The construction sector employed almost 8.3 million people in the United States in January 2025, which was the highest number since the 21st century. There is a strong correlation between the amount of investment in construction and demand for workers. For example, in the years following the 2008 financial crisis, the value of new construction put in place in the U.S. decreased, which also translated in lower employee numbers in the construction sector. How to improve the job shortage? Many contractors have reported difficulty finding skilled workers recently. However, that has not only been the case in the construction industry, but in many other sectors of the economy too. For example, U.S. restaurants reported shortages in different positions in the past years. Although there are many reasons why workers may quit, in general, an increase in the salaries of construction employees may help in reducing the number of resignations. Worker shortages in Europe The United States is not the only country where companies have been facing these challenges. Thus, the percentage of French infrastructure companies reporting staff shortage peaked in 2019 and 2023. However, there are certain industries that struggle finding new employees more than construction. Social and care work had the highest skilled labor shortages in Germany.

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 Arbeitslosenquote in den Gewerkschaftsverbänden (1914-1918) (gender-specific unemployment rate in trade union associations) A.05 die Arbeitslosenquoten in den Angestelltenverbänden (1908-1918) (unemployment rate in clerical worker unions)

B. Arbeitsnachweise (= employment agency; labor service)

B.01a Die nichtgewerbsmäßigen Arbeitsnachweise, Anzahl (1904-1927) (Not commercial labor service, number) B.02b Der Vermittlungsanteil der öffentlichen Arbeitsnachweise (1913-1928) (share of agency of jobs of...

The market for artificial intelligence (AI) is expected to show significant growth in the coming decade, according to a variety of sources. According to Statista data, the AI market size is projected to rise from 241.8 billion U.S. dollars in 2023 to almost 740 billion U.S. dollars in 2030, accounting for a compound annual growth rate of 17.3%. Meanwhile, according to  Next Move Strategy Consulting, its value of approximately 208 billion U.S. dollars in 2023 is expected to grow ninefold by 2030, reaching around 1.85 trillion U.S. dollars. Indeed, the AI market covers a vast number of industries, including healthcare, education, finance, media and marketing. The rate of adoption and deployment of the technology is becoming more prolific worldwide. Chatbots, image-generating AI, and mobile applications are all among the major trends that will enhance AI in the coming years.

AI demands data

Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together these bodies pose significant challenges to data-hungry AI companies.

AI could boost productivity growth

Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on a variety of factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

Number of job vacancies, labour demand and job vacancy rate by North American Industry Classification System (NAICS), last 5 months.

The market for artificial intelligence grew beyond 184 billion U.S. dollars in 2024, a considerable jump of nearly 50 billion compared to 2023. This staggering growth is expected to continue with the market racing past 826 billion U.S. dollars in 2030. AI demands data Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together these bodies pose significant challenges to data-hungry AI companies. AI could boost productivity growth Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on a variety of factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

Number of job vacancies, labour demand and job vacancy rate by North American Industry Classification System (NAICS), last 5 years.

The data and programs replicate tables and figures from "Can I live with you after I retire? Retirement, old age support, and internal migration in a developing country", by Chen, Jin, and Prettner. Please see the ReadMe file for additional details.

Labour Force Demand: FE: Sakhalin Region在2025-01达8,245.000人口，相较于2024-12的8,779.000人口有所下降。Labour Force Demand: FE: Sakhalin Region数据按月度更新，1999-05至2025-01期间平均值为7,578.000人口，共309份观测结果。该数据的历史最高值出现于2014-10，达32,238.000人口，而历史最低值则出现于2000-02，为1,134.000人口。CEIC提供的Labour Force Demand: FE: Sakhalin Region数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GB027: Labour Force Demand: by Region。

Abstract

The Turkish National Employment Agency (ISKUR) provides services for individuals who register as unemployed through 109 offices in 81 provinces. The impact evaluation study was designed by researchers from the World Bank to evaluate the impact of the ISKUR vocational training programs. These programs average 336 hours over three months are available for a wide range of subjects, and are offered by both private and public providers. These training services were provided to over 250,000 registered unemployed in 2011.

The Turkey Vocational Training Program for the Unemployed Impact Evaluation 2010-2012 was the first randomized experiment of a large-scale vocational training program for the general unemployed population (not just for disadvantaged youth) in a developing country. The program was able to trace longer-term impacts up to three years post-training, by complementing a follow-up survey with administrative data from the social security agency. A sample of 5,902 applicants was randomly allocated to treatment and control groups within 130 separate courses. Excess demand among the unemployed for many of the courses offered by ISKUR provided the possibility for an over-subscription design. The evaluation was carried out in collaboration with ISKUR and under the guidance of the Ministry of Labor.

