--- title: Institutions Are a Package Deal: What a Correlation... description: Using the World Bank's Worldwide Governance Indicators (WGI), this analysis demonstrates why the rule of law cannot be examined in isolation from other... canonical: https://daily.dev/posts/institutions-are-a-package-deal-what-a-correlation-can-t-tell-you-about-the-rule-of-law-2fe1uwv55 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Institutions Are a Package Deal: What a Correlation Can’t Tell You About the Rule of Law | daily.dev og:description: Using the World Bank's Worldwide Governance Indicators (WGI), this analysis demonstrates why the rule of law cannot be examined in isolation from other... og:url: https://daily.dev/posts/institutions-are-a-package-deal-what-a-correlation-can-t-tell-you-about-the-rule-of-law-2fe1uwv55 og:image: https://api.daily.dev/og/posts/2fe1UWV55.png og:image:alt: Institutions Are a Package Deal: What a Correlation Can’t Tell You About the Rule of Law og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Institutions Are a Package Deal: What a Correlation Can’t Tell You About the Rule of Law **[R-bloggers](https://daily.dev/sources/rbloggers)** · 27 min read · 0 upvotes · 0 comments ## Summary Using the World Bank's Worldwide Governance Indicators (WGI), this analysis demonstrates why the rule of law cannot be examined in isolation from other institutional dimensions. Pairwise correlations show all six WGI dimensions (Voice & Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, Control of Corruption) are strongly associated with one another. Partial correlations and network plots reveal overlapping information across dimensions. A PCA on country-level averages finds a single principal component explains nearly 90% of variance, suggesting the six dimensions largely reflect one common signal. Year-to-year first-difference correlations are weaker but still positive and significant. The key takeaway: whether dimensions co-determine each other, share a common driver, or share source data, institutions must be analyzed as an interdependent system — a pairwise correlation between one institution and an outcome cannot cleanly attribute causation to that pillar alone. Full R code is provided throughout. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How do I calculate pairwise correlations and partial correlations for multiple variables in R and visualize them as a network? Use the rstatix package's cor_test() for pairwise correlations, then ppcor for partial correlations. Filter to unique pairs, apply Holm p-value adjustment for multiple comparisons, and visualize with a bubble plot (ggplot2) for zero-order correlations and qgraph for the partial correlation network. Edges below a chosen threshold are omitted from the network to highlight direct links. _Developers building governance or social-science dashboards in R find relevant patterns and code on daily.dev._ ### How much variance does the first principal component explain when applying PCA to the World Bank WGI governance dimensions? For country-level averages across the six WGI dimensions, a single principal component explains nearly 90% of the variance, indicating the dimensions largely capture one common signal. For year-to-year first differences the first component explains around 40%, though these two figures are not directly comparable as they operate on different units of analysis (roughly 200 country averages vs. 5,000+ country-year observations). _Analysts working with composite indices and PCA in R track methodological discussions like this on daily.dev._ ### Why are year-to-year changes in WGI governance scores harder to interpret than country-level averages? Taking first differences inflates the influence of noise and measurement error relative to the underlying signal, because institutions move slowly and differencing strips out persistent components while retaining errors intact. The WGI authors explicitly warn against over-interpreting year-to-year score movements. Pooling first differences across countries also treats observations as independent when they come from the same countries, making statistical significance near-guaranteed and effect sizes the more meaningful metric. _Researchers navigating WGI data pitfalls and governance measurement debates find ongoing discussion on daily.dev._ ## Similar posts on daily.dev - [Close Enough? 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