Employment and unemployment
Information on labour status (employment and unemployment) of Latvia population is acquired with the help of continuous Labour Force Survey. The main goal of the Labour Force Survey is to obtain information on the labour status of Latvia population – characterise the labour force by sex, age and level of educational, as well as to compile data on kinds of economic activities, occupations in the current (for employed) or last (for unemployed) place of work, and other indicators on labour market.
- Table of contents
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Concepts and definitions
- Data collection and statistical processing
Concepts and definitions
Active population [Labour force survey]
Active population consists of persons who in the reference period offered their work for the production of goods and services.
Active population consists of employed persons and unemployed persons actively seeking a job (both those who are registered with the State Employment Agency and those who are not).
Information on labour status (incl. employment) in Labour Force Survey of Latvia in line with the methodology is acquired from persons aged 15–74. To ensure that data are internationally comparable, part of employment indicators are calculated also on age group 15–64. Internationally, age 15–64 years is working age, and it is used by Statistical Office of European Union (Eurostat) and International Labour Organisation (ILO), thus key economic indicators are published on two age groups: 15–64 years and 15–74 years.
Activity rate
Employed persons
According to the International Labour Organisation’s definition employed persons are all persons who during the reference week did any work for cash payment or compensation in goods or services.
Self-employed persons with a business, farm or those who are undergoing professional practice are also considered as employed.
The number of employed includes also those persons who are working to produce goods for own consumption or sale.
Persons, who are in temporary absence from work due to the prenatal or maternity leave, as well as due to the childcare leave are classified as employed, if after the end of the leave their return to the previous work is guaranteed.
Employment rate
Share of employed to the total population of the same age group, in per cent.
Employee [Labour Force Survey]
Person who works for a public or private employer and who receives compensation in cash (wage or salary, regular premiums and bonuses, honorarium, gratuity or tips) or in kind (goods or services).
Employer (owner)
Self-employed [Labour Force Survey]
Self-employed is person who works in own business, professional practice or farm for the purpose of earning a profit and who does not employ any other persons.
Family worker
Workers in a relative-owned enterprise or farm, unpaid, but bringing benefit to the family.
Unemployed persons
Unemployed persons are persons, whether registered at the State Employment Agency (SEA) or not, and who meet the following three conditions simultaneously:
- during the reference week neither worked nor were temporary absent from work;
- had actively sought employment during the past 4 weeks;
- in case of finding a job, were available to start work immediately (within the next 2 weeks). Persons who already had found a job and will start up within a period of three months are also classified as unemployed.
Unemployment rate
Share of unemployed persons to the active population of the same age group, in per cent.
Unemployed persons with previous working experience
Unemployed persons who previously had been employed, excluding those, who have had odd jobs (inconstant or irregular jobs where they worked only a few days or weeks without continuous job guarantees).
Unemployed persons without previous working experience
Unemployed persons who have never been employed.
Long-term unemployed persons
Persons who have been without work for 12 months or longer, are actively seeking work and are ready to start working within the next 2 weeks.
Long-term unemployment rate
Inactive population
Persons who can neither be classified as employed nor as unemployed persons (pupils, students, non–working pensioners, etc.).
Kind of economic activity [Labour Force Survey]
Specific sphere of activity of the enterprise that expresses itself in the produced goods or services.
The kind of the economic activiy is determined by Statistical Classification of Economic Activities (NACE Rev. 1.1 or NACE. Rev. 2 (starting from 2008)). In 2008 and 2009 the data were published according to NACE Rev. 1.1 and NACE Rev. 2.
Occupation or position
Occupations in Latvian economy according to the Classification of Occupations.
Main job [Labour Force Survey]
The job a person usually works the majority of working hours per week. All overtime hours and hours worked outside the work place, but in order to perform the work tasks (for example, at home if there is an agreement between the employer and employee on performing the work tasks outside the work place), are included. Those hours that are worked in other jobs are excluded. For a half-time two-job holder, the main job is the one where the person has the salary tax booklet.
Second job [Labour Force Survey]
All those activities that are not related to the main job but give extra income.
Full-time job
Time usually worked by the employees (also employers or self-employed) (for at least 40 hours per week) as well as reduced working time stipulated by the regulations for employees of some separate categories.
