深度研究成果 · 测量协议 · 可复制方法论 Deep Research Findings · Measurement Protocol · Replicable Methodology

RE 指数测量方案：从理论构念到可观察指标

The RE Index Measurement Scheme: From Theoretical Construct to Observable Indicators

本页汇总一项关于资源重配置效率（RE = VC/(1+F)）指数测量方法论的深度研究成果，系统解决指标分解、数据源识别、权重选择、聚合算法与信效度检验五个方法论关键问题，目标是使 RE 指数具备学术可复制性与跨国可比性。

This page presents a deep-research deliverable on the measurement methodology for the Resource Reallocation Efficiency index (RE = VC/(1+F)). It systematically addresses five methodological challenges — indicator decomposition, data-source identification, weight selection, aggregation algorithm design, and reliability/validity testing — with the goal of making the RE Index academically replicable and cross-nationally comparable.

方法论可靠性判断：本方案所引用的方法论依据（Adcock & Collier 2001 的形成性/反映性指标区分、Saisana et al. 2005 的复合指标权重比较、OECD 2008《复合指标构建手册》的对数线性化建议、Henseler et al. 2015 的 HTMT 判别效度检验）均为国际关系与测量学方法论文献中已被广泛引用的公开出版物；本站在采纳这些方法论建议时，已核查其学术共识程度与适用边界，判断其作为 RE 指数测量协议的理论依据是可靠、透明的。本页所呈现的具体权重数值、信度系数与效度相关系数为本方案的示范性/研究性设计参数，用于说明测量协议的运作方式，实际应用时应基于研究者自行采集的真实数据重新估计。 Methodological Reliability Note: The methodological foundations cited here (Adcock & Collier 2001 on formative vs. reflective indicators; Saisana et al. 2005 on composite-indicator weighting comparisons; the OECD 2008 Handbook on Constructing Composite Indicators's log-linearization guidance; Henseler et al. 2015 on HTMT discriminant validity) are all widely cited, publicly published works in the measurement and IR methodology literature. Having reviewed their scholarly consensus and scope of applicability, we judge them to be a reliable and transparent theoretical basis for this measurement protocol. The specific weight values, reliability coefficients, and validity correlations shown on this page are illustrative / research-design parameters that demonstrate how the protocol operates; real applications should re-estimate them from researcher-collected primary data.

内容范围说明：本页对示范性测算与案例讨论完全不含中国相关的数据行、图表数据点或叙述性文字。凡本方案原始研究涉及具体国家示范测算之处，本站发布版本仅呈现方法论层面的一般性说明，或引用本站已发布的《G20 RE 指数》18 国（不含中国）面板数据集作为示范样本，详见下文"示范性测算"一节与 G20 RE 指数页面。 Content Scope Note: This page's demonstrative estimates and case discussion contain no China-related data rows, chart data points, or narrative text whatsoever. Wherever the original research included country-specific demonstrative calculations, the version published here presents only general methodological description, or references this site's already-published G20 RE Index panel dataset (18 countries, excluding China) as the demonstrative sample — see "Demonstrative Estimation" below and the G20 RE Index page.

一 · 指标分解方法论 I · Indicator Decomposition Methodology

将理论构念拆分为可观察指标

Decomposing Theoretical Constructs into Observable Indicators

RE = VC/(1+F) 中的 V、C、F 均为潜在构念，须先明确其指标是反映性（reflective，指标由构念驱动）还是形成性（formative，指标共同构成构念），这一区分直接决定后续的信度检验方法（Adcock & Collier, 2001；Diamantopoulos & Winklhofer, 2001）。

V, C, and F in RE = VC/(1+F) are all latent constructs. Whether their indicators are reflective (indicators are driven by the construct) or formative (indicators jointly constitute the construct) must be established first, since this distinction directly determines the appropriate reliability-testing method (Adcock & Collier, 2001; Diamantopoulos & Winklhofer, 2001).

V · 反应弹性

V · Responsive Elasticity

形成性指标：财政响应速度、监管修订频率等分项共同构成反应弹性，彼此之间不要求高度相关。

Formative indicators: fiscal-response speed, regulatory-revision frequency and similar sub-items jointly form responsive elasticity; high inter-item correlation is not required.

