IESET.
Hypotheses·regulatory·gdpr_digital_sector_firm_scale_effect

The cumulative EU digital-sector regulatory stack — GDPR (2018), Digital Markets Act (2022), Digital Services Act (2022), AI Act (2024) — has imposed a measurable fixed-compliance-cost burden that falls disproportionately on scale of EU-headquartered digital firms relative to US counterparts.

Over 2018-2024, EU-headquartered digital firms show lower revenue-per-firm, lower VC-funding-per-firm, lower mean-employee count, and a lower rate of scale-up (defined as growth from <100 to >500 employees) than comparable-stage US firms. The effect is hypothesised to operate through the fixed-cost channel (compliance cost is lumpy and amortised over revenue base; smaller market means thinner spreading). The hypothesis does NOT claim the regulations produce net welfare loss (GDPR's consumer-privacy externality is explicitly outside the outcome set) — only that firm-scale is differentially smaller on the measured dimensions.

INCONCLUSIVEengine/runs/gdpr_digital_sector_firm_scale_effect

INCONCLUSIVE_DATA_PENDING — no outcome variable loaded; missing: ['constructed: Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log). Firm-level data-gated fetcher pending.', 'constructed: total VC dollars invested in digital-sector firms per country-year / number of active digital firms. Crunchbase fetcher pending.', 'constructed: fraction of digital firms that transitioned from <100 to >500 employees within the country-year window. Crunchbase / PitchBook.', 'constructed: share of total employment in ISIC J (information and communication). OECD STAN or ILO where available.']

confidence cueResult card produced; verdict unclassified.

policy briefCoverage too thin

In ordinary language

In plain terms, this asks whether eu post 2018 gdpr dummy is actually linked to better or worse log mean digital firm revenue from 2014 to 2024.

plain answer

This test cannot make a firm call yet. no outcome variable loaded; missing: ['constructed: Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log).

why it matters

This matters because regulatory claims should change belief only when they survive a pre-declared empirical test.

how the test works

It compares 15 country or place units from 2014 to 2024, using a panel fe design, with fixed effects for country and year.

what was measured
What changed
  • Eu post 2018 gdpr dummy
  • Eu post 2022 dma dsa dummy
What we checked
  • Log mean digital firm revenue
  • Log vc funding per active firm
  • Scaleup rate
what this does not prove

A single test is not the whole truth. It narrows the claim under a specific sample, time period, and method. Strong policy conclusions need the pattern to survive nearby tests, alternative data, and serious objections.

verification

No evidence packet has been generated yet.

Results

engine/runs/gdpr_digital_sector_firm_scale_effect
1007550250201420192024DEUFRAITAESPNLDBELPOL
illustrative sketch · run pending
No coefficients yet. When the model fires, this chart will show log_mean_digital_firm_revenue across 15 sampled countries over 2014–2024.
The shapes above are stylised — none of the lines are real data.
Placeholder for gdpr_digital_sector_firm_scale_effect. Published chart will be generated from engine/runs/gdpr_digital_sector_firm_scale_effect/chart_data.json.

Who has skin in the game — schools predicting on this

17 schools list this hypothesis as a test of their position. The chips below are school-level scoreboard outcomes, not a second hypothesis verdict.

hypothesis verdict vs scoreboard outcome

The banner verdict judges this hypothesis as written. The scoreboard asks whether each school's polarity-corrected prediction was right. Raw status is not a school win: SUPPORTED supports schools that needed SUPPORTED, but refutes schools that needed REFUTED.

Austrian Business Cycle Theory (ABCT)wins if SUPPORTEDpartial/no-callChicago School Monetarismwins if SUPPORTEDpartial/no-callClassical Liberalwins if SUPPORTEDpartial/no-callDegrowth / Ecological Macroeconomicswins if REFUTEDpartial/no-callDemocratic Socialism (Bernsteinian revisionism + modern)wins if REFUTEDpartial/no-callStructuralist / Developmentalistwins if MIXEDpartial/no-callEco-Socialism / Red-Greenwins if REFUTEDpartial/no-callEmpirical Pragmatist (the framework's house position)wins if MIXEDpartial/no-callNew Institutional Economicswins if MIXEDpartial/no-callMarket Socialism (Lange-Lerner tradition)wins if REFUTEDpartial/no-callMarxian Political Economywins if REFUTEDpartial/no-callMarxism-Leninism (central-planning implementation)wins if REFUTEDpartial/no-callModern Monetary Theorywins if REFUTEDpartial/no-callNew Keynesian (mainstream synthesis)wins if MIXEDpartial/no-callOrdoliberalism (Freiburg School)wins if SUPPORTEDpartial/no-callPost-Keynesianwins if REFUTEDpartial/no-callSocial Democraticwins if REFUTEDpartial/no-call

Pre-registration

pre-registered
first-spec commit bae09ab · 2026-04-29T22:09:42Z
run generated · 2026-06-29T17:54:29Z

The cumulative EU digital-sector regulatory stack — GDPR (2018), Digital Markets Act (2022), Digital Services Act (2022), AI Act (2024) — has imposed a measurable fixed-compliance-cost burden that falls disproportionately on scale of EU-headquartered digital firms relative to US counterparts. Over 2018-2024, EU-headquartered digital firms show lower revenue-per-firm, lower VC-funding-per-firm, lower mean-employee count, and a lower rate of scale-up (defined as growth from <100 to >500 employees) than comparable-stage US firms. The effect is hypothesised to operate through the fixed-cost channel (compliance cost is lumpy and amortised over revenue base; smaller market means thinner spreading). The hypothesis does NOT claim the regulations produce net welfare loss (GDPR's consumer-privacy externality is explicitly outside the outcome set) — only that firm-scale is differentially smaller on the measured dimensions.