The baseline survey took place between 13 September, 2010, and 31 January, 2011. The follow-up survey was implemented approximately one year after the end of training, between December 27, 2011, and March 5, 2012. It collected data on employment outcomes, as well as individual and household well-being.

Geographic coverage

National

Analysis unit

• Vocational programs' students,
• Vocational programs' staff

Kind of data

Sample survey data [ssd]

Sampling procedure

The selection of provinces for evaluation began with a list of the 39 provinces which had at least two significantly oversubscribed training courses in 2009. These provinces were first stratified by whether they had an unemployment rate above or below the median of 10 percent in 2009. Ten provinces were then randomly selected from each strata with probability proportional to the percentage of individuals trained in 2009. Three additional provinces (Antalya, Gaziantep, and Diyarbakir) were included in the sample at the request of ISKUR because of their importance in representing varying labor market conditions across Turkey. As a result, 23 provinces were selected for inclusion in the evaluation.

Power calculations gave a target sample size of 5,700 individuals. This target was divided among the 23 provinces in proportion to the number of trainees in these provinces in the previous year. Thus Istanbul accounts for 21.8 percent of the sample, Kocaeli, Ankara and Hatay collectively 28 percent, and the remaining half of the sample is split among the other 19 provinces.

The evaluation team worked with regional ISKUR offices to determine the actual courses from within each province to be included in the evaluation. The key criteria used to decide which courses to include in the evaluation were i) the likelihood of the course being oversubscribed (which ensures the most popular types of training, for which there would be demand for further scale-up, are included); ii) inclusion of a diversity of types of training providers to enable comparison of private and public course provision; and iii) course starting and ending dates. The evaluation includes courses that started between October and December 2010 and finished by May 2011 (75 percent had finished by the end of February 2011). The timing of the evaluation was determined by the fact that it tends to be a time of year when people in Turkey are more likely to seek training through ISKUR.

This resulted in a set of 130 evaluation courses spread throughout Turkey, of which 39 were offered by private providers and the remainder were mainly government-operated. Courses were advertised and potential trainees applied to them following standard procedures. Applications were then screened to ensure they met the eligibility criteria of ISKUR and the course provider. Training providers were then asked to select a list of potential trainees that was at least 2.2 times capacity.

The ISKUR Management Information System (MIS) stratified applicants for each course by gender and whether or not they were less than 25 years old. Within these strata, the MIS randomly allocated trainees at the individual level into one of three groups: a treatment group who were selected for training, a control group who were not, and a waitlisted group who the training provider could select into the training if there were drop-outs. Since training providers are paid on the basis of number actually trained, if individuals assigned to treatment drop out of training, providers look to quickly fill in the empty spots.

The final evaluation sample consisted of 5,902 applicants, of which 3,001 were allocated to treatment and 2,901 to control groups. There were 173 individuals who applied to more than one course.

Mode of data collection

Face-to-face [f2f]

Response rate

Baseline: 90% Follow-up: 94%

Labour Force Demand: SF: Republic of Adygea在2025-01达4,334.000人口，相较于2024-12的4,491.000人口有所下降。Labour Force Demand: SF: Republic of Adygea数据按月度更新，1999-05至2025-01期间平均值为3,813.000人口，共309份观测结果。该数据的历史最高值出现于2021-10，达7,521.000人口，而历史最低值则出现于1999-05，为433.000人口。CEIC提供的Labour Force Demand: SF: Republic of Adygea数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GB027: Labour Force Demand: by Region。

Labour Force Demand: NW: Murmansk Region在2025-01达8,961.000人口，相较于2024-12的10,992.000人口有所下降。Labour Force Demand: NW: Murmansk Region数据按月度更新，1999-05至2025-01期间平均值为8,797.000人口，共309份观测结果。该数据的历史最高值出现于2021-10，达35,291.000人口，而历史最低值则出现于1999-12，为2,593.000人口。CEIC提供的Labour Force Demand: NW: Murmansk Region数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GB027: Labour Force Demand: by Region。

This dataset is about book series and is filtered where the books is The impact of Everything But Arms on EU relative labour demand, featuring 10 columns including authors, average publication date, book publishers, book series, and books. The preview is ordered by number of books (descending).