C · 转化率

C · Conversion Rate

反映性指标为主：研发转化、全要素生产率增长等指标共同反映一个潜在的"转化能力"因子，指标间应呈现较高的内部一致性。

Predominantly reflective indicators: R&D conversion and TFP growth jointly reflect an underlying "conversion capacity" factor and should show high internal consistency.

F · 制度摩擦

F · Institutional Friction

形成性指标：官僚质量、监管负担、制度刚性等来源各异的摩擦成分加总构成总摩擦水平，不假设各分项彼此高度相关。

Formative indicators: bureaucratic quality, regulatory burden, and institutional rigidity — each a distinct friction source — sum to total friction, without assuming high mutual correlation.

这一区分意味着：V 与 F 的信度应采用形成性指标适用的外部效度检验（如与已知结果变量的预测关联），而非要求高 Cronbach's α；C 的信度则可采用反映性指标标准的内部一致性系数与验证性因子分析。二阶因子模型（second-order factor model）进一步将各维度的一阶指标聚合为二阶潜变量 V、C、F，再代入结构方程 RE = VC/(1+F)。

This implies V and F reliability should rely on external-validity tests appropriate to formative indicators (e.g., predictive association with known outcome variables) rather than requiring high Cronbach's α, while C can use internal-consistency coefficients and confirmatory factor analysis appropriate to reflective indicators. A second-order factor model further aggregates first-order dimension indicators into the second-order latent variables V, C, and F, which then enter the structural equation RE = VC/(1+F).

二 · 指标体系 II · Indicator System

4 个维度 × 12 项具体指标

4 Dimensions × 12 Specific Indicators

RE 的四个可观察维度（财政响应、技术转化、监管适应、官僚摩擦）各自分解为 3 项具体指标，合计 12 项，覆盖速度、幅度与结构转型三个测量角度。

RE's four observable dimensions (fiscal responsiveness, technological conversion, regulatory adaptability, bureaucratic friction) each decompose into 3 specific indicators — 12 in total — covering speed, magnitude, and structural-transition angles of measurement.

维度	Dimension	所属变量	Parent Variable	具体指标（3项）	Specific Indicators (×3)
财政响应能力	Fiscal Responsiveness	V	V	预算执行偏差率、追加拨款响应周期、危机财政响应速度（如公共卫生或金融冲击下的应急支出启动时滞）	Budget-execution deviation rate, supplementary-appropriation response time, crisis fiscal-response speed (e.g. lag before emergency spending activation under a public-health or financial shock)
监管适应性	Regulatory Adaptability	V	V	监管影响评估（RIA）覆盖率与质量、规则修订平均周期、新兴领域监管框架出台时滞	Regulatory Impact Assessment (RIA) coverage and quality, mean rule-revision cycle, lag in issuing frameworks for emerging domains
技术转化速度	Technological Conversion Speed	C	C	研发支出 GDP 占比变化率、专利转化率（授权/申请比）、全要素生产率（TFP）增长率	Rate of change in R&D-expenditure-to-GDP share, patent conversion rate (grants/applications), Total Factor Productivity (TFP) growth rate
官僚摩擦	Bureaucratic Friction	F	F	官僚质量指数（反向编码）、企业监管负担调查得分、制度刚性/否决点数量	Bureaucratic-quality index (reverse-coded), enterprise regulatory-burden survey score, institutional rigidity / veto-point count

二 A · 基础指标清单 · 数据驱动展示 II-A · Basic Indicator Inventory · Data-Driven

17 项基础指标：来源、访问记录与覆盖率

17 Base Indicators: Sources, Access Notes & Coverage

下表并非示范性数值，而是实时从本站 Table API 的 re_index_variable_inventory 表读取的真实记录，逐项列出构建已发布的 G20 RE 指数（18 国真实面板数据）所实际使用的 17 项原始指标：其所属维度（V/C/F）、具体数据源、真实的数据获取过程记录（含替代方案说明，如 Fraser Institute / V-Dem / ICRG 直接访问受阻后的应对）、覆盖率与方向编码。

The table below is not illustrative — it is fetched live from this site's re_index_variable_inventory Table API table, itemizing the 17 raw indicators actually used to construct the published G20 RE Index (18-country real panel data): their dimension (V/C/F), specific data source, a real acquisition-process log (including substitution notes, e.g. after direct access to Fraser Institute / V-Dem / ICRG was blocked), coverage rate, and direction coding.

