The share of law students who graduated in 2023 and were looking for work was 5 percent. This was a slight decrease when compared the previous year, with a slightly lower amount of graduates in 2023.

This statistic illustrates the unemployment rate of legal professionals in the United states in the second quarter of 2017. In that period, some *** percent of the lawyers were not employed in the U.S.

Context

Long-term unemployment refers to people who have been unemployed for 12 months or more. The long-term unemployment rate shows the proportion of these long-term unemployed among all unemployed. Unemployment is usually measured by national labour force surveys and refers to people reporting that they have worked in gainful employment for less than one hour in the previous week, who are available for work and who have sought employment in the past four weeks. Long-term unemployment causes significant mental and material stress for those affected and their families. It is also of particular concern for policy makers, as high rates of long-term unemployment indicate that labour markets are operating inefficiently. This indicator is measured as a percentage of unemployed.

https://data.oecd.org/unemp/long-term-unemployment-rate.htm

Dataset

This dataset was created by VasL

Contents

Analysis of ‘Recorded unemployment, January 2021 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/e0526164-80a3-498e-bd03-5f4e9e7123e6 on 18 January 2022.

--- Dataset description provided by original source is as follows ---

ANOFM calculates and publishes statistical indicators on registered unemployment, as required by the law. Registered unemployed persons represent both the unemployed paid (unemployed jobseekers with work experience benefits and SOMERI recipients of unemployment benefits without work experience/education graduates) as well as the unemployed (without receiving unemployment benefits) and are squeezed on the basis of data from the primary documents and records in the database of territorial employment agencies. Is the stock at the end of the reference month. The unemployment rate recorded is determined as the ratio between the number of unemployed persons registered with the county and Bucharest employment agencies (paid and unpaid) at the end of the reference month and the active civilian population. The civilian active population represents the potential labour supply and employment of the civilian and registered unemployed population. The indicator is determined annually by the National Institute of Statistics by means of the balance of labour at country, development region and county level. The rate of summons is calculated with the population of civil activity on 1 January 2017. The total number of registered SOMERI is structured on: Gender (women, Barbate), Type of compensation (indemnities, non-indemnities); Level of education (without education, primary education, secondary education, upper secondary education, postgraduate education, professional education/arts and trades, university education); Age groups (under 25, 25-29, 30-39, 40-49, 50-55 years, over 55 years). Average residency (urban, rural).The ANOFM calculates and publishes statistics on registered unemployment in accordance with the legal provisions. Registered unemployed persons represent both the unemployed paid (unemployed jobseekers with work experience benefits and SOMERI recipients of unemployment benefits without work experience/education graduates) as well as the unemployed (without receiving unemployment benefits) and are squeezed on the basis of data from the primary documents and records in the database of territorial employment agencies. Is the stock at the end of the reference month. The unemployment rate recorded is determined as the ratio between the number of unemployed persons registered with the county and Bucharest employment agencies (paid and unpaid) at the end of the reference month and the active civilian population. The civilian active population represents the potential labour supply and employment of the civilian and registered unemployed population. The indicator is determined annually by the National Institute of Statistics by means of the balance of labour at country, development region and county level. The rate of summons is calculated with the population of civil activity on 1 January 2017. The total number of registered SOMERI is structured on: Gender (women, Barbate), Type of compensation (indemnities, non-indemnities); Level of education (without education, primary education, secondary education, upper secondary education, postgraduate education, professional education/arts and trades, university education); Age groups (under 25, 25-29, 30-39, 40-49, 50-55 years, over 55 years). Residential environments (urban, rural).

--- Original source retains full ownership of the source dataset ---

Context

This is a dataset that I built by scraping the United States Department of Labor's Bureau of Labor Statistics. I was looking for county-level unemployment data and realized that there was a data source for this, but the data set itself hadn't existed yet, so I decided to write a scraper and build it out myself.

Content

This data represents the Local Area Unemployment Statistics from 1990-2016, broken down by state and month. The data itself is pulled from this mapping site:

https://data.bls.gov/map/MapToolServlet?survey=la&map=county&seasonal=u

Further, the ever-evolving and ever-improving codebase that pulled this data is available here:

https://github.com/jayrav13/bls_local_area_unemployment

Acknowledgements

Of course, a huge shoutout to bls.gov and their open and transparent data. I've certainly been inspired to dive into US-related data recently and having this data open further enables my curiosities.

