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.

Definitions

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.

Share of active population in the total population of the same age group, in per cent.

The average age at which active persons definitely withdraw from the labour market.

Various organisations or institutions (hospitals, old people’s homes, cloisters, barracks, prisons etc.) which are inhabited by persons whom the respective institutions provide with shelter and livelihood for a certain period of time or permanently, as well as dormitories of higher education establishments.

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.

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.

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.

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.

The number of employed includes also those persons who are working to produce goods for own consumption or sale, and their work is an important source of livelihood for the person or the family.

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.

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) is a person who works in own business, professional practice or farm for the purpose of earning a profit and who employs one or more employees.

Share of employed in the total population of the same age group, in per cent.

Employed persons aged 55–64 as a share of the total population of the same age group.

Persons who are full-time employed (employees) or usually work at least 40 hours per week (employers, self-employed), as well as employees of some special categories with shortened labour time (teachers, physicians etc.) who consider themselves as full-time employed.

The number of hours actually worked during the reference week (including idle time, rest time, overtime, and the number of hours worked outside the working place in order to perform the working tasks).

Persons who can be classified neither as persons in employment nor as unemployed persons (housewives, non-working disabled persons, pupils, students, non–working pensioners, etc.).

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.

Those who have been without work for 12 months or longer, are actively seeking for a job and are ready to start working within the next 2 weeks.

Long term unemployed (12 months or longer) as a share of the total active population.

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 taxpayer’s book.

Persons who are part-time employed (employees) or usually work less than 40 hours per week, excluding those who consider themselves to be employed full-time irrespective of the number of their working hours.

Persons aged 15–74 neither employed nor unemployed who want to work, are available to work in the next 2 weeks but do not seek work.

Persons aged 15–74 neither employed nor unemployed who actively sought work during the last 4 weeks but are not available to work in the next 2 weeks.

Percentage of children (0–17 years) and adults (18–59 years) living in households were no-one works in total population of corresponding age group.

Occupations in Latvian economy according to the Latvian Classification of Occupations.

The total of the 2 groups: persons seeking work but not immediately available and persons available to work but not seeking.

Sector of raw material acquisition and agriculture. Sections A and B according to Statistical Classification of Economic Activities (NACE Rev. 1.1) and section A according to Statistical Classification of Economic Activities (NACE Rev. 2).

Several persons living in one dwelling and sharing expenditures or one person having separate housekeeping.

All those activities that are not related to the main job but give extra income.

Production sector; sections C to F according to Statistical Classification of Economic Activities (NACE Rev. 1.1) and sections B to F according to Statistical Classification of Economic Activities (NACE Rev. 2).

Self-employed person 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.

Urban area – administrative territory, to which municipal status is granted.

Rural area – administrative territory, to which no municipal status is granted.

Statistical regions:

  • Riga – Riga city.
  • Pierīga region – Aloja county, Ādaži county, Babīte county, Baldone county, Carnikava county, Engure county, Garkalne county, Ikšķile county, Inčukalns county, Jaunpils county, Jūrmala city, Kandava county, Krimulda county, Ķegums county, Ķekava county, Lielvārde county, Limbaži county, Mālpils county, Mārupe county, Ogre county, Olaine county, Ropaži county, Salacgrīva county, Salaspils county, Saulkrasti county, Sēja county, Sigulda county, Stopiņi county, Tukums county.
  • Vidzeme region – Alūksne county, Amata county, Ape county, Beverīna county, Burtnieki county, Cesvaine county, Cēsis county, Ērgļi county, Gulbene county, Jaunpiebalga county, Kocēni county, Līgatne county, Lubāna county, Madona county, Mazsalaca county, Naukšēni county, Pārgauja county, Priekuļi county, Rauna county, Rūjiena county, Smiltene county, Strenči county, Valka county, Valmiera city, Varakļāni county, Vecpiebalga county.
  • Kurzeme region – Aizpute county, Alsunga county, Brocēni county, Dundaga county, Durbe county, Grobiņa county, Kuldīga county, Liepāja, Nīca county, Pāvilosta county, Priekule county, Roja county, Rucava county, Saldus county, Skrunda county, Talsi county, Vaiņode county, Ventspils city, Ventspils county.
  • Zemgale region – Aizkraukle county, Aknīste county, Auce county, Bauska county, Dobele county, Iecava county, Jaunjelgava county, Jelgava city, Jelgava county, Jēkabpils city, Jēkabpils county, Koknese county, Krustpils county, Nereta county, Ozolnieki county, Pļaviņas county, Rundāle county, Sala county, Skrīveri county, Tērvete county, Vecumnieki county, Viesīte county.
  • Latgale region – Aglona county, Baltinava county, Balvi county, Cibla county, Dagda county, Daugavpils city, Daugavpils county, Ilūkste county, Kārsava county, Krāslava county, Līvāni county, Ludza county, Preiļi county, Rēzekne city, Rēzekne county, Riebiņi county, Rugāji county, Vārkava county, Viļaka county, Viļāni county, Zilupe county.