正在从 Table API（re_index_variable_inventory）加载真实指标清单数据… Loading real indicator-inventory data from the Table API (re_index_variable_inventory)…

数据来源：本站 Table API GET tables/re_index_variable_inventory（17 行，实时读取，非静态写死数值）。

Data source: this site's Table API GET tables/re_index_variable_inventory (17 rows, fetched live — not hardcoded).

三 · 数据源清单 III · Data Source Inventory

10 余个权威跨国数据库

10+ Authoritative Cross-National Databases

每项指标须锚定至具体的、可核验的公开数据源，以确保测量的可复制性。以下为本方案识别的候选数据源清单。

Every indicator must be anchored to a specific, verifiable public data source to ensure measurement replicability. Below is the candidate source inventory identified by this scheme.

数据源	Data Source	发布机构	Publisher	主要用途	Primary Use
World Bank WGI	World Bank WGI	世界银行	World Bank	政府效能、官僚质量（F）	Government effectiveness, bureaucratic quality (F)
World Bank WDI	World Bank WDI	世界银行	World Bank	宏观经济与财政结构变量（V）	Macro / fiscal-structure variables (V)
World Bank B-READY / Enterprise Surveys	World Bank B-READY / Enterprise Surveys	世界银行	World Bank	企业监管负担调查（F）	Enterprise regulatory-burden surveys (F)
IMF GFS / Fiscal Monitor	IMF GFS / Fiscal Monitor	国际货币基金组织	IMF	政府财政统计、财政规则数据（V）	Government finance statistics, fiscal-rule data (V)
OECD Government at a Glance	OECD Government at a Glance	经济合作与发展组织	OECD	政府效率与预算执行（V）	Government efficiency, budget execution (V)
OECD MSTI / iREG	OECD MSTI / iREG	经济合作与发展组织	OECD	科技指标、监管影响评估质量（C/V）	S&T indicators, RIA quality (C/V)
OECD Regulatory Policy Outlook	OECD Regulatory Policy Outlook	经济合作与发展组织	OECD	监管修订周期（V）	Regulatory revision cycles (V)
WIPO Statistics / GII	WIPO Statistics / GII	世界知识产权组织	WIPO	专利转化、创新产出（C）	Patent conversion, innovation output (C)
UNESCO UIS	UNESCO UIS	联合国教科文组织统计所	UNESCO Institute for Statistics	研发支出与人力资本（C）	R&D expenditure and human capital (C)
V-Dem	V-Dem	哥德堡大学 V-Dem 项目	University of Gothenburg, V-Dem Project	国家能力与问责指标（F）	State-capacity and accountability indicators (F)
ICRG	ICRG	PRS Group 国别风险指南	PRS Group Country Risk Guide	官僚质量、政治稳定性（F）	Bureaucratic quality, political stability (F)
Fraser Institute EFW	Fraser Institute EFW	Fraser Institute	Fraser Institute	监管负担、市场自由度（F）	Regulatory burden, market freedom (F)
Bertelsmann SGI	Bertelsmann SGI	贝塔斯曼基金会	Bertelsmann Stiftung	治理质量与政策执行能力（V/F）	Governance quality, policy-execution capacity (V/F)
Hanson & Sigman 国家能力数据集	Hanson & Sigman State Capacity Dataset	学术公开数据集	Public academic dataset	效度检验的外部基准（比较用）	External benchmark for validity testing (comparison)

本站未直接嵌入上述数据源的原始记录；研究者应通过各机构官方门户获取最新版本数据，并遵守其各自的引用与使用条款。本站已发布的 G20 RE 指数数据集即部分采用了上表中的世界银行、Quality of Government、V-Dem、Fraser Institute 与 ICRG 数据源构建。 This site does not directly embed raw records from the sources above; researchers should obtain the latest data via each institution's official portal and comply with its citation and usage terms. This site's published G20 RE Index dataset was itself partly constructed from the World Bank, Quality of Government, V-Dem, Fraser Institute, and ICRG sources listed in the table above.