Part-time job
Time worked that usually is shorter than 40 hours per week (lower workload, part of the working day or part of the working week) as well as time worked by the persons considering themselves as part-time workers (regardless the number of hours worked).
Underemployed part-time workers
Hours actually worked
The number of hours actually worked during the reference week (including idle time, rest time, overtime, and the number of hours worked outside the workplace in order to perform the work-related tasks).
Worked at home
Work, carried out by person, who completely or partly is working at home, for this purpose often allocating part of the dwelling – place.
Work or part of the work, which employees are doing at home because of the personal interest or lack of time, but which according to labour contract is not intended to be done at home, is not considered as worked at home.
Worked in evenings
Worked at nights
Worked on Saturday
Work which according to work place official regulations of an establishment or labour contract is done on Saturday.
Work or part of the work, which employees on their own initiative do at home or at work place also on Saturdays, is not considered as worked on Saturday.
Worked on Sunday
Work which according to work place official regulations of an establishment or labour contract is done on Sunday.
Work or part of the work, which employees on their own initiative do at home or at work place also on Sundays, is not considered as worked on Sunday.
Persons available to work but not seeking
Persons seeking work but not immediately available
Potential (additional) labour force
Early leavers from education and training
The percentage of the population aged 18–24 with at most lower secondary education and not in further education or training in the four weeks.
Starting from 2009 Eurostat has changed calculation methodology of this indicator. It does not include young people who were on holidays during the last 4 weeks.
Adult participation in learning
Population aged 25–64 in education or training in the four weeks as a share of the total population of the same age group (Labour Force Survey).
Starting from 2016 Eurostat has changed calculation methodology of this indicator. It includes also persons who were on holidays during the last 4 weeks.
Private household (household)
Several persons living in one dwelling and sharing expenditures or one person having separate housekeeping.
Collective (institutional) household
Collective households cover institutions or organisations (e.g., hospitals, care facilities for children and elderly, prisons, barracks, religious institutions, hostels for workers or students, etc.) where person is provided with permanent accommodation (for a year or longer).
Dwelling
Territorial breakdown by place of residence of the respondent
Urban area – administrative territory, to which municipal status is granted.
Rural area – administrative territory, to which no municipal status is granted.
Statistical regions – Riga region, Pierīga region, Vidzeme region, Kurzeme region, Zemgale region, Latgale region.
Territorial breakdown by regions was set according to the Classification of Administrative Territories and Territorial Units approved by the Cabinet with the Regulation No 152 of March 21, 2017.
Remote work
The way work is carried out when employed might do it within the enterprise, but carries it out permanently or on a regular basis outside the employer's enterprise, including the use of information and communication technologies.
Data collection and statistical processing
Survey method and data source
The source of data is the CSB Labour Force Survey (LFS) that was first organised in November 1995 and until 2001 took place twice a year – in May and November. From 2002 the survey is taken every week throughout the whole year.
Data collection methods used in the Labour Force Survey:
- 1995–2005 – face-to-face interviews using paper questionnaires (Paper-and-Pencil Interviewing – PAPI);
- starting from 2006 – face-to-face interviews using portable computers (computer-assisted personal interviewing – CAPI);
- starting from 2007 – CAPI and telephone interviews (computer-assisted telephone interviewing – CATI);
- starting from 2018 – CAPI, CATI and online surveys (computer-assisted web interviewing – CAWI);
- as of 13 March 2020, with aim to limit spread of Covid-19 – CATI interviews and online surveys (CAWI).
The survey covers all persons living in the surveyed household, and questions on activity status are asked to persons aged 15–74 (prior to 2001, to persons aged 15 and over). The questionnaires contain relevant questions characterising the activity of the population; these questionnaires were prepared in accordance with the internationally approved methodology of the International Labour Organisation (ILO) specifically in the area of labour force surveys that ensures comparability of information with other countries. The LFS provides information on the number of population including the active population (employed and unemployed) broken down by various characteristics (sex, age, education qualification, place of residence, employment status, etc.).
