生成研究数据来源清单

• OECD UOE Data and Education at a Glance (OECD.Stat)

  • Scope and comparability: Harmonized cross-national indicators based on the joint UNESCO-OECD-Eurostat (UOE) data collection, ensuring methodological consistency across participating countries over time.
  • Coverage and unit: Country-level (with some subnational modules in select OECD databases); annual or biennial updates.
  • Relevant indicators: Education expenditure by level and source, expenditure per student, teacher numbers and pupil–teacher ratios, teacher compensation, instructional time, and staffing structure.
  • Use for this topic: Quantifies funding policies and staffing patterns across countries; can be linked to external sources providing urban–rural context.
  • Limitations: Urban–rural breakdowns are generally not included; analyses of intra-country disparities require merging with other datasets.

• UNESCO Institute for Statistics (UIS) Education Data

  • Scope and comparability: Global coverage using standardized definitions, enabling comparisons across low-, middle-, and high-income countries.
  • Coverage and unit: Primarily national aggregates; annual series with long time spans for many indicators.
  • Relevant indicators: Government education expenditure (total and by level), expenditure as a share of GDP and government outlays, numbers of teachers, pupil–teacher ratios, enrollment, and completion.
  • Use for this topic: Tracks funding trends and staffing capacity globally, facilitating comparative analysis of policy shifts across diverse systems.
  • Limitations: Urban–rural disaggregation and detailed staffing distribution within countries are limited; often requires complementary microdata for within-country disparities.

• PISA Student and School Microdata (OECD)

  • Scope and comparability: Standardized international assessment with harmonized student and school questionnaires across economies every three years.
  • Coverage and unit: Student- and school-level microdata; nationally representative samples, with school weights.
  • Relevant indicators: School location/urbanicity categories, indices of resource and staffing shortages, school governance and autonomy, sources of school funding, and student performance outcomes.
  • Use for this topic: Enables analysis of associations between resource constraints, funding sources, staffing adequacy, and urban–rural disparities in learning outcomes; supports multi-level modeling across countries.
  • Limitations: Does not provide audited finance totals; staffing measures are largely perception- or index-based rather than administrative counts.

• TALIS Teacher and Principal Microdata (OECD)

  • Scope and comparability: International survey of teachers and principals with harmonized instruments across countries and cycles.
  • Coverage and unit: Teacher- and school-level microdata; nationally representative samples.
  • Relevant indicators: Teacher workforce characteristics (experience, qualifications, workload), hiring and retention challenges, leadership and HR practices, professional development, school location categories, and perceived resource needs.
  • Use for this topic: Strong for examining staffing allocation, recruitment difficulties, and working conditions across urban–rural settings; complements PISA by focusing on workforce and management rather than student testing.
  • Limitations: Limited direct financial accounting; linking to expenditure data requires merging with national accounts or administrative data.

• World Bank BOOST Subnational Expenditure Databases

  • Scope and comparability: Standardized, program-classified (COFOG-compatible in many cases) government expenditure records at subnational levels for participating countries, enabling within-country comparisons over time.
  • Coverage and unit: Administrative unit–level finance data (e.g., provinces/districts), often with multi-year panels; country coverage varies.
  • Relevant indicators: Detailed education spending by function and economic category, enabling identification of policy shifts (e.g., grants, formula changes) and tracking of resource flows to localities.
  • Use for this topic: Supports quasi-experimental evaluations of funding policy effects and urban–rural disparities by linking expenditures to local characteristics and education outcomes or staffing from EMIS/household surveys.
  • Limitations: Country participation and years covered differ; requires integration with separate datasets to obtain teacher counts and precise urban–rural classifications.

以下为可用于研究生源结构、就业去向与专业吸引力的五类潜在数据来源（需依据法律法规与数据治理规范依法合规获取与使用）：

1. 校内招生、学籍与教务管理系统数据

• 典型字段：生源地/省份、城乡属性、性别、家庭背景（如是否家庭经济困难）、高考/入学成绩与位次、录检志愿与志愿顺序、调剂情况、专业分流去向、转专业记录、课程修读与成绩等。
• 适用维度：生源结构（画像、结构变动）、专业吸引力（志愿顺序、报录比、调剂率、转专业流向）。

2. 教育主管部门与考试招生公开数据

• 来源示例：教育部阳光高考信息公开、各省级教育考试院发布的招生计划、投档线/位次、选科要求，教育统计年鉴与各省教育年鉴等。
• 适用维度：生源结构（省域生源规模与结构对比）、专业吸引力（投档线差、报录比、区域竞争度），为校内数据提供外部基准。

3. 毕业生去向与就业质量调查数据

• 典型来源与载体：学校年度毕业生就业质量报告、毕业去向调查问卷与访谈、用人单位回访记录、校友跟踪调查。
• 典型字段：去向分类（签约就业、升学、出国、创业、灵活就业等）、行业与职业类别、单位性质与规模、就业地区、起薪与薪酬分位、中位签约时间、与所学专业相关度、满意度。
• 适用维度：就业去向（流向结构与质量）、专业吸引力（毕业生对专业和就业结果的主观评价与客观结果）。

4. 政府劳动力与就业管理数据

• 可能来源：人力资源和社会保障部门发布的高校毕业生就业与招聘统计、国家统计局劳动力与薪酬统计、公共就业服务平台数据；经授权的数据共享如社保参保与税务就业登记信息等。
• 适用维度：就业去向（实名制去向与留岗稳定性、行业与地区迁移）、就业质量（薪酬与用工形式），用于校内调查结果的外部校验与长期追踪。

5. 志愿填报与招生咨询行为数据

• 可能来源：第三方志愿填报与咨询平台的匿名化行为数据、学校官网与招生专题页/新媒体的访问与转化数据、线上线下招生咨询与宣讲活动登记、夏令营与开放日报名记录。
• 典型指标：专业页访问量、咨询转化率、意向专业偏好、地域来源热度、活动参与与转化链路。
• 适用维度：专业吸引力（报考兴趣与转化漏斗）、生源结构（潜在生源地域与学校类型分布的前置信号）。