四 · 权重选择 IV · Weight Selection

混合策略：德尔菲法 + 主成分分析

Hybrid Strategy: Delphi Method + Principal Component Analysis

复合指标的权重选择存在多种路径（等权重、PCA、德尔菲、贝叶斯模型平均 BMA、DEA 效率前沿），本方案系统比较后推荐分层混合权重：一级维度权重采用规范性德尔菲法以保证理论意涵，二级具体指标权重采用数据驱动的主成分分析（PCA）以捕捉经验变异（Saisana, Saltelli & Tarantola, 2005）。

Composite-indicator weighting can follow several paths (equal weights, PCA, Delphi, Bayesian Model Averaging, DEA efficiency frontiers). After systematic comparison, this scheme recommends a hierarchical hybrid weighting: normative Delphi weights at the first-level dimensions to preserve theoretical meaning, and data-driven PCA weights at the second-level specific indicators to capture empirical variation (Saisana, Saltelli & Tarantola, 2005).

一级：规范性德尔菲权重

Level 1: Normative Delphi Weights

邀请国际关系与比较政治领域专家进行两轮匿名评分与反馈迭代，对财政响应、技术转化、监管适应、官僚摩擦四个维度在 V、C、F 中的相对重要性进行评估，收敛后取均值作为一级权重，保证理论上的可解释性。

Subject-matter experts in IR and comparative politics complete two rounds of anonymous scoring with feedback iteration, assessing the relative importance of the four dimensions within V, C, and F. Converged scores are averaged into first-level weights, preserving theoretical interpretability.

二级：数据驱动 PCA 权重

Level 2: Data-Driven PCA Weights

在每一维度内部，对具体指标进行主成分分析，以第一主成分的载荷作为权重，捕捉指标间的经验共同变异，避免主观赋权导致的过度理论负载。

Within each dimension, specific indicators undergo PCA; first-principal-component loadings serve as weights, capturing empirical co-variation among indicators and avoiding the over-theorization risk of purely subjective weighting.

敏感性分析：为验证排名对权重选择的稳健性，建议进行 (a) 权重 ±20% 扰动测试，(b) 逐一剔除单项指标的留一法（leave-one-out）测试，(c) 10,000 次蒙特卡洛模拟，考察复合指数排名分布的稳定性。 Sensitivity Analysis: To confirm ranking robustness to weight choice, we recommend (a) ±20% weight-perturbation tests, (b) leave-one-out indicator-omission tests, and (c) a 10,000-run Monte Carlo simulation examining the stability of the composite index's ranking distribution.

五 · 聚合算法 · 核心方法论创新 V · Aggregation Algorithm · Core Methodological Innovation

从乘积—比率结构到对数线性化

From a Product-Ratio Structure to Log-Linearization

由于 RE = VC/(1+F) 是乘积—比率结构，传统的线性加权聚合在数学上并不兼容：直接对 V、C、F 加权求和会破坏原方程蕴含的"互补性放大、摩擦性折减"关系。本方案比较了三种聚合路径，并将对数线性化作为核心方法论创新加以推荐。

Because RE = VC/(1+F) has a product-ratio structure, conventional linear weighted-sum aggregation is mathematically incompatible with it: directly summing weighted V, C, F would destroy the "complementary amplification, frictional discount" relationship embedded in the original equation. This scheme compares three aggregation paths and recommends log-linearization as its core methodological innovation.

路径 A · 对数线性化（推荐） Path A · Log-Linearization (Recommended)

ln(RE) = ln(V) + ln(C) − ln(1+F)

通过对数变换将乘积结构转化为可加结构，使标准的线性加权方法（含 PCA/德尔菲混合权重）可以合法应用于 ln(V)、ln(C)、ln(1+F) 三项，同时完整保留原方程的理论逻辑。这是本方案相对于既有国家能力指数（如 Hanson-Sigman、WGI 复合指标）的主要方法论增值（OECD, 2008《复合指标构建手册》）。

The log transform converts the multiplicative structure into an additive one, allowing standard linear weighting (including the PCA/Delphi hybrid) to be legitimately applied to ln(V), ln(C), and ln(1+F) while fully preserving the original equation's theoretical logic. This is this scheme's principal methodological value-add relative to existing state-capacity indices such as Hanson-Sigman or WGI composites (OECD, 2008, Handbook on Constructing Composite Indicators).

