Population and key demographic indicators (experimental statistics)
1. Contact
Responsible agency
Unit
Contact person
Position
Email (agency)
Phone
2. Statistical presentation
Data description
Experimental statistics data on population, including various demographics characteristics: gender, age, citizenship, etc.
Experimental statistics is produced by using new data sources and methods in making attempts to expand the range of statistics or the level of detail thereof based on the needs of data users.
It should be noted that the methods used in experimental statistics are not constant, approbated or internationally harmonized and can be changed to improve data quality. CSB publishes experimental statistics to get user feedback, evaluate analytical potential of the data and relevance thereof to the actual reality and data user needs. The CSB believes that data users may find experimental data series valuable, and opinion of the data users may serve as a basis for making a decision to include this statistics in the Official Statistics Programme. By publishing experimental statistics, the CSB provides data users with new sources of information that may be used for decision making.
Statistical concepts and definitions
Statistical unit
Usual resident.
Statistical population
All usually resident population of Latvia (persons living in Latvia or having an intention to live for a period of at least 12 months).
3. Institutional mandate
Legal acts and other agreements
N/A
4. Accessibility and clarity
On-line database
5. Comparability
Comparability - geographical
N/A
Length of comparable time series
Data on mean years of schooling index are comparable only since 2017.
6. Coherence
Coherence- cross domain
N/A
7. Statistical processing (data source etc.)
Source data
Main data sources:
- Results of Population and Housing Census (PHC) 2000
- Results of PHC 2011
- CSB estimation of population starting from 2016 (see https://stat.gov.lv/en/metadata/5911-population-and-key-demographic-indicators#stat_process Data compilation → Population Estimation);
- CSB housing database starting from 2019;
- CSB cumulative education database starting from 2017;
- Register of Natural Persons (RNP, until 28.06.2021 Population Register (PR)) managed by the Office of Citizenship and Migration Affairs;
- Data of the State Revenue Service (SRS) from the notices regarding the amounts disbursed to a natural person, micro-enterprise tax declarations, notifications on the labour income, seasonal farm worker reports, employer’s reports and information regarding employees;
- National Real Estate Cadastre Information System (NRECIS) starting from 2016 and State Address Register managed by the State Land Service incl. territorial division spatial data.
- Boundaries of neighbourhoods and densely populated areas published on the Latvia's Open Data Portal.
Data collection
N/A
Data compilation
Boundaries estimation of territorial units
Villages, territorial units, administrative territories and statistical regions were set in line with the boundaries of the territorial division spatial data acquired from the State Address Register at the beginning of 2021. Densely populated areas were set in line with the boundaries at the beginning of the respective year.
Urban and rural population were set in line with the boundaries at the beginning of the respective year.
When using geo-coded addresses (coordinates of address points) and geometries of buildings (starting from 2016), territories can be defined spatially:
- in PHC 2000 – 99.99 % of usually resident population,
- in PHC 2011 – 100.00 %,
- in 2016 – 99.65 %,
- in 2017 – 99.65 %,
- in 2018 – 99.61 %,
- in 2019 – 99.59 %,
- in 2020 – 99.57 %,
- in 2021 – 99.53 %,
- in 2022 – 99.54 %,
- in 2023 – 98.75 %,
- in 2024 – 99.25 %,
- in 2025 – 99.45 %,
- in 2026 – 99.53 %.
Population and Housing Census 2000
It is assumed that usual residence of 90.7 % of the usually resident population of Latvia is the one indicated in the PHC 2000 questionnaire, while of those not met during the PHC – that indicated in the PR. Within the official results of the PHC 2000, the demographic data (those characterising person) are published in line with the place of residence indicated in the PR (registered address). Total population of the country in official statistics meets the number of inhabitants in the PR, whereas experimental statistics has excluded 321 people the existence of which at the critical moment of the population census is questionable. The grid data excludes also 189 inhabitants because geocoding of their address was not possible. The sum of inhabitants in age groups does not meet the total number of inhabitants, as there is no information about the age of 73 inhabitants. There is no information about the highest educational level attained of 7.7 % of the population. For 223 inhabitants in two surveyed areas, highest educational level attained indicated erroneously in the initial database was corrected to the one indicated in the questionnaire.
Within the framework of the project Merging Statistics and Geospatial Information in Member States undertaken in 2016–2017, geocoding of the PHC 2000 data was carried out by linking geographical coordinates and addresses: download (PDF).