Inspiration

I was excited about building this data set out because I was pretty sure something similar didn't exist - curious to see what folks can do with it once they run with it! A curious question I had was surrounding Unemployment vs 2016 Presidential Election outcome down to the county level. A comparison can probably lead to interesting questions and discoveries such as trends in local elections that led to their most recent election outcome, etc.

Next Steps

Version 1 of this is as a massive JSON blob, normalized by year / month / state. I intend to transform this into a CSV in the future as well.

In Denmark, the highest unemployment rate in 2022 could be found among youth between 25 and 29 years, who had an unemployment rate of 4.4 percent that year. People between 30 and 34 years had the second highest unemployment rate. On the other hand, the lowest unemployment rate was among people between 50 and 54 years of age. The unemployment rate in Denmark rose sharply in 2020 following the outbreak of COVID-19, but decreased again the following years.

 Receiving unemployment benefits in Denmark 

By law, Danes have the right to receive unemployment benefits. There are requirements to be eligible for unemployment benefits in Denmark: being a member of an unemployment insurance fund called “A-kasse” is necessary as well as having earned a specific amount of money in the past. In addition, being registered at the Public Employment Service is required and as of January 2019, only people who have stayed in Denmark, Greenland, Faroe Islands or another EU/EEA country in seven out of the last eight years can claim unemployment benefits.

Labor market

Since 2012, the number of employed Danes was growing each year, but experienced a setback in 2020 due to COVID-19. The Danish labor market is known for the Flexicurity Model – a combination of market economy and a welfare state, and due to this model, the labor market can reflect the needs of employers and guarantee the welfare of employees which creates many possibilities for both sides.

Financial overview and grant giving statistics of Unemployment Law Project

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels.

This bi-annual publication provides information on each state’s wage requirements for unemployment insurance benefit eligibility, computation and amount of the weekly benefit, number of allowable benefit weeks and benefit week calculation, and the amount of earnings that will be disregarded for those individuals who are working part-time. It also provides information on the size of employer payroll required to pay unemployment taxes, the amount of wages subject to unemployment taxes, and the tax rates specific to each state’s program.

Ukraine Registered Unemployment: Law Makers, Highest Government Officials & Managers data was reported at 64.500 Person th in 2016. This records a decrease from the previous number of 67.100 Person th for 2015. Ukraine Registered Unemployment: Law Makers, Highest Government Officials & Managers data is updated yearly, averaging 55.050 Person th from Dec 1999 (Median) to 2016, with 18 observations. The data reached an all-time high of 67.100 Person th in 2015 and a record low of 47.500 Person th in 2007. Ukraine Registered Unemployment: Law Makers, Highest Government Officials & Managers data remains active status in CEIC and is reported by State Statistics Service of Ukraine. The data is categorized under Global Database’s Ukraine – Table UA.G014: Registered Unemployment: Annual.

Portugal's graduate unemployment landscape between 2020 and 2024 reveals a striking imbalance across fields of study. Business sciences, administration, and law graduates faced the highest unemployment rate at 25.7 percent, while information and communication technologies (ICT) graduates experienced the lowest at 1.8 percent. The social sciences, journalism, and information field and arts and humanities presented the second and third-highest shares of unemployed graduates registered in employment centers, with 18 and 15.7 percent, respectively. Rising graduate numbers, persistent gender gap  The number of higher education graduates in Portugal has more than doubled since the late 1990s, reaching over 95,600 in the 2022/2023 academic year. Women consistently outnumbered men among graduates, with nearly 56,000 female graduates compared to 40,000 male graduates in the most recent year. However, this gender gap reversed in science, technology, engineering, and mathematics (STEM) fields, where men accounted for 65 percent of graduates across all study cycles during the 2022/2023 academic year. Growing higher education enrollment  Despite the increasing number of graduates, the unemployment rate for the youth has been decreasing slowly since the end of 2023. The positive trend occurred as higher education enrollment continues to grow, with over 446,000 students in the 2022/2023 academic year. Universities attract more students than polytechnic institutes across all regions, with Greater Lisbon hosting the largest student population of over 147,000, despite not being the country’s region with the highest number of higher education establishments.