Results of the Population and Housing Census 2011 testified that number of Latvia population is smaller than one used for the extrapolation of data acquired in the Labour Force Survey. Considering changes in the population number, taking place since the previous Population and Housing Census of 2000, and re-calculation of the population number in compliance with the results of the last Population and Housing Census, also data of the Labour Force Survey on years 2001–2011 is accordingly revised. Starting from the 1st quarter of 2012 results of the Labour Force Survey are published basing on the population number acquired in the Population and Housing Census 2011.
Data on 1996–2000 are published accordingly national approach – including in the calculations all persons surveyed and those serving in compulsory military service.
To achieve compliance with recommendations of Task Force on quality of the Labour Force Survey, extrapolation of the Labour Force Survey data is based only on number of persons living in private households, excluding collective households (old people’s homes, student hostels, hospitals, prisons etc.). Information on number of persons living in collective households was obtained within the framework of the Population and Housing Census and has been updated regulary.
Starting with 2014, for the Labour Force Survey quarterly data generalisation purposes, the quarterly average number of residents in private households is used (earlier – data at the beginning of the year). These methodological changes for the calculation of weights will in general not affect the time series.
Statistical population
The LFS has two target populations:
- Resident population of Latvia aged 15–74 that during the reference period are living in private households;
- Private households, in which at least one member during the reference period is permanent resident of Latvia aged 15–74.
Sample size
From 2002 to 2006 – 10 296 dwellings per year, from 2007 to 2012 – 24 128 dwellings per year, in 2013 – 26 676 dwellings per year, in 2014 – 29 588 dwellings per year, starting from 2015 – 29 952 dwellings per year.
Monthly estimates
Unadjusted and seasonally adjusted monthly estimates on two groups of economic activity are published in the CSB database – on employed and unemployed aged 15–74 years.
Monthly indicators on employed and unemployed are available since 2002. Three indicators characterising economic activity by months and by sex are published in the CSB database:
- number of unemployed;
- unemployment rate;
- number of employed.
Unadjusted and seasonally adjusted monthly indicators of the CSB on the number of employed and unemployed are elaborated basing on the International Labour Organization (ILO) methodology.
Unadjusted data
Monthly indicators of the CSB on the number of employed and unemployed are calculated indirectly using quarterly estimates of the Labour Force SAmple Survey and monthly data on the registered unemployed provided by the SEA. To obtain unadjusted time series on indicators of the number of monthly employed and unemployed Chow–Lin (splitting-up) approach is used1. Calculations are carried out applying R package tempdisagg.
1 Chow, G.C. and Lin, A.-L. (1971) Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series.
Revisions of the unadjusted time series for previous periods is carried out once in a quarter.
Seasonally adjusted data
A time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof. Seasonality and calendar effects are present in a large number of economic time series.
Seasonality or seasonal fluctuations of time series mean those movements, which recur with similar intensity in the same season each year. For example, each year Christmas shopping time can be observed in time series reflecting retail sales statistics. Change of seasons, social habits and influence of institutional factors are among the main causes of seasonality.
The calendar effects cover influence of calendar on time series. It is impact left by differing number of working days (or Mondays, Tuesdays and other days of the week) in months on changes of indicator. For example, number of working days differing among the months may affect goods produced time series.
When the time series are influenced by seasonality or calendar effects, it may be difficult to get clear understanding on indicator changes over the time. Seasonal adjustment is made to eliminate seasonal fluctuations and calendar effects in time series.
As a result seasonally adjusted time series, from which seasonality and calendar effects have been removed, are produced. It means that seasonally adjusted time series provide an estimate for what is “new” in the series, for example turning points in trends, business cycle or irregular component. Moreover seasonal adjustment results in calendar adjusted time series, in which calendar effects or varying number of working days in months has been eliminated. Specifics of seasonally adjusted statistics allows improving data comparability over time:
- Seasonally adjusted time series do not contain seasonal fluctuations and calendar effects, thus it is possible to compare, for example, data on the current month with the previous month's data.
- Calendar adjusted time series are not influenced by calendar effects and are used to compare, for example, statistics on current month with the data on corresponding month of the previous year.
The seasonal adjustment is made taking into account seasonal adjustment guidelines developed by the European Statistical System.
In line with the methodology of monthly estimates, as new data are added, the seasonally adjusted data on previous periods are revised.
