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This dataset contains annual measures of training and vocational schools in the United States from 1990 through 2022. The data include counts, per capita densities, area densities, and employment figures for twelve categories of training and vocational establishments: business and secretarial schools, data processing schools, general educational services, beauty schools and barber colleges, vocational schools, dance schools, instruction schools and camps, arts and crafts schools, music and drama schools, vehicle driving schools, reading and speaking schools, and personal development schools.

The unit of analysis is either the Census Tract or ZIP Code Tabulation Area (ZCTA), with separate files standardized to 2010 Census Tract boundaries, 2020 Census Tract boundaries, 2010 ZCTA boundaries, and 2020 ZCTA boundaries. Each file covers all census tracts or ZCTAs in the fifty United States, including Alaska, Hawaii, and US island territories.

Business establishment data were drawn from the National Establishment Time Series (NETS) database, which was cleaned and geocoded to Census Bureau TIGER/Line shapefiles. Population denominators came from the American Community Survey and Decennial Census. The data cleaning protocol addressed known NETS limitations by re-geocoding addresses, standardizing SIC codes across time, collapsing duplicate records, and removing businesses located at residential addresses using Zillow's ZTRAX data.

Key variables include count (e.g., count_businesscho), per capita density per 1000 population (e.g., den_datascho), area density per square mile (e.g., aden_vocationscho), and total employment (e.g., emps_beautyscho) for each establishment category. The Census Tract 2020 dataset includes both tract_fips20 and tract_fips22 variables to accommodate Connecticut's 2022 county boundary changes.

The Taiwan Education Panel Survey (TEPS) is a national longitudinal project initiated by Academia Sinica and jointly funded by Ministry of Education, the National Science Council, and Academia Sinica. The objective of TEPS is to stimulate more basic research in the fields of education, sociology, economics, and psychology by employing large scale panel data on representative samples of students, and their parents, teachers, and school administrators. In a nutshell, TEPS has five distinguishing features: (1) Theory driven: The focus is on the skills, behavioral, values, and psychological consequences of schooling institutions and family environments of students. Factors that are found in the literature to affect students' learning outcomes are all included. Specifically speaking, an AOE model of learning outcomes, representing learning capabilities (Ability), learning opportunities (Opportunity), and the amount of effort made by the students (Effort), serves as a guiding framework for questionnaire development. Ability and effort are more on students themselves while opportunities covers family, teachers, and school environment, peers, and so forth.

(2) Student centered and multidimensional and multi-levels: Central to the project were questionnaire surveys of students. The data collection extends to cover the most influential actors in their learning environment: parents, teachers, and schools. It covered nested multiple levels of data - individual students, classes, and schools, etc.

(3) Panel surveys covering multiple programs and multiple cohorts: Students in junior high (G7 to G9), senior high (G10 to G12), vocational (G10 to G12), and junior college (G10 to G14) programs were administered for data collection. All students were followed at least twice. A portion of them were followed four times at G7, G9, G11, and G12. In light of the ongoing transformation of the Taiwanese educational system in 1990s, the project started with two cohorts of approximately 40,000 students, making it possible to employ a quasi-experimental design in future analysis.

(4) National representative samples of the students: Students under data collection were representative samples of the 1984/85 and 1988/89 birth cohorts. Weighting is provided according to the probabilistic sampling design.

(5) Public goods: Data are made available to the public as soon as the data collection and data cleaning is completed, thereby providing an important resource for both academic and policy research.