Kristian Vepsäläinen – Maailma on jakauma https://kristianvepsalainen.com/whitepapers.html Datatieteen konsultointi pk-yrityksille. Avoimen datan analytiikka, bayeslainen mallinnus ja fractional Head of Data -palvelut. quarto-1.9.38 Fri, 26 Jun 2026 07:46:13 GMT What 56,000 Statutes Reveal About How a Country Legislates Kristian Vepsäläinen https://kristianvepsalainen.com/whitepapers/finlex-whitepaper.html

Download: PDF version · Source on GitHub

The question

Every lawyer knows what the law says. Almost nobody knows how the law behaves as a system — how fast it changes, which parts change most, when in the year new rules appear, or whether the pace of new legislation is rising or falling. These are empirical questions, and the data to answer them is public.

This whitepaper demonstrates a reproducible pipeline that turns Finland’s open legislative database into quantitative answers. It is written for two audiences: data professionals who want to see the method, and decision-makers who want to know what kind of insight open legal data can deliver. The same approach generalises to any jurisdiction that publishes machine-readable legislation.

All code is shown. All figures are computed from the data, not hard-coded. The entire analysis rests on 54,527 statutes retrieved from a free, public API.

The data source

Finland publishes its legislation through an open REST API following the Akoma Ntoso standard — a UN-backed XML schema for legal documents. No registration or API key is required; the only requirement is a User-Agent header.

Each statute carries a category — new statute, amending statute, or repealing statute — which is the key to most of the analysis. A statute is not a static text but an event: a law is enacted once and then amended, sometimes dozens of times, over its lifetime.

Code 
library(httr)
library(jsonlite)

BASE <- "https://opendata.finlex.fi/finlex/avoindata/v1/akn/fi/act/statute/list"
UA   <- "FinlexAnalysis/1.0 (your.email@example.com)"

# One page of results for a given year and category
fetch_page <- function(year, category, page) {
  resp <- GET(BASE,
    query = list(format = "json", page = page, limit = 10,
                 sortBy = "dateIssued",
                 startYear = year, endYear = year,
                 categoryStatute = category),
    add_headers(`User-Agent` = UA, Accept = "application/json"),
    timeout(30))
  stop_for_status(resp)
  fromJSON(rawToChar(resp$content))
}

The API caps page size at ten results and returns both Finnish and Swedish language versions, so the full retrieval loops over years and categories, keeps only the fin@ URIs, and paginates until a page comes back empty. Retrieving the complete dataset of 54,527 statutes plus their amendment targets is an overnight job — roughly 90,000 HTTP requests in total. The retrieval code is built to resume from an interruption and to back off on HTTP 429 responses, which matters when a job runs for hours.

Finding 1: EU membership left a measurable mark

The clearest structural signal in Finnish legislation is the ratio of amendments to new statutes — how much existing law is being modified relative to how much new law is created.

Code 
ratio_data <- df_a |>
  count(vuosi, statute_type) |>
  pivot_wider(names_from = statute_type, values_from = n, values_fill = 0) |>
  mutate(ratio = Amendment / NewStatute) |>
  arrange(vuosi)

A Bayesian changepoint analysis locates the moment this ratio shifted. The method models the series as having one structural break at an unknown point, places a uniform prior over all possible break years, and marginalises to get a posterior distribution over the changepoint location — not a point guess, but a full description of the uncertainty.

Code 
y <- ratio_data$ratio
n <- length(y)
yrs <- ratio_data$vuosi

log_post <- rep(-Inf, n)
for (tau in 3:(n - 3)) {
  y1 <- y[1:tau]; y2 <- y[(tau + 1):n]
  log_post[tau] <- -length(y1)/2 * log(var(y1) + 1e-9) -
                    length(y2)/2 * log(var(y2) + 1e-9)
}
ok <- is.finite(log_post)
log_post[ok] <- log_post[ok] - max(log_post[ok])
post <- exp(log_post); post <- post / sum(post)
map_year <- yrs[which.max(post)]
Code 
ggplot(ratio_data, aes(vuosi, ratio)) +
  geom_line(color = "#1d3557", linewidth = 0.9) +
  geom_point(color = "#1d3557", size = 1.6) +
  geom_vline(xintercept = map_year, linetype = "dashed",
             color = "#e63946", linewidth = 0.9) +
  annotate("text", x = map_year + 0.5, y = max(ratio_data$ratio) * 0.95,
           label = paste0("Changepoint: ", map_year),
           hjust = 0, color = "#e63946") +
  labs(x = "Year", y = "Amendments / new statutes") +
  theme_minimal(base_size = 13)

Amendment-to-new-statute ratio per year. The dashed line marks the Bayesian changepoint estimate.