Refer to the types of coordinate-address linking and the number of persons linked: download (XLSX).
Areas in cities under state jurisdiction where share of usually resident population in the total number of city population has increased or decreased significantly; 2000–2017: download (PDF).
Mean years of schooling index
Mean years of schooling index \(I=\frac{t-t_{min}}{t_{max}-t_{min}}\), where \(t\) – mean years of schooling in actual territorial unit, \(t_{min}\) – minimum mean years of schooling among all territorial units, \(t_{max}\) – maximum mean years of schooling among all territorial units.
Years of schooling equals average number of years needed to achieve educational attainment:
| Level of education | Years |
| Cannot read or write | 0 |
| Without formal education | 2 |
| Less than 4 grades | 2 |
| Primary education | 6 |
| Basic education | 9 |
| General secondary education | 12 |
| Professional secondary education | 12 |
| Higher education | 16.5 |
The number of years needed to complete higher education is estimated as the weighted average of the number of years needed to acquire short-cycle higher education (14.5 years), bachelor’s or equivalent degree (15.5 years), master’s or equivalent degree (17 years) and doctoral degree (20.5 years), by using the estimated share of usually resident population having the respective education in the total population having higher education at the beginning of 2018: 0.06×14.5+0.29×15.5+0.63×17+0.02×20.5=16.485≈16.5.
Population of Housing Census 2011
Just like in the official statistics, it is assumed that usual residence of 90.7 % of the usually resident population of Latvia is that indicated in the PHC 2011 questionnaire, while of those not met during the PHC – that indicated in the PR.
Addresses with the linked coordinates in the database are available on all persons, however not always they are correct and may not meet the addresses indicated in the questionnaire. For example, only a village is indicated in questionnaire, but the address of the building linked does not meet the actual one. Thus, comparison of the grid data with other periods may lead to inappropriate population change. For example, comparison of the situation at the PHC 2011 moment and beginning of 2018 shows that population within the grid cell 1kmX689Y309 (Lazdukalns rural territory of Rugāji municipality) has grown from 37 to 49, while in the neighbouring cell 1kmX689Y308 it has dropped from 44 to 18. Actually, address of part of the persons living in the cell 1kmX689Y309 in the PHC 2011 was wrongly indicated in the cell 1kmX689Y308. In some cases, the official PHC data on the location of usual residence have been corrected (territorial unit for 382 persons, code of the addressing object from the State Address Register for 454 persons).
For 126 inhabitants, highest educational level attained indicated erroneously in the initial database was corrected to the one indicated in the questionnaire.
Mean years of schooling index
Mean years of schooling index \(I=\frac{t-t_{min}}{t_{max}-t_{min}}\), where \(t\) – mean years of schooling in actual territorial unit, \(t_{min}\) – minimum mean years of schooling among all territorial units, \(t_{max}\) – maximum mean years of schooling among all territorial units.
Years of schooling equals average number of years needed to achieve educational attainment:
| Level of education | Years |
| Cannot read or write | 0 |
| Without formal education or less than primary education | 2 |
| Primary education | 6 |
| Basic education or professional basic education | 9 |
| General secondary education after basic education or vocational education | 12 |
| Vocational education or professional secondary education after basic education | 11.5 |
| Professional secondary education after secondary school | 14.25 |
| Higher education | 16.4 |
| Doctoral degree | 20.5 |
The number of years needed to complete higher education is estimated as the weighted average of the number of years needed to acquire short-cycle higher education (14.5 years), bachelor’s or equivalent degree (15.5 years) and master’s or equivalent degree (17 years), by using the estimated share of usually resident population having the respective education in the total population having higher education at the beginning of 2018: 0.06×14.5+0.3×15.5+0.64×17=16.4.
Monthly wages and salaries
Calculations similar to data from the population estimate starting from 2016. Only data from the notices regarding the amounts disbursed to a natural person have been used. The threshold of average monthly wages and salaries is 36.99 euros. Employees have been included in the territory of their place of actual residence at the Census moment.
Population estimate starting from 2016
Usual residence of usually resident population of Latvia is set based on the RNP and data of the PHC 2011.