Service provision sector, sections G to Q according to Statistical Classification of Economic Activities (NACE Rev. 1.1) and sections G to U according to Statistical Classification of Economic Activities (NACE Rev. 2).

Persons aged 15–74 working part-time which wish to work additional hours and are available to do so within the next two weeks.

Unemployed persons are persons aged 15–74 years, whether registered at the State Employment Agency (SEA) or not, and who meet the following three conditions simultaneously:

  1. during the reference week neither worked nor were temporary absent from work;
  2. had actively sought employment during the past 4 weeks;
  3. 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.

Unemployed who ever 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 who have never been employed.

Share of unemployed persons in the active population of the same age group, in per cent.

Workers in a relative-owned enterprise or peasant (fisherman) farm, unpaid, but bringing benefit to the family.

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.

Work, which is carried out after 10 p.m. at least 2 hours.

Work, which is carried out between 6 p.m. and 10 p.m. at least 2 hours.

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.

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 aged 20–24 having completed at least upper secondary education as percentage of the population in the same age group.

Data availability

Dissemination format and Release calendar

Quarterly data on 1st quarter are published in May, on 2nd quarter – in August, on 3rd quarter – in November and on 4th quarter – in February of the following year. Employment and Unemploymentquarterly data

The publication containing annual data is released in February. Employment and Unemploymentannual data

News Releases


Date Title
23.02.2017 Labour Force Survey in the 4th quarter of 2016 and in 2016
18.05.2017 Labour Force Survey in the 1st quarter of 2017
17.08.2017 Labour Force Survey in the 2nd quarter of 2017
16.11.2017 Labour Force Survey in the 3rd quarter of 2017

Publications

Download CSB publications on various time periods (starting from 2007) in section E-publications and subscribe for paper publications in section Publications.

Data on employment and unemployment are published also in the monthly bulletins and in the statistical yearbooks. Publications are available in the E-publications section under topic "General Statistics". 

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);
  • Latvian Classification of Occupations based on the International Standardized Classification of Occupations (ISCO-88 (COM), starting from 2011 basing on ISCO-08);
  • National Classification of Education of the Republic of Latvia, which is aligned 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 by Status in Employment (ICSE-93).

Classifications are available on RAMON Eurostat's Metadata Server.

Customised data sets

If you would like to obtain statistical data that are not available in publications or in the CSB online data base, please send us an information request:
 - postal mail: 1 Lāčplēša Street, Riga, Latvia, LV-1301;
 - e-mail: info [at] csb [dot] gov [dot] lv;
 - visiting Information centre.

Read more

Additional information

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.

Carrying out recalculation of the results of Labour Force Survey accordingly results of the Population and Housing Census 2011 on 2001–2006, methodology previously used in calculations has been changed. During the recalculation methodology developed by Eurostat was used in calculations for 2001–2006 and the calculations did not include persons serving in compulsory military service.

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.

Data collection

Survey method and source data

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.

The objective of the LFS is to obtain detailed information about the situation on the labour market in Latvia, i.e. activity of the population.

The survey is carried out by way of interviewing persons aged 15–74 years who are living within the household (prior to 2001 persons aged 15 years 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.).

Statistical population

The LFS has two target populations:

  1. Resident population of Latvia aged 15–74 that during the reference period are living in private households;
  2. 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 used [1]. Calculations are carried out applying R package tempdisagg.

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.

Software

JDemetra+

Seasonal adjustment method

TRAMO/SEATS

Last model revision

for data on July 2015


 [1] Chow, G.C. and Lin, A.-L. (1971) Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series.

Statistical processing

Calculation methods

1995–2001

Sample

Labour Force Survey (LFS) was launched for the first time in November 1995 [1]. 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 until 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 usedto 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;
  • four levels of urbanisation of the place of residence (Riga, eight cities, towns and rural areas);
  • by region of the palce of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).

The registered number of unemployed persons provided 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 2000 [2]. The new LFS methodology had the following key objectives:

  1. to obtain quarterly estimates;
  2. to organise the LFS as a continuous survey;
  3. 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 size. [3]. 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 method [4]. 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;
  • four levels of urbanisation of the place of residence (Riga, eight cities, towns and rural areas);
  • by region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).