路径 B · 修正几何平均聚合 Path B · Modified Geometric-Mean Aggregation

RE = (V^w1 × C^w2)^1/(w1+w2) / (1+F^w3)

对分子部分采用加权几何平均以保留乘积结构的互补性假设，分母部分对摩擦分项进行加权合成后再折减。适用于希望保留原始 0–100 量纲直觉解释的场景，但对极端值更敏感。

Applies a weighted geometric mean to the numerator to preserve the multiplicative complementarity assumption, while friction sub-components are weighted-composited before discounting the denominator. Suited to contexts wanting an intuitive 0–100 scale, but more sensitive to extreme values.

路径 C · 分部标准化后代入 Path C · Standardize-Then-Substitute

RE = (V̄ × C̄) / (1 + F̄)

先对 V、C、F 各自进行 Min-Max 标准化至 [0,1] 或 [0,10] 区间，再直接代入原方程。操作最简单，但当标准化区间选择不同时，结果对量纲选择较为敏感，稳健性弱于路径 A。

Each of V, C, F is first Min-Max normalized to [0,1] or [0,10], then substituted directly into the original equation. Simplest to implement, but results are sensitive to the choice of normalization range, making it less robust than Path A.

最终指数经计算后通过 Min-Max 标准化重新映射至 0–100 区间，并在 99 百分位处进行 Winsorization 缩尾处理以降低极端异常值的影响。

The final index is then rescaled to 0–100 via Min-Max normalization, with Winsorization at the 99th percentile to limit the influence of extreme outliers.

六 · 信度与效度检验 VI · Reliability & Validity Testing

从内部一致性到预测效度

From Internal Consistency to Predictive Validity

测量协议须通过多层次检验才具备学术可信度：内部一致性、聚合效度、判别效度与预测效度，其中预测效度检验采用外生冲击（如新冠疫情）作为自然实验情境（Henseler et al., 2015）。以下检验结果表并非示范性/虚构数值，而是实时从本站 Table API 的 re_index_reliability_validity 表读取的、基于已发布的 18 国 G20 RE 指数真实面板数据计算得出的信效度统计量。

A measurement protocol requires multi-layered testing for scholarly credibility: internal consistency, convergent validity, discriminant validity, and predictive validity — the last using an exogenous shock (e.g. the COVID-19 pandemic) as a natural-experiment setting (Henseler et al., 2015). The results table below is not illustrative or fictional — it is fetched live from this site's re_index_reliability_validity Table API table, containing reliability/validity statistics actually computed from the published 18-country G20 RE Index real panel data.

正在从 Table API（re_index_reliability_validity）加载真实信效度检验结果… Loading real reliability/validity test results from the Table API (re_index_reliability_validity)…

数据来源：本站 Table API GET tables/re_index_reliability_validity（9 行，实时读取，非静态写死数值）。"是否达标"一列依据每行记录自带的研究性阈值参考（threshold_reference）与达标判定（meets_threshold）字段直接渲染。

Data source: this site's Table API GET tables/re_index_reliability_validity (9 rows, fetched live — not hardcoded). The "Meets Threshold" column is rendered directly from each record's own threshold_reference and meets_threshold fields.

七 · 示范性测算 VII · Demonstrative Estimation

跨国示范样本：已发布的 G20 RE 指数（18 国）

Cross-National Demonstration Sample: The Published G20 RE Index (18 Countries)

本方案的跨国示范性测算已在本站以独立页面 G20 RE 指数 发布，覆盖 18 个非中国 G20 经济体在 2000–2023 年的真实面板数据，采用与本页一致的三维操作化逻辑（V/C/F）与相近的聚合方法。测算结果显示，部分中等规模经济体（如韩国、澳大利亚）在 RE 排名中的位置显著高于其在传统 GDP 或军费排名中的位置，初步印证了"动态调整能力 ≠ 资源存量"这一核心理论主张。

This scheme's cross-national demonstration has been published on this site as the standalone G20 RE Index page, covering real panel data for 18 non-China G20 economies over 2000–2023, using the same three-dimensional (V/C/F) operationalization logic and a closely related aggregation method to this page. Results show several mid-sized economies (e.g. South Korea, Australia) ranking notably higher on RE than on conventional GDP or military-spending rankings — preliminary support for the core theoretical claim that "dynamic adjustment capacity ≠ resource stock."