The estimate lands on 2008 — the year Finland joined the European Union. The obligation to implement EU directives shifted legislative work durably toward amending existing law. This is not a surprise to a lawyer, but it has never been shown with this precision: the break is located to within a single year, directly from the data.

Finding 2: Change pressure is extremely unequal

How many times is each law amended? Every amending statute contains a machine-readable reference (affectedDocument) to the law it changes, so the answer is a matter of counting.

Code 
change_freq <- aff |>
  filter(lahde_statute_type == "Amendment") |>
  count(kohde_href, name = "amendments")

top_law      <- max(change_freq$amendments)
once_only    <- round(mean(change_freq$amendments == 1) * 100)
sorted       <- sort(change_freq$amendments, decreasing = TRUE)
top10_share  <- round(sum(sorted[1:ceiling(length(sorted) * 0.1)]) / sum(sorted) * 100)

The distribution is severely long-tailed. 45% of laws are amended only once, while the top 10% of most-amended laws absorb 51% of all amendments. The single most-amended statute is the Criminal Code of 1889, revised more than 330 times. A handful of central laws — chiefly tax and social-security legislation — carry most of the change burden. In practice, following legislation means following a small set of volatile laws.

Finding 3: Laws change fast, and December dominates

Two more questions fall out of the same data. First, how long does a law survive before its first amendment? Restricting to laws enacted from 1990 onward (so the full history is observed), the median time to first amendment is just two years, and nearly half of all laws are amended within two years of enactment. A law rarely stabilises before it is revised.

Second, when in the year are statutes issued?

Code 
month_data <- rik_a |>
  filter(!is.na(kuukausi)) |>
  count(kuukausi)
dec_share <- round(month_data$n[month_data$kuukausi == 12] / sum(month_data$n) * 100)
Code 
month_names <- c("Jan","Feb","Mar","Apr","May","Jun",
                 "Jul","Aug","Sep","Oct","Nov","Dec")
month_data |>
  mutate(m = factor(month_names[kuukausi], levels = month_names)) |>
  ggplot(aes(m, n, fill = kuukausi == 12)) +
  geom_col(show.legend = FALSE) +
  scale_fill_manual(values = c(`FALSE` = "#457b9d", `TRUE` = "#e63946")) +
  labs(x = NULL, y = "Number of statutes") +
  theme_minimal(base_size = 13)

Statutes issued by month. December alone accounts for over a quarter of the year’s legislation.

December accounts for 28% of all statutes — a consequence of new rules timed to take effect at the turn of the year. The pattern is structural and holds across statute types, not just budget laws.

Finding 4: Knowing what you cannot predict

A forecast is only worth making if the quantity has predictable structure. The annual total number of statutes does not: its year-to-year autocorrelation is essentially zero, so it is noise around a stable mean. Recognising this is itself a result — it prevents a misleading forecast.

The number of new statutes is different. It has strong autocorrelation and a clear downward trend. A Bayesian linear trend model on the log scale produces a genuinely informative forecast: new legislation is declining at roughly 2–3% per year, and the projection carries this forward with honest, and narrow, uncertainty bands. The substantive reading reinforces the whole analysis — legislative work is shifting from creating new law toward amending what already exists.

Why this matters

None of these findings required privileged access or proprietary data. They came from a free public API, a few hundred lines of R, and statistical methods that quantify uncertainty rather than hide it. The same pipeline applies to any domain-specific question — the change rate of a single field of law, the volatility of statutes referenced in a contract, or a comparison of legislative turnover across sectors.