Falsification criterion — what would disprove this

set before the run · honoured after

This hypothesis is considered falsified if:

Not supported if (a) β_eu_post_2018 is non-negative or not statistically significant at p<0.10 on at least two of the four outcome measures, OR (b) the effect vanishes after controlling for capital-market depth and VC-raised-as-share-of-GDP, OR (c) placebo at pre-2018 fake date shows a similar-magnitude "effect" (indicating pre-existing trend rather than GDPR), OR (d) the scale-up rate difference is driven by US superstar firms rather than EU-firm scale-up deficit (decomposable via medians vs means). Report honestly if the scale-gap exists but cannot be attributed to regulation specifically — this is a real and legitimate null.

formal test & threshold
test:      eu_digital_regulation_firm_scale_did
threshold: β_eu_post_2018 < -0.05 log points on log VC funding per firm AND β_eu_post_2018 < -0.02 on scaleup_rate (at p<0.10) AND placebo pre-2018 |t| < 1.65 AND effects survive capital-market-depth control

Method

Template
panel_fe
Fixed effects
country, year
Clustering
country
Sample
15 countries · 2014 – 2024
Evidence type
associational

Two-way FE panel with USA, CAN, and post-2020 GBR as non-EU control. β_eu_post_2018 identifies the EU-member post-GDPR average shift on the four outcome measures. Secondary: event-study around GDPR enforcement date (May 2018), DMA entry into application (2023), and AI Act enforcement phases. Separate coefficient per episode; cumulative burden tested as sum. Identification is associational rather than causal: multiple treatments coincide with other large shocks (COVID, gas-price shock, US Big Tech concentration continuing), and the pure regulation-compliance channel cannot be cleanly separated from co-occurring forces. Placebo at pre-2018 fake dates tested. Known limitations: (1) Crunchbase / PitchBook firm-level data has uneven country coverage; EU coverage weaker than US. Measurement bias could under- or overstate effects in unknown directions. (2) Capital-market depth is a confound with known importance — EU VC markets are thinner independently of regulation. Controlling for VC raised as share of GDP partly addresses, but collinearity with treatment is real. (3) Big Tech concentration in the US is partly a tax and antitrust story independent of EU regulation. The comparison is not "EU-regulated vs counterfactual EU-unregulated" but "EU-regulated vs US-different-bundle."

Data

VariableSourceTransform
log_mean_digital_firm_revenue
outcome
constructed:Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log). Firm-level data-gated ftier 5
log
log_vc_funding_per_active_firm
outcome
constructed:total VC dollars invested in digital-sector firms per country-year / number of active digital firms. Crunchbase fetcher tier 5
log
scaleup_rate
outcome
constructed:fraction of digital firms that transitioned from <100 to >500 employees within the country-year window. Crunchbase / Pittier 5
level
digital_sector_employment_share
outcome
constructed:share of total employment in ISIC J (information and communication). OECD STAN or ILO where available.tier 5
level
eu_post_2018_gdpr_dummy
treatment
constructed:indicator = 1 for EU member states in year 2018 onwards. USA, CAN, GBR (post-2020) = control.tier 5
indicator
eu_post_2022_dma_dsa_dummy
treatment
constructed:indicator for EU member states in 2022 onwards, incremental cumulative burden.tier 5
indicator
eu_post_2024_ai_act_dummy
treatment
constructed:indicator for EU member states in 2024 onwards; 2024 effect will be shallow given recency.tier 5
indicator
log_population
control
world_bank_wdi:SP.POP.TOTLtier 2
log
oecd_pmr_digital_subindex
control
oecd_pmr:OECD.ECO.GCRDtier 4
level
log_gdp_pc_ppp
control
world_bank_wdi:NY.GDP.PCAP.PP.KDtier 2
log
capital_market_depth_proxy
control
constructed:domestic VC dollars raised as share of GDP (Invest Europe + NVCA). Fetcher pending.tier 5
level

● ready  · ● pending  · ● reconstruct-needed

Detailed result card

Result card — gdpr_digital_sector_firm_scale_effect

Verdict: INCONCLUSIVE_DATA_PENDING — no outcome variable loaded; missing: ['constructed: Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log). Firm-level data-gated fetcher pending.', 'constructed: total VC dollars invested in digital-sector firms per country-year / number of active digital firms. Crunchbase fetcher pending.', 'constructed: fraction of digital firms that transitioned from <100 to >500 employees within the country-year window. Crunchbase / PitchBook.', 'constructed: share of total employment in ISIC J (information and communication). OECD STAN or ILO where available.']