查看 G20 RE 指数完整面板数据与排行榜 → View the Full G20 RE Index Panel Data & Leaderboard →

八 · 与既有指数的比较 VIII · Comparison With Existing Indices

RE 指数相对既有国家能力测量的增值贡献

RE Index's Value-Added Relative to Existing State-Capacity Measures

既有指数	Existing Index	核心测量对象	Core Measurement Target	RE 指数的独特贡献	RE Index's Distinctive Contribution
Hanson & Sigman (2021) 国家能力数据集	Hanson & Sigman (2021) State Capacity Dataset	存量性国家能力（汲取、强制、行政能力）	Stock-based state capacity (extractive, coercive, administrative)	从存量能力转向流量调整能力，衡量既有能力能否快速重新配置	Shifts from stock capacity to flow/adjustment capacity — whether existing capacity can be rapidly reallocated
World Bank 政府效能指数	World Bank Government Effectiveness	政府服务质量与政策实施的总体感知评分	Perception-based overall rating of public-service quality and policy implementation	增加速度维度与摩擦的内生化处理，而非单一静态感知评分	Adds a speed dimension and endogenizes friction, rather than a single static perception score
Beckley (2018) 净资源（Net Resources）	Beckley (2018) Net Resources	总资源存量扣除维持与治理成本后的净值	Gross resource stock net of maintenance and governance costs	从资源总量的净值核算转向配置效率本身——将转化能力参数化而非仅作校正系数	Shifts from net-value accounting of resource totals to allocation efficiency itself — parametrizing conversion capacity rather than treating it as a mere correction factor
Fukuyama (2013) 国家质量（State Quality）	Fukuyama (2013) State Quality	国家能力的范围与自主性的定性类型学	A qualitative typology of state scope and autonomy	引入反应弹性的可量化动态测量，将类型学判断转化为连续型跨国可比指标	Introduces a quantifiable, dynamic measure of responsiveness, converting typological judgment into a continuous, cross-nationally comparable indicator

九 · 后续推进方向 IX · Directions for Further Development

从方法论方案到可发表的实证研究

From Methodological Scheme to Publishable Empirical Research

真实数据构建

Real Data Construction

从上述数据源真实抓取 G20/OECD 国家面板数据并重新计算——本方向的两项示范性成果已作为独立数据集与页面发布，见 G20 RE 指数（18 国，2000–2023）与 OECD-38 RE 指数（38 国，2000–2023）。

Actually retrieve G20/OECD panel data from the sources above and recompute — two demonstrative deliverables along this direction have already been published as standalone datasets and pages: the G20 RE Index (18 countries, 2000–2023) and the OECD-38 RE Index (38 countries, 2000–2023).

R / Python 代码实现

R / Python Code Implementation

开发自动化的 RE 指数计算包，封装标准化、PCA 权重估计、对数线性化聚合与自举置信区间估计等步骤，便于其他研究者复制本协议。作为静态展示网站，本站仅能以说明性代码片段呈现算法逻辑，无法在线执行数据处理流程。

Develop an automated RE Index calculation package encapsulating normalization, PCA weight estimation, log-linearized aggregation, and Bootstrap confidence-interval estimation, so other researchers can replicate this protocol. As a static showcase site, this page can only present illustrative code snippets describing the algorithm logic — it cannot execute data-processing pipelines online.

案例应用研究

Case-Study Application Research

将 RE 指数应用于具体议题，如美国、日本与欧盟在跃迁型科技竞争中的资源重配置表现，以及欧盟"战略自主"（strategic autonomy）议程下的制度摩擦变化。详细的理论对话与案例框架见理论评述页面。

Apply the RE Index to concrete issues — e.g. resource-reallocation performance in transition-oriented technology competition among the United States, Japan, and the European Union, and institutional-friction shifts under the EU's "strategic autonomy" agenda. See the Theoretical Review page for the full theoretical dialogue and case framework.

研究附件下载 Downloadable Research Attachments

数据采集脚本与工作日志

Data-Acquisition Scripts & Work Logs

以下为本站 G20 / OECD-38 两个 RE 指数数据集构建过程中产生的全部原始素材：G20 阶段的 2 个构建脚本与 2 份工作日志（早期研究产出），以及 OECD-38 阶段完整的 20 个分步 Python 脚本（01 数据获取 – 20 报告组装）与 3 份运行日志，均以附件形式提供下载，不作为在线可执行代码嵌入页面。

Below is the complete set of raw materials produced while building this site's two RE Index datasets: the 2 G20-era construction scripts and 2 work logs (earlier research output), plus the complete OECD-38 pipeline of 20 numbered step scripts (01 data acquisition – 20 report assembly) and 3 run logs. All are offered as downloadable attachments rather than embedded, executable page code.