Background: This study investigates the influence of regional minimum wages (RMW), gross domestic product (GDP), and inflation on Indonesia's unemployment rates from 2012 to 2020. Methods: Multiple linear regression analysis examines the relationships between these economic variables. Findings: The findings reveal that RMW significantly negatively affects unemployment rates, indicating that a 1% increase in the minimum wage leads to a 3.951% decrease in unemployment, ceteris paribus. GDP also exhibits a significant negative influence, aligning with Okun's law, which suggests an inverse relationship between economic growth and unemployment. In contrast, inflation does not significantly impact unemployment rates during the studied period. Collectively, the three variables positively and significantly affect Indonesia's unemployment rate, with an adjusted R-squared value of 0.749. This implies that 74.9% of the variation in unemployment can be explained by GDP, inflation, and minimum wages, while other factors account for the remaining 25.1%. Conclusion: The study highlights the complex interplay between these macroeconomic indicators and unemployment, providing insights for policymakers to develop effective strategies for managing employment challenges in Indonesia. Novelty/Originality of this article: This empirical analysis reveals the dynamic relationship between RMW, GDP, inflation, and unemployment in Indonesia (2012—2020). The findings provide an evidence-based basis for formulating more effective and responsive employment and economic policies for Indonesia's labour market conditions.

ANOFM calculates and publishes statistical indicators on registered unemployment, according to the legal provisions. The number of registered unemployed represents both the unemployed compensated (unemployed persons with experience in work and unemployed unemployed inexperienced unemployment benefits/education graduates) and the unemployed unemployed (without unemployment benefit) and are based on the data from the primary documents and the records from the database of the Territorial Employment Agencies. Represents the stock at the end of the reference month. The unemployment rate recorded is determined as the ratio between the number of unemployed registered with the county employment agencies and the municipality of Bucharest (allowed and unpaid) at the end of the reference month and the civil active population. The civilian active population represents the potential labour supply and employment rate of the population comprising the civilian-occupied population and the registered unemployed. The indicator is determined annually by the National Institute of Statistics through the labour force balance at the level of the country, development region and county. The total number of registered unemployed is structured on: sexes (women, men); type of compensation (allowed, non-allowed); education level (without studies, primary education, secondary education, secondary education, post-iceal education, vocational education/arts and crafts, university education); age groups (under 25 years, 25-29 years, 30-39 years, 40-49 years, 50-55 years, over 55 years). residential environments (urban, rural). ANOFM calculates and publishes statistical indicators on registered unemployment, according to the legal provisions.

The NEA calculates and makes public the statistical indicators on registered unemployment, according to the legal provisions. The number of registered unemployed persons represents both the unemployed persons receiving benefits with work experience and the unemployed persons receiving unemployment benefits without work experience/ education graduates) and the unemployed persons not receiving benefits (without benefiting from unemployment benefits) and is calculated on the basis of the data from the primary documents and the records from the database of the territorial employment agencies. Represents the stock at the end of the reference month. The registered unemployment rate is determined as the ratio between the number of unemployed persons registered with the employment agencies in the county and the municipality of Bucharest (remunerated and unremunerated) at the end of the reference month and the active civilian population. The civilian active population represents the potential labour supply and employment of the population comprising the civilian employed population and the registered unemployed. The indicator is determined annually by the National Institute of Statistics through the labour balance at country, development region and county level. The total number of registered unemployed is structured by: - sexes (women, men); - type of compensation (remunerated, unremunerated); - level of education (without education, primary education, secondary education, secondary education, post-secondary education, vocational education/arts and crafts, university education); age groups (under 25 years, 25-29 years, 30-39 years, 40-49 years, 50-55 years, over 55 years). - Residential environments (urban, rural). The NEA calculates and makes public the statistical indicators on registered unemployment, according to the legal provisions.

As of April 2024, almost ** percent of law students who graduated in 2023 in the United States were employed in law firm positions, while **** percent were working for the government.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI.