The barrier to this kind of analysis is technical, not legal. That is precisely where the collaboration between domain expertise and data science creates value that neither produces alone.

Reproducing this analysis

The full retrieval and analysis code is available on request. The pipeline has four stages:

  1. Retrieve the statute list — loop over years and categories against the Finlex REST API, keep Finnish-language URIs, paginate to exhaustion.
  2. Retrieve amendment targets — for each amending and repealing statute, fetch its Akoma Ntoso XML and extract the affectedDocument reference. This is the long step (~35,000 requests).
  3. Enrich metadata — fetch dateIssued, title, and section count per statute to derive month, statute type, and length.
  4. Analyse — changepoint detection, change-frequency distribution, timing, and the Bayesian trend forecast.

Every stage caches to disk (.fst and .rds) so the expensive retrieval runs once. The analysis layer reads only local files, which keeps it fast and reproducible — and crucially, lets it run in a CI environment such as GitHub Actions without hitting the API.

Kristian Vepsäläinen is an independent data scientist (M.Sc. mathematics, M.Eng. cyber security) specialising in open-data analysis, Bayesian methods, and turning public data into decision-relevant insight. kristianvepsalainen.com

]]> https://kristianvepsalainen.com/whitepapers/finlex-whitepaper.html Fri, 26 Jun 2026 07:46:13 GMT EU Legislation as a Distribution Kristian Vepsäläinen https://kristianvepsalainen.com/whitepapers/eu_legislation_whitepaper.html

Download: PDF version · Source on GitHub

Introduction

Point estimates dominate public discussion of EU regulation. A directive “enters into force.” A regulation is “amended.” A member state “fails to implement.” Each event is reported as a discrete fact, stripped of distributional context.

This framing is analytically impoverished. Regulatory output is a stochastic process with time-varying intensity. Implementation delay is a random variable with a fat right tail. Act survival time is a censored duration. Legal citations form a network with non-trivial topology.

None of these phenomena are adequately described by a single number. All of them reveal structure when examined as distributions.

This whitepaper presents five analyses of EU legislation using data from the EUR-Lex Cellar database, accessed through the eurlex R package [@ovadek2021]. The analyses span four methodological families: time series decomposition, distributional modelling, survival analysis, and network analysis. Together they constitute a statistical portrait of EU law that is not available in any existing regulatory report.

The intended audience is policy analysts, legal practitioners, and compliance professionals who need a quantitative understanding of EU regulatory dynamics — not as background knowledge, but as an operational input to decision-making.

What data is available

The eurlex package provides programmatic access to nine document types in EUR-Lex:

Type Description Analytical potential
regulation Directly applicable law Volume analysis, structural breaks
directive Requires national transposition Implementation delay, survival
decision Commission/Council decisions Decision-making rhythm
recommendation Non-binding guidance Soft vs. hard law trends
intagr International agreements External relations activity
caselaw ECJ and General Court Caseload, legal network centrality
proposal Legislative proposals Time-to-adoption, mortality rate
national_impl National transposition measures Implementation delay by country
manual Other documents (SWD, impact assessments) Preparatory work volume

This whitepaper uses regulation, directive, decision, recommendation, and national_impl. The remaining types — particularly caselaw and proposal — are reserved for subsequent analyses.

Data

Show code 
# All data files are pre-fetched locally — CI renders without network access.
# See blog series parts 1-5 for full data acquisition code.

data_path  <- here("data/eu/eu_saadanto_raw.rds")
dir_path   <- here("data/eu/eu_dir_raw.rds")
nimpl_path <- here("data/eu/eu_nimpl_raw.rds")

stopifnot(
  "Run blog part 1 to create eu_saadanto_raw.rds" = file.exists(data_path),
  "Run blog part 2 to create eu_dir_raw.rds"      = file.exists(dir_path),
  "Run blog part 2 to create eu_nimpl_raw.rds"    = file.exists(nimpl_path)
)

raw      <- readRDS(data_path)
raw_dir  <- readRDS(dir_path)
raw_nimpl <- readRDS(nimpl_path)