Abstract

A 2002-2003 Labour Force Survey with Child Activities Module is a first national survey that ever conducted in Mongolia which captures all four quarters in order to elicit information on the seasonality in labour supply and demand. Particularly, the survey aimed at collection of comprehensive data on employment, underemployment, unemployment and child labour to enable the estimate of the related indicators by regions, sectors and social and economic categories. The overall objective of the survey was to build the national capacity for conducting employment and other household based socio-economic surveys and provide the data to benefit the policy making and planning for the national development and social welfare.

Geographic coverage

The survey is nationally and regionally (5 regions - West, Central, East, South, Ulaanbaatar) representative and covers the whole of Mongolia.

Analysis unit

• Household
• Individual

Universe

The survey covered all de jure household members aged 5 and over resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame derived from the Census of Population 2000 was used in the survey design. The institutional facilities such as hostels, army barracks, boarding houses, etc. were excluded from the frame and a truncated frame comprising ordinary households was prepared. Considering the socio-economic stratification of the main items canvassed through the survey it was considered that Mongolia should be classified into urban, rural and regional stratifications. Accordingly, Mongolia was divided into urban and rural areas and Ulaanbaatar, Central, East, West and Khangai regions. A two stage stratified random sampling design was adopted with baghs (census enumeration areas) as primary sampling units (PSUs) and households as secondary sampling units (SSUs). The frame which had baghs grouped by district and province in effect provided an implicit stratification for the PSUs for the probability proportional to size systematic random sampling procedure adopted in the selection of the PSUs. In order to capture seasonal variations in labour supply and demand a two stage stratified random sampling design was adopted to enable the preparation of estimates for 9 strata comprising the capital city of Ulaanbaatar, and the urban and rural sectors of the 4 geographic regions into which the country is divided.

The survey sampled 3,200 households or more than 12000 persons in each quarter that was sufficiently large for the preparation of statistically reliable estimates on key variables based on the data from the 4 quarterly rounds. The questionnaire was designed to capture labour supply and demand under both currently active and usually active concepts based on a short reference period of 1 week before the survey and a long reference period of one year considering the large proportion of the working population that was engaged in agriculture and livestock production activities.

Refer Appendix 3 of the Main Report for details of sampling design.

Sampling deviation

10 households were to be selected from every sample enumeration area in all strata in each Quarter, but due to non-response/ absence of sampled households the enumerated number was less than 10 households in a few enumeration areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed to produce data and information to achieve the objectives, scope and coverage described earlier. In designing a questionnaire, the currently active and usually active concepts were used and child labour and child activities module was integrated as the last section of the questionnaire. The questionnaire was completed by trained interviewers who visited all sampled households to take face to face interviews and collect comprehensive information on the economically active and economically inactive population. A reference period of 7 days preceding the survey was used in the currently active population section of the questionnaire to derive the activity status of the population of working age that was extended to cover children. Considerable attention was paid towards examination and identification of economic activities for an accurate assessment of the economically active population through an inclusion of activities undertaken in a predominantly agricultural subsistence economy.

Since the animal husbandry plays a dominant role in the economy of Mongolia, a long reference period or the usual status approach of measuring employment with a reference period of 12 months was used in identifying economically active status and recording the employment, unemployment and economically inactive status in the reference period of 12 months preceding the survey.

ILO/ IPEC had been interested in incorporating a child activities module in the labour force survey and offered to co-finance the cost of the survey. The child activity section was designed to measure the participation of children in economic and non-economic activities within and outside the household and illness and injuries related to work. Accordingly, in this section questions to canvass information on the participation of children aged 5-17 years in household chores, age at first employment outside the household, illnesses and injuries related to work was drafted and included in the questionnaire. Further the age cut off on questions on education and training and economic activity was also lowered to 5 years to enable the collection of comprehensive information on child activities.

Several drafts of the questionnaires were prepared and internally discussed and revised versions were prepared. The NSO finalized the questionnaire through extensive consultations with Steering Committee, various Ministries of the Government of Mongolia, representatives of trade unions and employers, and international agencies based in Ulaanbaatar. The following topics and items of information were canvassed through the survey.