Apart from a few individual studies the labor market and it’s segregation, the relation between supply and demand, employment structure, and unemployment is until today not analyzed in its historical dimension. The same applies to the history of labor market policy. The present study’s aim is to fill this gap by describing the most important elements of the labor market policy: the employment service, the job creation, and the unemployment benefit. The researcher addresses one of the most important problems of the modern, on the intense division of labor based economy: the labor market coverage with highly skilled workers. The reason for the complexity of this task lies in the strong segmentation of the labor market (numerous sub-markets and sectors with different requirements on qualifications) and – since the industrialization – the fact, that the labor market is in a process of constant, sometimes short term changes. To manage this situation, market transparency to the largest possible extent is necessary, which is a central field of the employment service’s responsibility. To this basic function (the supply of the labor market with adequate skilled workers, called by Anselm Faust ‘market function’) further important functions are attached, for example the prevention of unemployment. The not commercially labor service was expanded in Germany to an inherent part of modern labor market policy in a period between 30 and 40 years. The labor service was in the end of the 19th century insignificant and both institutionally and in terms of a policy of interests fragmented. But in 1927 it was integrated by the law about labor service and unemployment insurance into a system of coordinated public institutions and public policies. The purpose of this new implemented law is to balance and to influence the labor market, the employment policy, and to ensure a basic social care of the unemployed. The goals of the labor market policy, developed in a long historical process til today, can be summarized as follows: - to influence quantity, composition and qualification of possible and actual labor force in direction to an optimal structure and development; - to induce the best possible adaption between available labor force and working places; - to use the labor force productively, fully and continuously to enable the individual and public increase in welfare or benefit; - to protect the economically active population from the consequences of unemployment. The preset study addresses the most important elements in historical view and in policy terms, i.e. the labor service, job creation, and unemployment compensation. The labor market is the place to meet demand and supply. Therefore, labor service is the organized market process and the contact point, where supply and demand for labor does coincide. The history of labor service, job creation, and unemployment compensation in Germany between 1890 and 1918 is analyzed in terms of: - its social and economical preconditions: the structure of the labor market, the development and social meaning of employment and unemployment; - the theoretical and ideological subsumption as well as the social interests derived from the social and economic conditions. - the function of labor market policy within the German ‘Kaiserreich’s’ (German Royal empire’s) conflicts of interests and the political importance of labor market policy, resulting from the conflicts of interests; - the strategies for solving the labor market conflicts, the actions and investments and their organizational arrangement; - the government’s part by solving conflicts and organizing the labor market; - the relevance of labor market instruments to organize labor market processes and to protect the unemployed. (see: Faust, A., 1986: Arbeitsmarktpolitik im Deutschen Kaiserreich. Arbeitsvermittlung, Arbeitsbeschaffung und Arbeitslosenunter¬stützung 1890-1918. Stuttgart: Franz Steiner, S. 2f, S. 10).

Datatables in the search- and downloadsystem HISTAT (Topic: Erwerbstätigkeit (=employment) ) Annotation: HISTAT is offered in German.

A. Arbeitslosigkeit (=Unemployment)

A.01 Arbeitsgesuche auf 100 offene Stellen (1907-1918) (number of applications to 100 vacancies) A.02 Die Arbeitslosenquote in den Gewerkschaften (1904-1918) (unemployment rate in lobor unions) A.03 Die Arbeitslosenquoten in den Gewerkschaftsverbänden (1904-1918) (unemployment rates in trade union associations) A.04 die geschlechtsspezifische Arbeitslosenqu...

Since the oil price shock in 1974 unemployment increased significantly and also did not really decline in periods of economic upswings in Europe. This is especially the case for the countries of the European Union; therefore we face a special need for explanation. Looking at the member states on finds considerable differences. Since 1977 the unemployment rate within the EU is higher than the average unemployment rate of all OECD countries. The economic upswing in the second half of the 80s relaxed the labor market but nevertheless the unemployment rate remained on a high level. This study deals with the development of unemployment between 1974 and 1993 in four different G7 countries: Germany, France, Great Britain and Italy.

Besides the common trend of an increasing unemployment rate, there are significantly different developments within the four countries. The analysis is divided in two parts: the first part looks at the reasons for the increase in unemployment in the considered countries; the second part aims to explain the difference between the developments of unemployment during economic cycles in the different countries.

After the description of similarities and differences of labor markets in the four countries it follows a long term analysis based on annual data as well as a short and medium term analysis on quarterly data. This is due to the fact that short and medium term developments are mainly influenced by cyclical economic developments but long term developments are mainly influenced by other factors like demographical and structural changes. A concrete question within this framework is if an increase in production potential can contribute to a decrease in unemployment.

For the long term analysis among others the Hysteresis-hypothesis (Hysteresis = Greek: to remain; denotes the remaining effect; in this context: remaining of unemployment) used for the explanation of the persistence of a high unemployment rate.

According to this approach consisting unemployment is barely decreased after economic recovery despite full utilization of capacity. According to the Hysteresis-hypothesis there are two reasons for this. The first reason is that for long term unemployed the abilities to work and the qualification level decreased, their human capital is partly devalued. The second reason is that employees give up wage restraint, because they do not fear unemployment anymore and therefore enforce higher real wages. Besides economic recovery companies are not willing to hire long term unemployed with a lower expected productivity for the higher established tariff wages. In the context of the empirical investigation a multiple explanatory approach is chosen which takes supply side and demand side factors into consideration.