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Software |
JDemetra+ |
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Seasonal adjustment method |
TRAMO/SEATS |
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Last model revision |
for data on January 2018 |
Calculation methods
1995–2001
Sample
Labour Force Survey (LFS) was launched for the first time in November 19951. After that, LFS was organised twice a year – in May and in November.
The LFS sample was representative to residents of Latvia living in private households and aged 15–74 years.
A sample of dwellings was used for the LFS. In case of response, one household from each sampled dwelling and all corresponding household members aged 15 and over during reference period, took part in the survey.
The sample was formed as a rotating panel sample, where sampling units (dwellings) took part in the survey several times (three times in a row). The dwellings were replaced with other dwellings after the third time of interview, thus ensuring the rotation of dwellings in the panel.
Weighting
From 1995 to 2000, design weights were adjusted using the response homogeneity group method. Each primary sampling unit was used as a response homogeneity group for households.
Post-stratification method was used to obtain the final weights. In post-stratification method statistical data on permanent residents of Latvia at the beginning of the reference year in Riga, six cities and 26 administrative territory towns and rural areas in breakdown by age group and sex, were used as auxiliary information. Weight adjustment was performed at the personal level.
In 2001 design weights were adjusted using the response homogeneity group method. Each primary sampling unit was used as a response homogeneity group for households. Breakdown by stratum and survey time were defined as response homogeneity groups.
In 2001 in order to obtain final weights, calibration (ranking-ratio) method was applied. Weight adjustment was carried out at the household level, dividing a household into three age groups (0–14, 15–74, 74+). For calibration purposes statistical data on the number of population living in private households were used as auxiliary information taking into account Population and Housing Census 2011 results at the beginning of the period in breakdown by:
- 14 age groups;
- sex;
- territory type of place of residence (Riga, eight cities, towns and rural areas);
- region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).
The number of unemployed persons registered by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.
Weight calibration method was implemented, using the statistical calculation environment R and set of procedures "Sampling".
2002–2006
Sample
In 2000 the CSB started elaboration of new LFS methodology in 20002. The new LFS methodology had the following key objectives:
- to obtain quarterly estimates;
- to organise the LFS as a continuous survey;
- to implement efficient dispersion evaluators.
Based on the information on Population and Housing Census 2000 counting areas, a new sample design was developed, resulting in the creation of a frame of territories covering all private households of Latvia. These territories have been further on used as primary sample units. The territories were stratified into four strata: Riga, cities under state jurisdiction, towns and rural territories.
Two-stage sampling was used for the LFS. Territories were selected as primary sampling units at the first stage. Sampling of the territories was done with stratified systematic πps sampling, where elements are included in the sample with probabilities proportional to their size3. The second stage sampling units were dwellings. Dwellings were sampled with simple random sampling sample in each of the sampled territories.
In case of response from each dwelling included in the sample one household and members of the respective household who on Sunday of the reference week of the survey were 15–74 years old, took part in the LFS.
The main aim for using two-stage sampling was to reduce the survey costs. Design effect (which increased as the two-stage sample was used) was minimized with high number of primary sampling units in the sample.
The LFS was organised as a rotating panel survey. The rotation scheme of the households was similar to that before 2002. Dwellings took part in the survey three times with a 26-week interval.
Weighting
Design weights were adjusted using response homogeneity group method4. Each primary sampling unit was used as a response homogeneity group for households. Breakdown by stratum and survey time were defined as response homogeneity groups.
From 2002 to 2006 in order to obtain final weights, calibration (ranking-ratio) method was applied. Weight adjustment was carried out at the household level, dividing a household into three age groups (0–14, 15–74, 74+). For calibration purposes statistical data on the number of population living in private households were used as auxiliary information, taking into account Population and Housing Census 2011 results at the beginning of the period in breakdown by:
- 14 age groups;
- sex;
- territory type of place of residence (Riga, eight cities, towns and rural areas);
- region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).
The number of unemployed persons registered by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.
Weight calibration method was implemented, using the statistical calculation environment R and set of procedures "Sampling". Each primary sampling unit (territory) was used as response homogeneity group for dwellings.
2007–2012
In order to obtain more precise estimates of the number of employed and unemployed persons in breakdown by region, sex and age group, as of 2007 the LFS sample size was increased 2.4 times. The rotation scheme was changed to ensure the overlap of the samples of successive quarters.