# --- Base dataset ---
df <- raw |>
  filter(!is.na(date)) |>
  mutate(
    date         = as.Date(date),
    vuosi        = year(date),
    saadostyyppi = case_when(
      resource_type == "regulation"     ~ "Regulation",
      resource_type == "directive"      ~ "Directive",
      resource_type == "decision"       ~ "Decision",
      resource_type == "recommendation" ~ "Recommendation"
    )
  ) |>
  filter(vuosi >= 1960, vuosi <= 2023)

vuosi_yht <- df |>
  count(vuosi) |>
  complete(vuosi = 1960:2023, fill = list(n = 0))

# --- Directive + implementation join ---
dir_df <- raw_dir |>
  filter(!is.na(celex), !is.na(date)) |>
  mutate(
    date_adopted  = as.Date(date),
    date_transpos = as.Date(datetranspos),
    vuosi_hyvaks  = year(date_adopted),
    transpos_kk   = as.numeric(date_transpos - date_adopted) / 30.44
  ) |>
  filter(vuosi_hyvaks >= 1975, vuosi_hyvaks <= 2023,
         transpos_kk > 0, transpos_kk < 240)

nimpl_df <- raw_nimpl |>
  filter(!is.na(celex), !is.na(date)) |>
  mutate(
    date_impl     = as.Date(date),
    vuosi_impl    = year(date_impl),
    maakoodi      = str_extract(celex, "[A-Z]{2,3}(?=_)"),
    dir_celex_raw = str_extract(celex, "(?<=^7)[0-9]{4}[A-Z][0-9]+"),
    dir_celex     = paste0("3", dir_celex_raw)
  ) |>
  filter(vuosi_impl >= 1986, vuosi_impl <= 2024,
         !is.na(maakoodi), !is.na(dir_celex_raw))

viive_df <- nimpl_df |>
  inner_join(
    dir_df |> select(celex, date_adopted, date_transpos, vuosi_hyvaks),
    by = c("dir_celex" = "celex")
  ) |>
  mutate(
    viive_kk = as.numeric(date_impl - date_transpos) / 30.44,
    myohassa = viive_kk > 0
  ) |>
  filter(!is.na(viive_kk), abs(viive_kk) < 600)

cat(
  "Legislative acts:", nrow(df), "\n",
  "Directives with transposition deadline:", nrow(dir_df), "\n",
  "Implementation records:", nrow(viive_df), "\n",
  "Countries covered:", n_distinct(viive_df$maakoodi), "\n"
)
Legislative acts: 208164 
 Directives with transposition deadline: 3753 
 Implementation records: 269712 
 Countries covered: 28

The dataset covers 208,164 legislative acts adopted between 1960 and 2023, 3753 directives with known transposition deadlines, and 269,712 directive–country implementation pairs across 28 member states.

Finding 1: Regulatory output is a non-stationary process

Annual legislative volume is neither constant nor smoothly trending. It is a step function punctuated by structural breaks tied to identifiable institutional events.

Show code 
set.seed(42)
bcp_fit <- bcp(as.numeric(vuosi_yht$n), p0 = 0.2, burnin = 500, mcmc = 5000)

bcp_df <- tibble(
  vuosi     = vuosi_yht$vuosi,
  n         = vuosi_yht$n,
  post_mean = bcp_fit$posterior.mean[, 1],
  post_prob = bcp_fit$posterior.prob
)

events <- tribble(
  ~vuosi, ~label,
  1973,   "First\nenlargement",
  1986,   "Single\nEuropean\nAct",
  1993,   "Internal\nmarket",
  2004,   "Eastern\nenlargement",
  2009,   "Lisbon"
)

p1 <- ggplot(bcp_df, aes(vuosi, n)) +
  geom_vline(data = events, aes(xintercept = vuosi),
             linetype = "dotted", color = "grey70", linewidth = 0.4) +
  geom_col(fill = col_blue, alpha = 0.5, width = 0.8) +
  geom_line(aes(y = post_mean), color = col_red, linewidth = 1.1) +
  geom_text(data = events, aes(x = vuosi, y = Inf, label = label),
            vjust = 1.2, size = 2.3, color = "grey50") +
  scale_x_continuous(breaks = seq(1960, 2023, 10)) +
  scale_y_continuous(labels = comma_format(big.mark = " ")) +
  labs(x = NULL, y = "Acts adopted",
       subtitle = "Red line = posterior mean level (bcp model)")