The short and medium term analysis refers to Okun´s law (=an increase in the unemployment rate is connected with a decrease of the GDP; if the unemployment rate stays unchanged, the GDP grows with 3% p.a.) and aims to analyze more detailed the reactions of unemployment to economic cycles. A geometrical lag-model is compared with a lag-model ager Almon. This should ensure a precise as possible analysis of the Okun´s relations and coefficients.

Register of tables in HISTAT:

A.: Unemployment in the European G7 countries B.: Analysis of unemployment in the Federal Republic of Germany C.: Basic numbers: International comparison

A.: Unemployment in the European G7 countries A.1. Determinates of unemployment in the EU, Germany (1974-1993) A.2. Determinates of unemployment in the EU, France (1974-1993) A.3. Determinates of unemployment in the EU, Great Britain (1974-1993) A.4. Determinates of unemployment in the EU, Italy (1974-1993)

B: Analysis of unemployment in the Federal Republic of Germany B.1. Growth of unemployment in the Federal Republic of Germany (1984-1991) B.2. Output and unemployment in the Federal Republic of Germany (1961-1990)

C: Basic numbers: International comparison C.1. Unemployment in EU countries, the USA, Japan and Switzerland (1960-1996) C.2. Gainful employments in EU countries, the USA, Japan and Switzerland (after inland and residency concept) (1960-1996) C.3. Employees in EU countries, the USA and Japan (1960-1996) C.4. Population in EU countries, the USA and Japan (1960-1996)

Abstract

Objectives:

The Labor Force Survey (LFS) aims to provide a quantitative framework for the preparation of plans and formulation of policies affecting the labor market.

Specifically, the survey is designed to provide statistics on levels and trends of employment, unemployment and underemployment for the country as a whole, and for each of the administrative regions.

Importance of the Labor Force Survey:

a. It provides a quantitative framework for the preparation of plans and formulation of policies affecting the labor market towards 1) creation and generation of gainful employment and livelihood opportunities 2) reduction of unemployment and promotion of employment 3) improvement of working conditions 4) enhancement of the welfare of a working person b. It provides statistics on levels and trends of employment and unemployment and underemployment for the country and regions; c. It is used for the projection of future manpower, which when compared with the future manpower requirements, will help identify employment and training needs; d. It helps in the assessment of the potential human resource available for economic development; and e. It identifies the differences in employment, unemployment, and underemployment according to the different economic, social and ethnic groups existing within the population.

Geographic coverage

The geographic coverage consists of the country's 17 administrative regions. The 17 regions are:

Region I - Ilocos,
Region II - Cagayan Valley, Region III - Central Luzon, Region IV-A - Calabarzon, Region IV-B - Mimaropa Region V - Bicol,
Region VI - Western Visayas,
Region VII - Central Visayas,
Region VIII - Eastern Visayas,
Region IX - Zamboanga Peninsula,
Region X - Northern Mindanao,
Region XI - Davao,
Region XII - Soccksargen,
Region XIII - National Capital Region (NCR),
Region XIV - Cordillera Administrative Region (CAR),
Region XV - Autonomous Region in Muslim Mindanao (ARMM) Region XVI - Caraga,

Starting this July 2003 round of the Labor Force Survey, the generation of the labor force and employment statistics adopted the 2003 Master Sample Design. - Using this new master sample design, the number of samples increased from 41,000 to around 51,000 sample households. - The province of Basilan is grouped under Autonomous Region in Muslim Mindanao while Isabela City (Basilan) is now grouped under Region IX. This is in consonance with the regional grouping under Executive Order No. 36. - The 1992 four-digit code for Philippine Standard Occupational Classification (PSOC) and 1994 Philippine Standard Industry Classification (PSIC) were used in classifying the occupation and industry. - Because of unavailability of data files for the province of Zamboanga Sibugay of Region IX and the provinces of Sulu and Lanao del Sur of ARMM on cut-off date, estimates at the national level and for the two regions exclude those of the said three provinces. Estimates for the three provinces will be included in the Final Results.

Analysis unit

Individuals

Universe

The LFS has as its target population, all household members of the sample housing units nationwide. A household is defined as an aggregate of persons, generally but not necessarily bound by ties of kinship, who live together under the same roof and eat together or share in common the household food. Household membership comprises the head of the household, relatives living with him such as his or her spouse, children, parent, brother or sister, son-in-law or daughter-in-law, grandson or granddaughter, and other relatives. Household membership likewise includes boarders, domestic helpers and non-relatives. A person who lives alone is considered a separate household.