From 2007 onwards, dwellings participate in the LFS four times with a 13-week break, 39-week break and 13-week break. Such rotation scheme ensured the overlap of the samples of successive quarters and years.
During the time period between 2002 and 2009 the frame of territories used for the LFS sample was not updated. At the end of 2009 a study was preformed, which showed that the frame of territories was outdated. It did not characterise the target population of the LFS well enough due to population migration. A new frame of territories for sampling was developed at the end of 2009. The frame was developed using the previous frame of territories. The decision was made to perform stepwise update of the LFS sample in 2010.
The new sample of territories was sampled for the period 2010–2011. Dwellings participating in the LFS for the first time in 2010 were selected from the new frame of territories. Dwellings participating in the LFS for the first time before 2010 were selected from the old frame of territories. As a result two samples of territories were used for LFS 2010.
Before 2010 LFS sample was representative to residents of Latvia living in private households aged 15–74 years. The changes were made to the sampling frame starting from the fourth quarter of 2010 with an aim to make the LFS sample representative to all population of Latvia living in private households.
Since the LFS is a rotating panel survey, the period of five quarters (4th quarter of 2010 to 4th quarter of 2011) was needed to make the LFS sample representative to all population of Latvia living in private households. As of the first quarter of 2012 the LFS sample is representative to all population of Latvia living in private households.
Weighting
Design weights were adjusted using response homogeneity group method5. Each primary sampling unit was used as a response homogeneity group for households.Breakdown by stratum and survey time were defined as response homogeneity groups.
From 2007 to 2009 in order to obtain the final weights, the calibration (ranking-ratio) method was applied. Weight adjustment was carried out at the household level, dividing a household into three age groups (0–14, 15–74, 74+). For calibration purposes statistical data on the number of population living in private households were used as auxiliary information, taking into account Population and Housing Census 2011 results at the beginning of the period in breakdown by:
- 14 age groups;
- sex;
- territory type of place of residence (Riga, eight cities, towns and rural areas);
- region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).
The number of unemployed persons registered by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.
Weight calibration method was implemented, using the statistical calculation environment R and set of procedures "Sampling".
As of the 1st quarter of 2010 weight adjustment was performed at the household level. It means that the weights for all persons of one household are equal. The weights of persons are equal to the corresponding household weight. The remaining weight formation methodology was not changed. In order to obtain the final weights, the calibration (ranking-ratio) method was applied. For calibration purposes statistical data on the number of population living in private households were used as auxiliary information, taking into account Population and Housing Census 2011 results at the beginning of the period in breakdown by:
- 14 age groups;
- sex;
- territory type of place of residence (Riga, eight cities, towns and rural areas);
- region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).
The number of unemployed persons registered by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.
Starting from 2013
Sample
In order to ensure higher data quality for estimates providing a more detailed breakdown, during the time period from 2013 to 2014 the sample size was gradually increased, and in 2015 it 1.2 times exceeded the size of 2012.
During the time period from 2013 to 2015 the LFS sample and weight methodology has not been changed significantly. The LFS uses a rotating panel survey, which is representative to all population of Latvia living in private households. Dwellings participate in the LFS four times with a 13-week break, 39-week break and 13-week break.
Weighting
To obtain more precise statistical data the weight formation methodology has been improved.
In 2013 statistical data on population living in private households at the beginning of the reference year by the following breakdown were used as auxiliary information for weight calibration:
- 14 age groups;
- sex;
- territory type of place of residence (Riga, eight cities, towns and rural areas);
- region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale) and eight cities (Daugavpils, Jelgava, Jēkabpils, Jūrmala, Liepāja, Rēzekne, Valmiera and Ventspils).
The number of unemployed persons registered by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.
Starting from 2014
In turn, as of 2014, quarterly average statistical data on population living in private households preserving the above-mentioned breakdown by age, sex, territory type and region of place of residence has been used as auxiliary information for weight calibration.
As of the 3rd quarter of 2014 statistical data on the number of employed persons from the State Revenue Service database were added as auxiliary information for weight calibration, at the same time also using the registered number of unemployed persons provided by the State Employment Agency in breakdown by sex and age group.