The registered number of unemployed persons provided 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 time 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 method [5]. 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;
  • four levels of urbanisation of the place of residence (Riga, eight cities, towns and rural areas);
  • by region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).

The registered number of unemployed persons provided by the State Employment Agency in breakdown by sex and age group was used as auxiliary information.

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;
  • four levels of urbanisation of the place of residence (Riga, eight cities, towns and rural areas);
  • by region of the place of residence (Riga, Pierīga, Vidzeme, Latgale, Kurzeme and Zemgale).

The registered number of unemployed persons provided 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;
  • four levels of urbanisation of the 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 registered number of unemployed persons provided by the State Employment Agency in breakdown by sex and age group was used as auxiliary information for weight calibration.

In turn, as of 2014, quarterly average statistical data on population living in private households preserving the above-mentioned breakdown by age, sex level of urbanisation and 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.

In 2015 weight methodology was improved with additional non-response adjustment in breakdown by survey data collection form (direct interviews and telephone interviews).

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 confidence interval.  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 confidence interval – standard error must be multiplied by 1.961

Conf. Int. (+/–) = 6.14 * 1.961 = 12.03

1Coefficient used for the 95% confidence interval.

Confidence interval – an interval, which with a certain credibility (usually 95%) covers the actual or true value of the indicator of the population under study.

When comparing estimates, it is important to use confidence intervals to determine whether differences between values are statistically significant.

Example: if estimate is 90.0 and confidence interval (+/–) 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 confidence interval) are available for the following tables:

Reliability limits

Quarterly data (thousands)

 

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011–2013

2014 2015 2016

A

6.5–10.6

8.6–14.0

6.0–9.8

6.4–10.4

7.4–12.1

3.7–5.9

3.8–6.2

4.5–7.3

5.4–8.8

4.5–7.2

3.9–6.2 3.7–5.9 3.6–5.8

B

0.0–6.4

0.0–8.5

0.0–5.9

0.0–6.3

0.0–7.3

0.0–3.6

0.0–3.7

0.0–4.4

0.0–5.3

0.0–4.4

0.0–3.8 0.0–3.6 0.0–3.5

Annual data (thousands)

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013 2014 2015

A

12.9–21.1

2.6–4.1

3.6–5.7

2.0–3.2

2.2–3.4

2.0–3.2

1.0–1.5

1.1–1.7

1.7–2.6

2.2–3.5

1.9–3.0

1.6–2.5

1.7–2.7 1.5–2.3 1.3–2.0

B

0.0–12.8

0.0–2.5

0.0–3.5

0.0–1.9

0.0–2.1

0.0–1.9

0.0–0.9

0.0–1.0

0.0–1.6

0.0–2.1

0.0–1.8

0.0–1.5

0.0–1.6 0.0–1.4 0.0–1.2

Additional information:

A – Quarterly and annual estimates are published with a notice, if variation coefficient varies between 21% and 27% (for 2011 quarterly estimates between 25–32%).

B – Quarterly and annual estimates are not published, if variation coefficient exceeds 27% (for 2011 quarterly estimates exceeds 32%).


[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.

Comparability

Comparability over time

Key indicators of the Labour Force Survey are available since 1996, whereas quarterly data since 2002.

International comparability

Eurostat

The Statistical Office of the European Union (Eurostat) publishes on its home page information on employment and unemployment in the countries of the European Union. This information can be found in the section: Statistics/ Statistics A-Z/ D-E-F/ Employment and unemployment.

Eurostat also compiles information on the labour market in the section: Statistics/ Population and social conditions/ Labour market (including the Labour Force Survey).

http://ec.europa.eu/eurostat

International Labour Organisation

The database LABORSTA of the International Labour Organisation (ILO) provides annual data both on employment in the European countries (including Latvia) and information on other regions worldwide (America, Africa, Asia and Australia).

http://laborsta.ilo.org/

International Monetary Fund

The International Monetary Fund (IMF) publishes employment statistics on its homepage.

http://dsbb.imf.org/

Other comparability

State Employment Agency

The State Employment Agency publishes employment statistics on its homepage.

http://www.nva.gov.lv/

Contact person on methodology

Name Surname Phone number Position Email
Zaiga Priede 67366886 vadītājs Zaiga.Priede@csb.gov.lv

Last update

18.05.2016

Explanation of symbols

-

Magnitude zero

0

Less than half of the unit employed

...

Data not available or too uncertain for presentation

X

Figure not applicable because column headinng and stub line make entry impossible, absurd or meaningless

.

Data not released for confidentiality reasons

If data are absolute numbers

0

Magnitude zero