A. Demographic Characteristics a. Relationship to household head b. Sex c. Date of birth and age d. School attendance, ever attended, current attendance e. Highest grade/level completed f. Literacy g. Marital status

B. Labour Force Characteristics based on short and long reference periods Current activities performed and time spent on them a. Participation in identified economic activities during the reference week. b. Total time in hours spent on identified economic activities during the reference week c. Participation in identified non-economic activities during the reference week. d. Total time spent on activities described in c above. e. Activity status during the last 7 days. f. Primary and secondary occupations under current status. g. Duration of employment in primary and secondary occupations h. Average number of hours spent on primary and secondary occupations under current status i. Industrial and occupational attachments in primary and secondary occupations j. Employment status in primary and secondary occupations k. Sector of employment of the enterprise l. Average number of hours worked in the primary and secondary occupations m. Number of paid employees in the enterprise in the primary and secondary occupations n. Earnings from primary and secondary occupations in cash and in kind o. Availability for more work p. Reasons for not working more hours q. Duration of underemployment r. Steps taken to find more work

C. Unemployment s. Availability for work t. Reasons for economically inactive status u. How long had respondent sought work v. Expected kind of work/occupation w. Expected daily wage rate/monthly remuneration x. Whether registered at Employment Registration Office y. Period of registration z. Steps taken to find work aa. Duration of unemployment

D. Usually Active Status bb. Activity status during the last 12 months cc. Primary and secondary occupations during the past 12 months dd. Industrial and occupational attachments in primary and secondary occupations during the past 12 months ee. Duration of unemployment ff. Steps taken to find work gg. Employment status in primary and secondary occupations hh. Average monthly wages and earnings during the past 12 months from primary and secondary occupations E. Past Employment Record a. Occupation, industry and sector in which the respondent last worked b. Duration of employment in last occupation c. Employment status in last occupation d. Last date worked e. Sector to which the industry where the respondent worked belonged f. Main reason for leaving the last job/occupation g. Main source of income support during the period of unemployment

F. Child Activities a. Main types of chores performed in the household. b. Current school attendance. c. Reasons for not attending school full time. d. Participation in any household economic activity. e. Age at which the child first began to work. f. Reasons for participation in economic activity. g. Whether the child had engaged in any work other than in household economic activity and reasons for engaging in such work. h. Whether the child engage in work under supervision by others. i. Whether the child is satisfied with the working conditions. j. Whether the child's occupation is stressful physically or mentally. k. Frequency with which the child had to work during evenings and night. l. Whether the child had fallen sick or was injured because of work. m. What sickness or injury from work has the child suffered. n. Main items on which the child's earnings were spent. o. The number of hours of free time per day available for recreation.

Cleaning

Labour Force Demand: NW: City of St Petersburg在2025-01达47,685.000人口，相较于2024-12的47,290.000人口有所增长。Labour Force Demand: NW: City of St Petersburg数据按月度更新，1999-05至2025-01期间平均值为55,824.000人口，共309份观测结果。该数据的历史最高值出现于2014-10，达113,880.000人口，而历史最低值则出现于2023-02，为15,069.000人口。CEIC提供的Labour Force Demand: NW: City of St Petersburg数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GB027: Labour Force Demand: by Region。

Brain evolution is hypothesized to be driven by behavioral selection on neuroarchitecture. We developed a novel metric of relative neuroanatomical investments involved in performing tasks varying in sensorimotor and processing demands across polymorphic task-specialized workers of the leafcutter ant Atta cephalotes and quantified brain size and structure to examine their correlation with our computational approximations. Investment in multi-sensory and motor integration for task performance was estimated to be greatest for media workers, whose highly diverse repertoire includes leaf-quality discrimination and leaf-harvesting tasks that likely involve demanding sensory and motor processes. Confocal imaging revealed that absolute brain volume increased with worker size and functionally specialized compartmental scaling differed among workers. The mushroom bodies, centers of sensory integration and learning and memory, and the antennal lobes, olfactory input sites, were larger in medias than in minims (gardeners) and significantly larger than in majors (“soldiers”), both of which had lower scores for involvement of olfactory processing in the performance of their characteristic tasks. Minims had a proportionally larger central complex compared to other workers. These results support the hypothesis that variation in task performance influences selection for mosaic brain structure, the independent evolution of proportions of the brain composed by different neuropils.