Starting from 2015
In 2015, weight methodology was improved with additional non-response adjustment in breakdown by survey data collection form (direct interviews and telephone interviews).
Starting from 2017
As of 2017, the State Employment Agency data on people registered as unemployed persons are linked with the information acquired within the Labour Force Survey (thus reducing responding burden, since this information is not asked to respondents anymore). Thereinafter this information will be used in the weight calibration by linking it with the State Employment Agency data on the number of registered unemployed persons in breakdown by gender and age group. The rest of the methodology used in weight development is not changed and is comparable with the historical data.
Wave approach
Sample
As of 2017, annual indicators in Labour Force Survey are acquired with the help of wave approach. Indicators the periodicity whereof in line with the commission Regulation (EC) No 377/2008 is year are acquired in the first interview only. Thus, in line with the sample design, in one quarter annual estimates are produced by asking information from one fourth of the quarterly sample.
Weights
In 2017, additional weights for indicators with annual periodicity are introduced.
In line with the sample design, initially quarterly design weights from the 1st wave respondents are created. Design weights are adjusted for each quarter by using response homogeneity group method.
The quarterly weights acquired are merged before calibration and divided by four. Calibration is used for annual data by using the raking-ratio method.
Statistics on usually resident population living in private households of Latvia in breakdown by five-year age groups and in territorial breakdown, as well as State Employment Agency (SEA) data on registered unemployed persons by age group is used as additional information in the weight calibration. To comply with the requirements of the Paragraph 3 in Annex I to the Regulation No 377/2008 regarding compliance of the totals, additionally also Labour Force Survey full-scope sample employment, unemployment and inactive population estimates in breakdown by gender and ten-year age group are used in weight calibration.
Data publishing
In line with the wave approach, annual indicators on employed persons by type of atypical work and sex (table NBG140) – worked at home, working shift work, worked in evenings, worked at nights, working on Saturday and working on Sunday – are published in the CSB database. Until 2016, information was acquired in all interviews (all waves) and annual indicators were calculated as arithmetic mean of the quarterly indicators, whereas as of 2017, the indicators are acquired via the 1st interview (1st wave) only. In other tables, just like before, annual data of the Labour Force Survey are arithmetic mean of the quarterly indicators.
Data accuracy
Standard error – since the Labour Force Survey is a sample form survey, its estimates, generalised for the entire population, may differ from the results that would be obtained in a full scale survey. Standard error is an indicator characterising sample error, and it serves also as an output value for the calculation of other quality indicators, for example, for relative standard error or coefficient of variation and absolute margin of error. By calculating coefficient of variation, it can be established whether the obtained result is sufficiently reliable.
Example: If an estimation is 90.0 and standard error is 6.14, relative standard error or coefficient of variation (CV) can be obtained as follows:
CV = 6.14 / 90 * 100 = 6.82. To calculate the absolute margin of error – standard error must be multiplied by 1.961
Absolute margin of error (+/–) = 6.14 * 1.961 = 12.03
1 Coefficient used at 95% confidence.
Absolute margin of error – a precision indicator, which expresses the maximum possible difference between the true population parameter and a sample estimate of that parameter with 95 % confidence.
When comparing estimates, it is important to use absolute margin of error to determine whether differences between values are statistically significant.
Example: if estimate is 90.0 and absolute margin of error (+/–) 12.03, confidence limits are (77.97; 102.03), with 95% confidence that the true value lies there.
Currently data accuracy indicators (standard error and absolute margin of error) are available for the following tables:
NBG010. Population by labour status and sex
NBG081. Employed by economic activity and sex (NACE Rev. 2.)