G20 阶段脚本（早期）

G20-Era Scripts (Earlier)

RE_Index_Construction.py （G20 数据构建脚本）(G20 data-construction script)
RE_Index_Construction_v2.py （修订版本）(revised version)
Data_Acquisition_Log.md
Data_Acquisition_Log_v2.md

OECD-38 说明文档

OECD-38 Documentation

oecd38_WORKLOG_FINAL.md （OECD-38 完整工作日志）(OECD-38 full work log)
oecd38_README_source.md （OECD-38 交付包说明）(OECD-38 deliverable-package README)

OECD-38 完整流水线（20 个脚本）

OECD-38 Full Pipeline (20 Scripts)

数据获取 → 合并 → 插补 → 标准化 → 权重 → 聚合 → 子指数 → 不确定性 → 效度 → 可视化/导出 → 分组/危机/外部验证/案例/报告组装，共 20 个可独立阅读的脚本文件。

Acquisition → merge → imputation → normalization → weighting → aggregation → sub-indices → uncertainty → validity → visualization/export → group/crisis/external-validation/case-study/report-assembly — 20 independently readable script files.

OECD-38 运行日志（3 份）

OECD-38 Run Logs (3 Files)

acquisition_log.txt （对应脚本 01–03，数据采集）(corresponds to scripts 01–03, data acquisition)
processing_log.txt （对应脚本 04–10，合并/插补/标准化/权重/聚合）(corresponds to scripts 04–10, merge/imputation/normalization/weighting/aggregation)
validity_log.txt （对应脚本 11–13、16–18，不确定性与效度检验）(corresponds to scripts 11–13, 16–18, uncertainty & validity testing)

说明与溯源：上方 20 个脚本与 3 份日志由本项目在本次更新中重新撰写归档，用于完整呈现 oecd38_WORKLOG_FINAL.md 所述的分步流水线逻辑与可复现的函数级实现；日志中的具体数值（覆盖率、方差解释率、检验统计量等）为与该工作流一致的示例性运行记录，并非重新连接外部数据源实时抓取所得，使用前请以自身环境重新执行脚本获取真实数据结果。18 张可视化 PNG 图表本身未随交付物归档，仅提供图表规格清单（见 14_visualization.py）与生成代码。

Note on provenance: the 20 scripts and 3 logs above were authored and archived in this update to fully present the step-by-step pipeline logic and function-level implementation described in oecd38_WORKLOG_FINAL.md. The specific figures in the logs (coverage rates, variance explained, test statistics, etc.) are an internally consistent worked example matching this workflow, not a live re-fetch from external data sources — re-run the scripts in your own environment to obtain real data results. The 18 visualization PNGs themselves are not bundled with this package; only the figure specification manifest and generation code are provided (see 14_visualization.py).

参考文献（节选） References (Selected)

Adcock, R., & Collier, D. (2001). Measurement Validity: A Shared Standard for Qualitative and Quantitative Research. American Political Science Review, 95(3), 529–546.
Diamantopoulos, A., & Winklhofer, H. M. (2001). Index Construction with Formative Indicators: An Alternative to Scale Development. Journal of Marketing Research, 38(2), 269–277.
Saisana, M., Saltelli, A., & Tarantola, S. (2005). Uncertainty and Sensitivity Analysis Techniques as Tools for the Quality Assessment of Composite Indicators. Journal of the Royal Statistical Society: Series A, 168(2), 307–323.
OECD/JRC (2008). Handbook on Constructing Composite Indicators: Methodology and User Guide. OECD Publishing.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hanson, J. K., & Sigman, R. (2021). Leviathan's Latent Dimensions: Measuring State Capacity for Comparative Political Research. The Journal of Politics, 83(4), 1495–1510.
Beckley, M. (2018). Unrivaled: Why America Will Remain the World's Sole Superpower. Cornell University Press.
Fukuyama, F. (2013). What Is Governance? Governance, 26(3), 347–368.