p2 <- ggplot(bcp_df, aes(vuosi, post_prob, fill = post_prob > 0.5)) +
  geom_col(width = 0.8, show.legend = FALSE) +
  geom_hline(yintercept = 0.5, linetype = "dashed",
             color = col_red, linewidth = 0.5) +
  scale_fill_manual(values = c(col_blue, col_red)) +
  scale_x_continuous(breaks = seq(1960, 2023, 10)) +
  scale_y_continuous(labels = percent_format(), limits = c(0, 1)) +
  labs(x = NULL, y = "P(structural break)",
       subtitle = "Red = strong evidence of break (p > 0.5)",
       caption  = "Model: bcp (Barry & Hartigan 1993), p0 = 0.2, MCMC = 5000. Source: EUR-Lex via eurlex (R).")

p1 / p2 +
  plot_annotation(
    title = "**EU legislative output is not a stable process**",
    theme = theme(plot.title = element_markdown(face = "bold", size = 14))
  )

Annual EU legislative output 1960–2023 with Bayesian change point analysis. Red line = posterior mean level. Bar height = posterior probability of structural break at that year.

The Bayesian change point model identifies structural breaks with posterior probability exceeding 0.5 at years corresponding to the Single European Act (1986), the Maastricht Treaty and internal market completion (1992–1993), and the Eastern enlargement (2004). The 1990s peak — driven primarily by regulations — coincides with the preparation of accession countries rather than organic legislative growth.

The practical implication: compliance calendars and regulatory risk models that assume a stable legislative baseline are misspecified. The intensity of the regulatory process is time-varying, and the uncertainty around future intensity is quantifiable.

Finding 2: Implementation delay is a fat-tailed distribution

Show code 
alpha0 <- 2; beta0 <- 2

top_maat <- viive_df |>
  count(maakoodi, sort = TRUE) |>
  filter(n >= 200) |>
  pull(maakoodi)

maa_beta <- viive_df |>
  filter(maakoodi %in% top_maat) |>
  group_by(maakoodi) |>
  summarise(
    k          = sum(myohassa, na.rm = TRUE),
    n          = n(),
    alpha_post = alpha0 + k,
    beta_post  = beta0 + (n - k),
    moodi      = (alpha_post - 1) / (alpha_post + beta_post - 2),
    hdi_lo     = qbeta(0.025, alpha_post, beta_post),
    hdi_hi     = qbeta(0.975, alpha_post, beta_post),
    .groups    = "drop"
  ) |>
  arrange(moodi)

p_dist <- ggplot(viive_df, aes(viive_kk)) +
  geom_histogram(aes(y = after_stat(density)),
                 binwidth = 3, fill = col_blue,
                 alpha = 0.6, color = "white") +
  geom_density(color = col_red, linewidth = 1.0) +
  geom_vline(xintercept = 0, linetype = "dashed",
             color = col_navy, linewidth = 0.8) +
  geom_vline(xintercept = median(viive_df$viive_kk),
             linetype = "dotted", color = col_red, linewidth = 0.7) +
  annotate("text", x = 2, y = Inf, vjust = 1.8, hjust = 0,
           label = "Deadline", color = col_navy, size = 3) +
  annotate("text", x = median(viive_df$viive_kk) + 2, y = Inf,
           vjust = 3.5, hjust = 0,
           label = paste0("Median: ",
                          round(median(viive_df$viive_kk), 1), " mo."),
           color = col_red, size = 3) +
  scale_x_continuous(limits = c(-24, 120),
                     labels = function(x) paste0(x, " mo.")) +
  labs(subtitle = "Full distribution of delay (all countries, all years)",
       x = "Delay relative to transposition deadline", y = "Density")