NB190c. Unemployed persons and unemployment rate by age group, sex and quarter
NB260c. Active population, inactive population and activity rate by age group, sex and quarter
NB050c. Employed and employment rate by age group, sex and quarter
Reliability limits
Quarterly data (thousands)
| A | B | |
| 2002 | 6.5–10.6 | 0.0–6.4 |
| 2003 | 8.6–14.0 | 0.0–8.5 |
| 2004 | 6,0–9,8 | 0.0–5.9 |
| 2005 | 6.4–10.4 | 0.0–6.3 |
| 2006 | 7.4–12.1 | 0.0–7.3 |
| 2007 | 3.7–5.9 | 0.0–3.6 |
| 2008 | 3.8–6.2 | 0.0–3.7 |
| 2009 | 4.5–7.3 | 0.0–4.4 |
| 2010 | 5.4–8.8 | 0.0–5.3 |
| 2011 – 2013 | 4.5–7.2 | 0.0–4.4 |
| 2014 | 3.9–6.2 | 0.0–3.8 |
| 2015 | 3.7–5.9 | 0.0–3.6 |
| 2016 | 3.6–5.8 | 0.0–3.5 |
| 2017 | 3.2–5.0 | 0.0–3.1 |
| 2018 | 3.0–4.8 | 0.0–2.9 |
| 2019 | 3.8–6.0 | 0.0–3.7 |
| 2020 | 4.2–6.8 | 0.0–4.1 |
Annual data (thousands)
| A | B | |
| 2001 | 12.9–21.1 | 0.0–12.8 |
| 2002 | 2.6–4.1 | 0.0–2.5 |
| 2003 | 3.6–5.7 | 0.0–3.5 |
| 2004 | 2.0–3.2 | 0.0–1.9 |
| 2005 | 2.2–3.4 | 0.0–2.1 |
| 2006 | 2.0–3.2 | 0.0–1.9 |
| 2007 | 1.0–1.5 | 0.0–0.9 |
| 2008 | 1.1–1.7 | 0.0–1.0 |
| 2009 | 1.7–2.6 | 0.0–1.6 |
| 2010 | 2.2–3.5 | 0.0–2.1 |
| 2011 | 1.9–3.0 | 0.0–1.8 |
| 2012 | 1.6–2.5 | 0.0–1.5 |
| 2013 | 1.7–2.7 | 0.0–1.6 |
| 2014 | 1.5–2.3 | 0.0–1.4 |
| 2015 – 2016 | 1.3–2.0 | 0.0–1.2 |
| 2017 | 1.3–2.0 | 0.0–1.2 |
| 2018 | 1.6–2.3 | 0.0–1.5 |
| 2019 | 2.0–2.9 | 0.0–1.9 |
| 2020 | 1.8–2.5 | 0.0–1.7 |
Structural (annual) data (thousands)
| A | B | |
| 2017 | 3.2–5.0 | 0.0–3.1 |
| 2018 | 1.7–2.4 | 0.0–1.6 |
| 2019 | 1.5–2.1 | 0.0–1.4 |
| 2020 | 1.6–2.3 | 0.0–1.5 |
Additional information:
A – Quarterly and annual estimates are based on small number of respondent answers.
B – Quarterly and annual estimates are not published because they are too uncertain for presentation.
1 Lapins, J. (1997). Sampling Surveys in Latvia: Current Situation, Problems and Future Development. Statistics in Transition, Vol. 3, No. 2, 281–292.
2 Lapins, J., Vaskis, E., Priede, Z. and Balina, S. (2002). Household Surveys in Latvia. Statistics in Transition, Vol. 5, No. 4, 617–641.
3 Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. New-York: Springer-Verlag.
4 Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. New-York: Springer-Verlag.
5 Särndal, C.-E., Swensson, B. and Wretman, J. (1992). Model Assisted Survey Sampling. New-York: Springer-Verlag.
Classifications
Data of the Labour Force Survey are classified by using following classifications:
- Statistical Classification of Economic Activities in European Community (NACE Rev.1.1);
- Statistical Classification of Economic Activities in European Community NACE (Rev. 2; starting from 2008);
- Classification of Administrative Territories and Territorial Units of the Republic of Latvia (CATTU);
- Classification of Occupations that has been developed on the basis of the International Standard Classification of Occupations (ISCO-88 (COM), starting from 2011 – ISCO-08);
- Classification of Education that is comparable with the International Standard Classification of Education (ISCED) (until 2013 – ISCED 1997, starting from 2014 – ISCED 2011);
- Nomenclature of Territorial Units for Statistics (NUTS);
- International Classification of Status in Employment (ICSE).
Contact person on methodology
Zaiga Priede
Social Statistics Department
Social Statistics Methodology Section
Senior Expert