Persons who reside in the institutions are not within the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design of the Labor Force Survey (LFS) uses the sampling design of the 2003 Master Sample (MS) for Household Surveys that started July 2003.

Sampling Frame

As in most household surveys, the 2003 MS used an area sample design. The Enumeration Area Reference File (EARF) of the 2000 Census of Population and Housing (CPH) was utilized as sampling frame. The EARF contains the number of households by enumeration area (EA) in each barangay. This frame was used to form the primary sampling units (PSUs). With consideration of the period for which the 2003 MS will be in use, the PSUs were formed/defined as a barangay or a combination of barangays with at least 500 households.

Stratification Scheme

Startification involves the division of the entire population into non-overlapping subgroups called starta. Prior to sample selection, the PSUs in each domain were stratified as follows: 1) All large PSUs were treated as separate strata and were referred to as certainty selections (self-representing PSUs). A PSU was considered large if it has a large probability of selection. 2) All other PSUs were then stratified by province, highly urbanized city (HUC) and independent component city (ICC). 3) Within each province/HUC/ICC, the PSUs were further stratified or grouped with respect to some socio-economic variables that were related to poverty incidence. These variables were: (a) the proportion of strongly built houses (PSTRONG); (b) an indication of the proportion of households engaged in agriculture (AGRI); and (c) the per-capita income (PERCAPITA).

Sample Selection

To have some control over the subsample size, the PSUs were selected with probability proportional to some estimated measure of size. The size measure refers to the total number of households from the 2000 CPH. Because of the wide variation in PSU sizes, PSUs with selection probabilities greater than 1 were identified and were included in the sample as certainty selections.

At the second stage, enumeration areas (EAs) were selected within sampled PSUs, and at the third stage, housing units were selected within sampled EAs. Generally, all households in sampled housing units were enumerated, except for few cases when the number of households in a housing unit exceeds three. In which case, a sample of three households in a sampled housing unit was selected at random with equal probability.

An EA is defined as an area with discernable boundaries within barangays, consisting of about 150 contiguous households. These EAs were identified during the 2000 CPH. A housing unit is a structurally separate and independent place of abode which, by the way it has been constructed, converted, or arranged, is intended for habitation by a household

Sample Size

The 2003 Master Sample consist of a sample of 2,835 PSUs of which 330 were certainty PSUs and 2,505 were non certainty PSUs. The number of households for the 2000 CPH was used as measure of size. The entire MS was divided into four sub-samples or independent replicates, such as a quarter sample contains one fourth of the PSUs found in one replicate; a half-sample contains one-half of the PSUs in two replicates. Thus, the survey covers a nationwide sample of about 51,000 households deemed sufficient to measure the levels of employment and unemployment at the national and regional levels.

Strategy for non-response

Replacement of sample households within the sample housing units is allowed only if the listed sample households had moved out of the housing unit. Replacement should be the household currently residing in the sample housing unit previously occupied by the original sample.

Sampling deviation

Starting the July 2003 round of the Labor Force Survey, the generation of the labor force and employment statistics adopted the 2003 Master Sample Design. - Using this new master sample design, the number of samples increased from 41,000 to around 51,000 sample households.

• The province of Basilan is grouped under Autonomous Region in Muslim Mindanao while Isabela City (Basilan) is now grouped under Region IX. This is to adopt the regional grouping under Executive Order No.36.
• The 1992 four-digit code for Philippine Standard Occupational Classification (PSOC) and 1994 Philippine Standard Industry Classification (PSIC) were used in classifying the occupation and industry.

Mode of data collection

Face-to-face [f2f]

Research instrument

ISH FORM 2 (LFS questionnaire) is a four-page, forty four-column questionnaire that is being used in the quarterly rounds of the Labor Force Survey nationwide. This questionnaire gathers data on the demographic and economic characteristics of the population.

On the first page of the questionnaire, the particulars about the geographic location, design codes and household auxiliary information of the sample household that is being interviewed are to be recorded. Certifications by the enumerator and his supervisor regarding the manner by which the data are collected are likewise to be made on this page.

The inside pages of the questionnaire contain the items to be determined about each member of the sample household. Columns 2 to 11 are for the demographic characteristics; columns 2 to 7A are to be ascertained of all members of the household regardless of age. Columns 8 to 9 are asked for members 5 years old and over, while column 10 is asked for members 5 to 24 years old, column 11, for 15 years old and over, while columns 12 to 16 are asked for members 5 years old and over. Items 18 to 44 on the other hand, are the series of items that will be asked of all the members 15 years old and over to determine their labor force and employment characteristics.

Most of the