If after the PHC 2011 information in the RNP is updated (about declared and registered residence, or residence address was indicated orally), it is assumed that residence of a person is the one indicated in the RNP (code of the addressing object is used, if available). Number of inhabitants whose usual residence is set based on the data of the PHC 2011:
- in 2016 – 1 443 814,
- in 2017 – 1 321 591,
- in 2018 – 1 221 577,
- in 2019 – 1 139 091,
- in 2020 – 1 066 007,
- in 2021 – 1 002 514,
- in 2022 – 936 182,
- in 2023 – 877 260,
- in 2024 – 825 085,
- in 2025 – 771 639,
- in 2026 – 724 073.
If the reason behind the last update of the information about residence is cancellation of residence registration (false information was given or legal basis have been lost), person is included in the population of the administrative territory (municipality or city which form municipality) in which residence of the person was declared or registered prior.
In some cases, codes of the addressing objects indicated in the RNP for persons with residence permits are not used if there are too many persons in one address and it can be justified that those persons do not actually live there. In such cases, only territorial unit code is used.
Number of inhabitants excluded from the grid data (for the reasons mentioned before or geocoding of their address was not possible):
- in 2016 – 6 940,
- in 2017 – 6 892,
- in 2018 – 7 520,
- in 2019 – 7 851,
- in 2020 – 8 279,
- in 2021 – 8 820,
- in 2022 – 8 615,
- in 2023 – 23 663,
- in 2024 – 14 032,
- in 2025 – 10 190,
- in 2026 – 8 634.
Addresses of the residences of persons in prisons, social care institutions or orphanages are the addresses of the respective institution. Starting from the data about 2018, more sources about persons in social care institutions and orphanages are used. Thus, when compared to the data on previous two years, population change might not match the actual situation (e.g., sharp increase in population within the grid cell 1kmX656Y323 and Līgo rural territory of Gulbene municipality where social care institution Siltais is located).
In all other cases, it is assumed that usual residence of person is that indicated in the PHC 2011, except for cases when person was imprisoned at the PHC 2011 moment (starting from the data about 2018, located also in social care institution or orphanage), but is not in prison anymore, and information about residence of the person in RNP has not been updated after PHC 2011 (about declared and registered residence, or residence address was indicated orally). In such a case, it is assumed that residence of the person is that indicated in the PR before the PHC 2011.
Assumption that usual residence of a person is that indicated in the PHC 2011 is based on the corrected addressing object or territorial unit codes.
In respect to the children without indicated addressing object code, parents’ code is used if the territorial unit code matches. In respect to the children without indicated territorial unit code, mother’s addressing object code is used.
Coordinates of the address codes of flats as well as of the address codes having cadastral designation in the housing database (for 2016–2018 data the housing database 2019 is used) are taken from the building centroids which are based on the NRECIS data (in case centroid is outside a building, a pole of inaccessibility is used). Only buildings not containing address points are used.
Sex, age, ethnicity and citizenship according to the RNP data.
The status of economic activity in 2017–2018 has been determined from the SRS, the State Employment Agency and other administrative data sources. For the remaining persons, the figures have been imputed using the method of regulated discriminant analysis. The status of economic activity since 2019 has been determined according to the methodology of the PHC 2021.
The highest educational level attained according to the CSB cumulative education database.
Mean years of schooling index
Mean years of schooling index \(I=\frac{t-t_{min}}{t_{max}-t_{min}}\), where \(t\) – mean years of schooling in actual territorial unit, \(t_{min}\) – minimum mean years of schooling among all territorial units, \(t_{max}\) – maximum mean years of schooling among all territorial units.
Years of schooling equals average number of years needed to achieve educational attainment:
| Level of education | Years |
| ISCED 0 – early childhood education | 2 |
| ISCED 1 – primary education | 6 |
| ISCED 2 – lower secondary education | 9 |
| ISCED 3 – upper secondary education | 12 |
| ISCED 4 – post-secondary non-tertiary education | 14.25 |
| ISCED 5 – short-cycle tertiary education | 14.5 |
| ISCED 6 – bachelor’s or equivalent level | 15.5 |
| ISCED 7 – master’s or equivalent level | 17 |
| ISCED 8 – doctoral or equivalent level | 20.5 |
Monthly wages and salaries
Data of the SRS on wages and salaries from the notices regarding the amounts disbursed to a natural person, micro-enterprise tax declarations, as well as notifications on the labour income, personal income tax and mandatory state social insurance contributions of seasonal agricultural worker income taxpayers and information regarding employees.