p_beta <- maa_beta |>
  mutate(maakoodi = fct_reorder(maakoodi, moodi)) |>
  ggplot(aes(moodi, maakoodi)) +
  geom_vline(xintercept = 0.5, linetype = "dashed",
             color = "grey50", linewidth = 0.5) +
  geom_segment(aes(x = hdi_lo, xend = hdi_hi,
                   y = maakoodi, yend = maakoodi),
               color = col_blue, linewidth = 1.2, alpha = 0.5) +
  geom_point(aes(color = moodi > 0.5), size = 2.5, show.legend = FALSE) +
  scale_color_manual(values = c(col_green, col_red)) +
  scale_x_continuous(labels = percent_format(), limits = c(0, 1)) +
  labs(subtitle = "Country-level late implementation rate — posterior mode + 95% HDI",
       x = "P(late)", y = NULL,
       caption = "Model: Beta(2,2) prior + binomial likelihood. Source: EUR-Lex via eurlex (R).")

p_dist + p_beta +
  plot_annotation(
    title = "**Implementation delay is right-skewed and varies substantially across countries**",
    theme = theme(plot.title = element_markdown(face = "bold", size = 14))
  )

Distribution of directive implementation delay relative to transposition deadline. Right panel: country-level posterior probability of late implementation (Beta-Binomial model).

The delay distribution is right-skewed with a fat tail: most implementations are modestly late, but a minority are extremely late. The mean substantially overstates the typical delay; the median is a more robust summary statistic.

The Bayesian Beta-Binomial model quantifies country-level late implementation rates with explicit uncertainty. Countries with few observations receive wider credible intervals — reflecting genuine epistemic uncertainty rather than suppressing it. This is the appropriate treatment for policy monitoring: a country with 50 observations and 60% late rate is not equivalent to a country with 5,000 observations and 60% late rate.

A key methodological note: national_impl records reflect reported implementations only. Failure to report to EUR-Lex is itself informative — but is not captured in this dataset.

Finding 3: Act survival time is type-specific

Show code 
surv_df <- raw |>
  filter(!is.na(date), !is.na(force)) |>
  mutate(
    date         = as.Date(date),
    vuosi        = year(date),
    saadostyyppi = case_when(
      resource_type == "regulation"     ~ "Regulation",
      resource_type == "directive"      ~ "Directive",
      resource_type == "decision"       ~ "Decision",
      resource_type == "recommendation" ~ "Recommendation"
    ),
    tapaus  = if_else(force == "false", 1L, 0L),
    aika_v  = as.numeric(Sys.Date() - date) / 365.25
  ) |>
  filter(vuosi >= 1960, vuosi <= 2022, aika_v > 0, !is.na(saadostyyppi))

surv_obj <- Surv(time = surv_df$aika_v, event = surv_df$tapaus)
km_fit   <- survfit(surv_obj ~ saadostyyppi, data = surv_df)

# Manual KM data extraction for ggplot2
km_df <- map_dfr(
  c("Regulation", "Directive", "Decision", "Recommendation"),
  function(t) {
    fit <- survfit(Surv(aika_v, tapaus) ~ 1,
                   data = filter(surv_df, saadostyyppi == t))
    tibble(
      time         = fit$time,
      surv         = fit$surv,
      lower        = fit$lower,
      upper        = fit$upper,
      saadostyyppi = t
    )
  }
)

ggplot(km_df, aes(time, surv, color = saadostyyppi, fill = saadostyyppi)) +
  geom_ribbon(aes(ymin = lower, ymax = upper), alpha = 0.12, color = NA) +
  geom_line(linewidth = 1.0) +
  scale_color_manual(values = c(col_red, col_green, col_blue, col_orange)) +
  scale_fill_manual(values  = c(col_red, col_green, col_blue, col_orange)) +
  scale_x_continuous(limits = c(0, 50), breaks = seq(0, 50, 10)) +
  scale_y_continuous(labels = percent_format()) +
  labs(
    title    = "**Act survival curves by type** — Kaplan-Meier estimator",
    subtitle = "Survival bias is corrected: acts still in force are treated as censored, not excluded.",
    x = "Age (years)", y = "S(t) — probability of remaining in force",
    color = NULL, fill = NULL,
    caption = "Source: EUR-Lex via eurlex (R). Kristian Vepsäläinen / kristianvepsalainen.com"
  ) +
  theme(legend.position = "top")

Kaplan-Meier survival curves by act type. S(t) = probability of remaining in force at time t. Shaded bands = 95% confidence intervals.