Pre-registration

  • Claim: The cumulative EU digital-sector regulatory stack — GDPR (2018), Digital Markets Act (2022), Digital Services Act (2022), AI Act (2024) — has imposed a measurable fixed-compliance-cost burden that falls disproportionately on scale of EU-headquartered digital firms relative to US counterparts. Over 2018-2024, EU-headquartered digital firms show lower revenue-per-firm, lower VC-funding-per-firm, lower mean-employee count, and a lower rate of scale-up (defined as growth from <100 to >500 employees) than comparable-stage US firms. The effect is hypothesised to operate through the fixed-cost channel (compliance cost is lumpy and amortised over revenue base; smaller market means thinner spreading). The hypothesis does NOT claim the regulations produce net welfare loss (GDPR's consumer-privacy externality is explicitly outside the outcome set) — only that firm-scale is differentially smaller on the measured dimensions.
  • Falsification rule: Not supported if (a) β_eu_post_2018 is non-negative or not statistically significant at p<0.10 on at least two of the four outcome measures, OR (b) the effect vanishes after controlling for capital-market depth and VC-raised-as-share-of-GDP, OR (c) placebo at pre-2018 fake date shows a similar-magnitude "effect" (indicating pre-existing trend rather than GDPR), OR (d) the scale-up rate difference is driven by US superstar firms rather than EU-firm scale-up deficit (decomposable via medians vs means). Report honestly if the scale-gap exists but cannot be attributed to regulation specifically — this is a real and legitimate null.
  • Falsification test: eu_digital_regulation_firm_scale_did

Estimate

  • Error: no outcome variable loaded; missing: ['constructed: Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log). Firm-level data-gated fetcher pending.', 'constructed: total VC dollars invested in digital-sector firms per country-year / number of active digital firms. Crunchbase fetcher pending.', 'constructed: fraction of digital firms that transitioned from <100 to >500 employees within the country-year window. Crunchbase / PitchBook.', 'constructed: share of total employment in ISIC J (information and communication). OECD STAN or ILO where available.']

Variables resolved

  • constructed: indicator = 1 for EU member states in year 2018 onwards. USA, CAN, GBR (post-2020) = control. → eu_post_2018_gdpr_dummy (treatment, publisher=constructed, n=165)
  • constructed: indicator for EU member states in 2022 onwards, incremental cumulative burden. → eu_post_2022_dma_dsa_dummy (treatment, publisher=constructed, n=165)
  • constructed: indicator for EU member states in 2024 onwards; 2024 effect will be shallow given recency. → eu_post_2024_ai_act_dummy (treatment, publisher=constructed, n=165)
  • world_bank_wdi:SP.POP.TOTL → log_population (controls, publisher=world_bank_wdi, n=14447)
  • oecd_pmr:OECD.ECO.GCRD,DSD_PMR@DF_PMR,1.2 → oecd_pmr_digital_subindex (controls, publisher=oecd_pmr, n=105)
  • world_bank_wdi:NY.GDP.PCAP.PP.KD → log_gdp_pc_ppp (controls, publisher=world_bank_wdi, n=8325)

Variables missing data

  • constructed: Crunchbase / PitchBook firm-level digital-sector revenue, aggregated to country-year mean (log). Firm-level data-gated fetcher pending. (outcome, name=log_mean_digital_firm_revenue) — vintage not on disk
  • constructed: total VC dollars invested in digital-sector firms per country-year / number of active digital firms. Crunchbase fetcher pending. (outcome, name=log_vc_funding_per_active_firm) — vintage not on disk
  • constructed: fraction of digital firms that transitioned from <100 to >500 employees within the country-year window. Crunchbase / PitchBook. (outcome, name=scaleup_rate) — vintage not on disk
  • constructed: share of total employment in ISIC J (information and communication). OECD STAN or ILO where available. (outcome, name=digital_sector_employment_share) — vintage not on disk
  • constructed: domestic VC dollars raised as share of GDP (Invest Europe + NVCA). Fetcher pending. (controls, name=capital_market_depth_proxy) — vintage not on disk

Generated by scripts/run_panel_fe.py at 2026-06-29T17:54:29+00:00

Strongest opposing argument

Every hypothesis ships with its charitable opposing argument. The framework earns credibility by handling objections at their strongest, not weakest.

Notes

Data readiness: - Crunchbase / PitchBook firm-level — commercial, data-gated, fetcher pending and possibly requires partnership - OECD PMR digital subindex — ready - OECD STAN ICT sector VA — fetcher pending - Invest Europe + NVCA VC totals — fetcher pending - WDI, IMF — ready This hypothesis is data-gated on firm-level commercial sources. A weaker v1 using only sector-aggregate outcomes (ICT VA, ICT employment, aggregate VC inflows) is runnable now; v1.1 with firm-level outcomes when commercial data access lands.

Authored framework. Read the transparency note.