‰, and by Molėtai District Municipality, at 21.5 ‰. The median municipality stands at 15.6 ‰.

The link is not mechanical, however. Take the municipality with the oldest population, Visaginas Municipality. Its death rate, 15.0 ‰, is close to the median. Inside Alytus County, Varėna District Municipality and Druskininkai Municipality show similar shares of retirement-age residents, 26.1% and 25.3%, yet their death rates are 21.8 ‰ and 14.4 ‰.

Another pair makes the same point. Palanga City Municipality and Biržai District Municipality have almost identical shares of retirement-age residents, 26.2% and 26.3%, while their death rates are 12.3 ‰ and 19.5 ‰. Age alone does not decide how many deaths a municipality records in a year.

Crude death rate (deaths per 1,000 inhabitants): top 8 municipalities

Median across the 60 municipalities: 15.6 ‰

Births, and the places where they keep pace with deaths

Birth rates vary much less than death rates, yet they show a clear divide too. The highest birth rate is found in Kaunas District Municipality, at 9.5 ‰, followed by Pagėgiai Municipality, at 9.4 ‰, and by Klaipėda District Municipality, at 9.0 ‰. One of these three, Pagėgiai Municipality, has only 6,989 residents, so its rate can move sharply when a few births are added or missed.

Births come close to deaths in only a few places. In Kaunas District Municipality the birth rate is 9.5 ‰ against a death rate of 8.9 ‰, and in Klaipėda District Municipality the two rates are 9.0 ‰ and 9.1 ‰. In Vilnius City Municipality, the rates are 7.9 ‰ for births and 9.4 ‰ for deaths.

At the other extreme, the two rates are far apart. Ignalina District Municipality has a birth rate of 4.9 ‰ and a death rate of 23.2 ‰, Varėna District Municipality has 5.3 ‰ against 21.8 ‰, and Molėtai District Municipality has 4.3 ‰ against 21.5 ‰. In each of these three municipalities the death rate is several times the birth rate.

Age and births go together as well. Places with many older residents record few births, while the districts around the big cities combine a young population with the highest birth rates.

Reading rates and shares with care

Rates for small municipalities swing from year to year. Birštonas Municipality has a death rate of 16.8 ‰ for 2025, while its average over 2023-2025 is 13.3 ‰. In Neringa Municipality, the single-year figure is 6.6 ‰ and the three-year average is 8.2 ‰.

The reference years also differ. The age shares refer to 2025, the death rate to 2025 and the birth rate to 2024, so putting a birth rate beside a death rate mixes two different years. Birth rates are missing for Birštonas Municipality, Neringa Municipality and Rietavas Municipality, so the birth rankings cover only part of the municipalities.

A share is not a count. A municipality can look old because its young residents left or because its older residents stayed, and the table does not tell the two apart. The ten-year population change, covered in another analysis, adds that missing dimension.

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