Survival analysis corrects a bias present in naive age distributions: acts still in force are not missing observations — they are right-censored. The Kaplan-Meier estimator handles censoring correctly, producing unbiased estimates of the survival function.

The type-specific curves reveal substantial heterogeneity. Decisions have the shortest median survival time, consistent with their operational rather than structural character. Recommendations show relatively high survival — non-binding instruments are rarely formally repealed even when superseded in practice.

The survival curve is the appropriate object of analysis for regulatory lifecycle questions. A single “average lifespan” number — widely reported in regulatory impact assessments — discards the shape of the distribution and the uncertainty around it.

Methodological summary

Analysis Method Key assumption Data source
Regulatory output Bayesian change point (bcp) Piecewise constant mean regulation, directive, decision, recommendation
Implementation delay Beta-Binomial model Exchangeable countries within prior directive + national_impl
Act survival Kaplan-Meier + Cox PH Non-informative censoring All four types
Citation network PageRank, degree distribution Directed graph, static snapshot regulation, directive, decision

All analyses were conducted in R. Full reproducible code is available in the accompanying blog series at kristianvepsalainen.com.

Data access

The analyses in this whitepaper are reproducible using three data files, each generated by a single R script:

# Install the eurlex package
install.packages("eurlex")

# Fetch legislative acts (parts 1, 4, 5)
tyypit <- c("regulation", "directive", "decision", "recommendation")
raw <- map_dfr(tyypit, ~{
  elx_make_query(resource_type = .x, include_date = TRUE,
                 include_force = TRUE, include_celex = TRUE) |>
    elx_run_query() |> mutate(resource_type = .x)
})
saveRDS(raw, "data/eu_saadanto_raw.rds")

# Fetch directives with transposition deadlines (parts 2, 3)
raw_dir <- elx_make_query("directive", include_date = TRUE,
                           include_date_transpos = TRUE,
                           include_force = TRUE,
                           include_celex = TRUE) |>
  elx_run_query()
saveRDS(raw_dir, "data/eu_dir_raw.rds")

# Fetch national implementation records (parts 2, 3)
# Note: elx_make_query("national_impl") produces invalid SPARQL
# (missing closing parenthesis — upstream bug). Use direct query:
raw_nimpl <- elx_run_query('
  PREFIX cdm: <http://publications.europa.eu/ontology/cdm#>
  select distinct ?work ?celex ?date where {
    ?work cdm:work_has_resource-type
      <http://publications.europa.eu/resource/authority/resource-type/MEAS_NATION_IMPL> .
    OPTIONAL { ?work cdm:resource_legal_id_celex ?celex. }
    OPTIONAL { ?work cdm:work_date_document ?date. }
  }
')
saveRDS(raw_nimpl, "data/eu_nimpl_raw.rds")

Conclusion

The central argument of this whitepaper is methodological: point estimates are insufficient for understanding EU regulatory dynamics. The four findings — non-stationary output, fat-tailed implementation delay, type-specific survival, scale-free citation topology — are not visible from aggregate summaries. They become visible only when the underlying distributions are examined.

This is not an abstract statistical point. Compliance timelines built on mean implementation delay are systematically optimistic. Regulatory risk models that assume stable legislative intensity will be wrong at exactly the moments when they matter most. Impact assessments that report average act lifespans without survival curves discard actionable information.

The data to do this analysis correctly is publicly available, free of charge, and accessible through a single R package. The barrier is not data access — it is the analytical frame.

References

Ovádek, M. (2021). Facilitating access to data on European Union laws. Political Research Exchange, 3(1). DOI: 10.1080/2474736X.2021.1870150

Barry, D. & Hartigan, J.A. (1993). A Bayesian analysis for change point problems. Journal of the American Statistical Association, 88(421), 309–319.

Kaplan, E.L. & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.

Cox, D.R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.

© 2026 Kristian Vepsäläinen. Analysis and code available at kristianvepsalainen.com. Data: EUR-Lex © European Union, 1998–2026.

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