From the notices regarding the amounts disbursed to a natural person, those whose earning period is no longer ago than the beginning of the previous year have been selected by the type of income:
| Code of the income type till 2020 | Code of the income type for 2021 and subsequent years | Income type |
|---|---|---|
| 01 | 1001 | Wage or salary |
| 03 | 1003 | Income from intellectual property or royalties (excluding income of copyright heirs) |
| 08 | 1008, 2008 | Income from contract for work-performance |
| 18 | 1018 | Income from duties on the supervisory board or board of directors |
| 43 | 1043 | Income from hired staff |
| 50 | 3050 | Labour income in another EU or EEA country |
| 58 | 3058 | Income from the provision of social care services |
| 59 | 3059, 3069 | Catering expenses paid by the employer |
| – | 3063 | Remote work stipend |
| – | 1064 | Employment income from professional sports |
| – | 1066 | Wage or salary of a civilian from Ukraine |
| – | 3070 | Medical expenses covered by the employer |
| – | 3072 | Accommodation expenses covered by the employer |
| – | 3073 | Transport expenses covered by the employer |
Royalties have been credited only if other income included in the calculation of wages and salaries is also received in the respective enterprise in the same period.
Regarding the notices on the amounts disbursed to a natural person, gross wages and salaries have been taken to be income (line 5), including contributions to private pension funds and payments of insurance premiums (with accumulation of funds, does not exceed 10 % of the employee’s gross annual wages and salaries) from the employer’s own funds on behalf of an employee under non-taxable income (line 6). The amounts of life insurance (without accumulation of funds), health and accident insurance premiums paid from the employer’s own funds on behalf of an employee indicated separately (line 13) have not been included in gross wages and salaries. Earning period of wages or salaries and labour income in another EU or EEA country excludes leave without retention of remuneration and childcare leave from the information regarding employees which in turn excludes months with wages and salaries from employer’s reports. Net wages and salaries have been calculated by deducting the personal income tax (PIT) deducted and eligible expenditure from labour income of the employee (incl. mandatory state social insurance contributions) from the gross amount.
For employees of micro-enterprises, gross wages and salaries have been calculated by adding PIT to net wages and salaries. PIT is calculated according to the formula i=n/N*M*I where n – monthly net wages and salaries of the employee, N – quarterly wages and salaries of all employees of the micro-enterprise, M – total micro-enterprise tax per quarter, I – average PIT rate, expressed in hundredths.
| Year | Average PIT rate, % |
|---|---|
| 2015 | 29.1 |
| 2016 | 26.27 |
| 2017 | 28.9 |
| 2018–2020 | 19.56 |
| 2021–2025 | 20 |
Time period of obtaining of the income have been expressed in months. It has been taken from the employer’s reports collected by the SRS if it is shorter in the notices regarding the amounts disbursed to a natural person within the enterprise and type of income than according to the employer’s reports collected by the SRS and the income difference does not exceed 5 %.
The average monthly wages and salaries at the individual level have been calculated by summing all selected income and dividing by the total number of months when there was income.
Only those employees have been included who are at least 15 years old and whose average monthly wages and salaries exceed the threshold (calculated using the national minimum hourly rate and assuming that the employee works at least one hour a day on all calendar working days):
| Year | Threshold, euros |
|---|---|
| 2015 | 45.00 |
| 2016 | 47.00 |
| 2017 | 49.00 |
| 2018–2020 | 55.00 |
| 2021–2022 | 64.00 |
| 2023 | 75.00 |
| 2024 | 84.00 |
| 2025 | 88.00 |
Employees have been included in the territory of their place of actual residence on 1 January of the following year.
Population fully primary vaccinated against Covid-19
Data of the National Health Service on vaccination status. Fully primary vaccinated persons are those who have passed 14 days after completing a full primary vaccination course or receiving a single dose of the vaccine after a laboratory-confirmed positive Covid-19 test result (who meet the conditions for vaccination certificate, for more details see: https://www.vmnvd.gov.lv/lv/sertifikatu-veidi-un-izmantosana). For monthly relative data, the population at the beginning of the year was used; the vaccinated population does not include those persons who died between the beginning of the year and the relevant month. When calculating the January–June relative data for the most recent year, the population at the beginning of the previous year is used until data for July become available.