Methods Mature fully sclerotized workers collected from colonies Ac09, Ac16, Ac20, and Ac21 were decapitated immediately prior to brain dissection and fixation. Workers were sampled from five worker size groups identified by head width (HW): minims (0.6mm±0.1mm), medias (1.2mm±0.1mm, 1.8±0.1mm, or 2.4mm±0.1mm), and majors (3.0mm or larger). Brains (n=30) from workers sampled from Ac09, Ac20, and Ac21 were dissected in ice-cold HEPES Buffered Saline (HBS), placed in 16% zinc-formaldehyde (Ott 2008) and fixed overnight at room temperature (RT) on a shaker. Whole brains were processed to visualize the presynaptic protein synapsin. Fixed brains were washed in HBS six times, 10 minutes per wash, and fixed in Dent’s Fixative (80% MeOH, 20% DMSO) for minimally 1 hour. Brains were then washed in 100% methanol and either stored at -17°C or immediately processed. Brains were washed in 0.1M Tris buffer (pH=7.4) and blocked in PBSTN (5% neutral goat serum, 0.005% sodium azide in 0.2% PBST) at RT for 1 hour before incubation for 3 days at RT in primary antibody (1:30 SYNORF 1 in PBSTN; monoclonal antibody anti-synorf 3C11 obtained from DSHB, University of Iowa, IA, USA; 62). They were washed 6x10 minutes in 0.2% PBST and incubated in the secondary antibody (1:100 AlexaFluor 488 goat anti-mouse in PBSTN) for 4 days at RT. Brains were then washed a final time (6x10 minutes in 0.2% PBST) and dehydrated in an ethanol and PBS series (10 minutes per concentration, 30/50/70/95/100/100% ethanol in 1x PBS), then cleared with and immersed in methyl salicylate, and mounted on stainless steel glass windowed slides for imaging.

Brains were imaged with a Nikon C2 confocal microscope and images were manually annotated using Amira 6.0 software to quantify neuropil volumes (not including cell bodies). The individual who annotated all brains for the study did not have any expectation of specific outcomes and did not have knowledge of predictions generated by our model. The annotation process involved using paintbrush- or magic wand-style tools to select areas to be included in a given neuropil in a given single scan of a 3D stack. The margins of focal neuropil regions were identified visually (or automatically when using the magic wand tool) based on the presence of synapsin staining. The magic wand-style tool was used primarily to annotate the antennal lobe glomeruli. Every third frame was annotated manually (or every other frame in the case of the antennal lobes) and intervening frames were filled in using the interpolation function of Amira. Interpolated frames were also checked and edited for accuracy. Annotated slices were then used to calculate the 3D volume of each neuropil using Amira and these data were exported for analysis. We recorded the volumes of OL, AL, MB, CX, SEZ, and ROCB. We use the term ROCB for simplicity and to correspond with our ability to associate specific compartments with sensorimotor functions to describe the tissue composed of the superior neuropils, lateral horn, ventrolateral neuropils, inferior neuropils, and ventromedial neuropils, as designated in a fruit fly brain (Ito et al. 2014). For the ALs, only glomerular tissue was included (excluding aglomerular neuropil and all soma layers). For the OLs, we measured only the medulla and lobula neuropils, excluding surrounding cell bodies. Similarly, measurements of the SEZ did not include somata. We also measured and separately examined substructures of the MB: the medial calyces (MB medial calyces), lateral calyces (MB lateral calyces), and peduncle and lobes (MB peduncle). Our peduncle measurements incorporated vertical and medial lobes; these metrics are included in all discussions of the peduncle. The volumes of these components were combined to quantify total MB size (total MB) across worker size groups. For bilateral structures, one hemisphere was measured, and for compartments located along the brain midline (SEZ and CX), the whole structure was measured (Supplementary Table 1; Supplementary Table 2). When calculating total brain volume, we excluded all soma layers and used only neuropil volumes.

Graph and download economic data for Producer Price Index by Commodity: Intermediate Demand by Commodity Type: Materials and Components for Construction (WPSID612) from Jan 1973 to Jan 2025 about intermediate, materials, construction, commodities, PPI, inflation, price index, indexes, price, and USA.

Abstract

Defining the school-to-work transition is a matter worthy of careful consideration since it is the definition that determines the interpretation. Most studies define the transition as the length of time between the exit from education (either upon graduation or early exit without completion) to the first entry into stable employment. But exactly what is meant by “stable employment”? The definition of the term and the subsequent measurement of the transition vary from study to study and from country to country. Some studies take as the end point the first moment of employment in any job and others apply qualitative elements such as first stable job (measured by contract type).

The ILO SWTS was designed in a way that applies a stricter definition of “stable employment” than is typically used in the genre. By starting from the premise that a person has not “transited” until settled in a job that meets a very basic criteria of “decency”, namely a permanency that can provide the worker with a sense of security (e.g. a permanent contract), or a job that the worker feels personally satisfied with, the ILO is introducing a new quality element to the standard definition of school-to-work transition.

The main objectives of the CSWTS 2012 are to collect detailed information on the country's employment of persons aged 15-29 years old disaggregated by urban and rural areas. The survey provides information on the national youth employment that can then be used to develop, manage and evaluate youth employment policies and programmes.

The CSWTS serves a number of purposes. First, it detects the individual characteristics of young people that determine labour market disadvantage. This, in turn, is instrumental to the development of policy response to prevent the emergence of risk factors, as well as measures to remedy those factors that negatively affect the transition to decent work. Second, it identifies the features of youth labour demand, which help determine mismatches that can be addressed by policy interventions. Third, in countries where the labour market information system is not developed, it serves as an instrument to generate reliable data for policy-making and for monitoring progress towards the achievement of MDG1. In countries with a reasonably developed labour market information system, the survey helps to shed light on areas usually not captured by household-based surveys, such as youth conditions of work, wages and earnings, engagement in the informal economy, access to financial products and difficulties experienced by young people in running their business. Finally, it provides information to governments, the social partners and the donor community on the youth employment areas that require urgent attention. Other specific objectives are: - Obtain data on personal, family and household information including financial situation, health problems, highest educational level of parents, and occupation of parents. - Collect data on formal education/training, activities history and aspirations of youth/persons aged 15-29 years, including education and training, full history of economic activities, main goal in life, and working criteria.
- Collect data on young workers including personal work details of business or place of work, employment status, wage and salaried workers (employees), self-employed workers, contributing family workers, perception, time related underemployment and other inadequate employment situations, future prospects, training in current activity, and the job search.
- Collect data on unemployed youth including seeking work criteria, length of job search, availability criteria, and details of job search. - Collect data on youth in education. - Collect data on youth not in the labour force.

Geographic coverage

National coverage

Capital city (Phnom Penh)

Urban, Rural

Analysis unit

Households Individuals

Universe

Youth 15-29 years of age

Kind of data

Sample survey data [ssd]

Sampling procedure

Total sample of 160 Enumeration Areas (EAs), of which 123 would be rural and 37 urban. With 16 households were selected in each EA and this would have given an overall sample size of 2,560 households. These sample EAs were selected from the sample EAs of Cambodia Labour Force and Child Labour 2011-2012 as a sampling frame.

According to the sample selection, the SWTS in Cambodia 2012 was conducted in ten Capital/Provinces namely, Phnom Penh, Banteay Meanchey, Battambang, Kampong Cham, Kampot, Koh Kong, Prey Veng, Preah Sihanouk, Siem Reap, and Takeo, with a representative sample of 2,560 households within 160 EAs. The survey is to collect information on various characteristics of youth aged 15 to 29 years.

The sample design for the survey was a stratified two-stage probability sample where the first stage units were enumeration areas (EAs) designated as the Primary Sampling Units (PSUs) and the second stage units as the Second Sampling Units (SSUs) were the households.

1. The first stage sampling selection

In this stage, enumeration areas (EAs) were selected with Systematic Random Sampling method. For the sample urban areas in each province, all numbers of urban areas were selected from the sampling frame. For the sample rural areas in each province, the method of Systematic Random Sampling with random start was used.

1. The second stage sampling selection

A fixed sample size of 16 households in each EA would be selected by using the method of Systematic Random Sampling with a random start.

For further details please refer to the technical document on sample selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

1. Questionnaire development Draft questionnaire for the Cambodia School-to-Work Transition Survey 2012 (SWTS) was developed based on guidelines of ILO Youth Employment Programme and Work for Youth Project model SWTS questionnaires.

2. Area of the pilot test The pilot test of the Cambodia School-to-Work Transition Survey 2012 was conducted in two provinces namely Kampong Speu and Takeo. Each province consists of 5 enumeration areas (EAs) and each EA was random selected 16 sample households having members aged 15-29 years. Totally, there were 120 youth households to be interviewed.

3. Recruitment Eight staffs were recruited for the pilot test. The pilot test was divided into 2 groups for the field operations in 2 provinces. Each group consisted of one supervisor and three enumerators for conducting in one province. All of these staff will be assigned as supervisors for the main survey.

4. Training of the pilot test Before going to the field of the pilot test, 8 staffs were received a three-day training on how to carry out data collection from 29 to 31 May 2012 at NIS. The training consisted of 2 days for training, 1 day for field-test of draft questionnaire, and reviewing of field-test. Observed difficulties and problems during field-test served as additional inputs for further revisions and improvement of the questionnaires and understanding.

5. Data collection of the pilot test The data collection of the pilot test was conducted from 12 to 16 June 2012. The EA map from the population census 2008, household listing form and the draft questionnaire were used in the pilot test.

First, selecting an EA where a leader of village lives and make updating listing of all households that are now living in a selected EA on the listing sheet given. Depending on the completed household listing sheet in the selected EA, a probability systematic random sampling of 8 households was used. 8 sample households were random selected from all households having members aged 15-29 years old.

1. Lessons learnt According to the fieldwork of pilot test, some points learned were stated as follows:
2. The engagement of the village leaders in the fieldwork made it possible to enjoin the active cooperation of households for the pilot test. They played a very important role in guiding and helping our fieldwork to the target.
3. Supervisors and enumerators should close cooperation with local authority or village leaders during the fieldwork. In general, before interviewing the village leaders have to inform first to the households or another word, the households can be interviewed while the local authority or village leader gave permission.
4. Providing of gift to village leaders and households during the field interview would encourage participating in the survey and welcome to answer the questions at any time. Moreover, the respondents will provide reliable information and gain close cooperation.
5. The time-period of interview was depended upon the types of the household members who aged 15-29 years and educational background or knowledge of the respondents.
6. Get long time for waiting the target persons who are employees.
7. Have to make interview at night time when the target persons work far away from home.
8. Having car for the field work that made easily transport and save time from and to villages as well as the households to be interviewed.
9. Difficulty of recalled answer seemed not reliable.

For details on the findings of the pilot test please refer to the attached report.

Cleaning operations

Upon submission of the completed questionnaires to NIS, those questionnaires were processed at the NIS. The training of data processing was carried out for 4 days from 9-12 August 2012. After training, the editing of the completed questionnaires was done manually starting from 13 August 2012. Data entry will be carried out after finishing data editing.

In 2023, the labor force employment rate of those aged 15 years and above in Singapore was 66.2 percent. Singapore has enjoyed a relatively stable employment rate. In the face of a rapidly aging population, however, it faces higher demand for labor in the workforce. Aging population While Singapore is likely to continue with the strategies of migration and input from foreign labor supply as a means to maintain labor force growth, there is a need to expand the resident labor force at the same time by tapping older age groups as well as women. The minimum retirement age in Singapore is set at 62 years old, however 31.5 percent of residents aged 65 years old were still employed or seeking employment. A profile of older workers in Singapore showed that a large proportion of the current cohort of workers tend to be less educated, and thus many are employed in low-skilled jobs and hence receive lesser wages. It is thus a challenge to raise labor productivity and to enhance their long-term employability in an unstable economic climate. Women in the workforce The female labor force participation rate in Singapore places the city-state behind countries in the APAC region like Vietnam, Cambodia and New Zealand despite higher education attainment amongst women in recent years. The gender gap in the local labor force only emerges when women enter their 30s. In addressing this issue, ad hoc flexible work arrangements, such as unplanned time-off and telecommuting were introduced in recent years. Singapore has also implemented several enhanced maternity benefits and leave schemes for working parents.

Graph and download economic data for Producer Price Index by Commodity: Final Demand: Finished Goods (WPUFD49207) from Jan 1947 to Feb 2025 about finished, final demand, goods, commodities, PPI, inflation, price index, indexes, price, and USA.

Thailand’s gross domestic product (GDP) grew at a rate of 2.12 percent in 2019. The Thai economy

Thailand relies less on agriculture and more on employment in the service sector, which is a sign of a more advanced economy. This development is also apparent in its GDP per capita, which is one of the highest in Southeast Asia. One aspect of a developed economy is that it is more diverse, and thus less exposed to economic shocks. This statistic reflects that robustness in its optimistic view of Thai GDP growth.

Domestic factors

Thailand has posted an incredibly low unemployment rate for several years, which suggests that the workforce matches the demand for labor remarkably well. Equally important, the inflation rate tends to be low and stable, though sometimes too low. If Thailand wants to realize the positive projections in this statistic, the inflation rate of the baht should be between 2 and 3 percent, according to most economists.