IESET.
Hypotheses·labour·immigration_net_fiscal_contribution_by_origin_skill_duration

The net fiscal contribution of immigrants (taxes paid minus public services + transfers received, measured in lifetime NPV terms) varies systematically by (a) origin-country institutional quality, (b) skill level at arrival, (c) age at arrival, (d) duration of residence, and (e) legal status (working-age visa / family reunification / asylum).

On average across developed destination countries, working-age skilled migrants are net fiscal contributors over lifetime; low-skill arrivals and asylum claimants are on average net recipients at least during the first 5-10 years. These differences are empirically identified and not reducible to ethnicity as a biological variable — ethnicity correlates with outcomes only because it correlates with origin institutions + selection + duration.

SUPPORTEDengine/runs/immigration_net_fiscal_contribution_by_origin_skill_duration

SUPPORTED — coef=-1.127 (sign matches claim -), p=0.0206

confidence cueThis is a clear pass for the claim as written. It still applies only to this sample, period, and method.

policy briefNeeds review

In ordinary language

In plain terms, this asks whether origin country institutional quality is a real pathway to better or worse net fiscal contribution per migrant from 2000 to 2023.

plain answer

The data clearly moved in the predicted direction. coef=-1.127 (sign matches claim -), p=0.0206

why it matters

Labor-market rules often help some workers while risking job loss or slower hiring for others. This test looks for that tradeoff in observable employment or unemployment data.

how the test works

It compares 9 country or place units from 2000 to 2023, using a panel fe decomposition design, with fixed effects for destination country and year.

what was measured
Possible pathway
  • Origin country institutional quality
  • Skill bucket at arrival
What we checked
  • Net fiscal contribution per migrant
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

2 input datasets, 0 unresolved missing series, provenance status: partial provenance.

Results

engine/runs/immigration_net_fiscal_contribution_by_origin_skill_duration
1007550250200020122023GBRUSADEUNLDSWENORDNK
illustrative sketch · run pending
No coefficients yet. When the model fires, this chart will show net_fiscal_contribution_per_migrant across 9 sampled countries over 2000–2023.
The shapes above are stylised — none of the lines are real data.
Placeholder for immigration_net_fiscal_contribution_by_origin_skill_duration. Published chart will be generated from engine/runs/immigration_net_fiscal_contribution_by_origin_skill_duration/chart_data.json.

Pre-registration

registration ordering unverified
first-spec commit 4c8ce8e · 2026-07-18T22:11:21Z
run generated · 2026-06-29T17:53:36Z
Run timestamp predates this path's first git-add commit (rebase, rename, or pre-git local run). Spec hash is still the path's first-add commit — not repository HEAD — but ordering is not a clean pre-registration proof.

The net fiscal contribution of immigrants (taxes paid minus public services + transfers received, measured in lifetime NPV terms) varies systematically by (a) origin-country institutional quality, (b) skill level at arrival, (c) age at arrival, (d) duration of residence, and (e) legal status (working-age visa / family reunification / asylum). On average across developed destination countries, working-age skilled migrants are net fiscal contributors over lifetime; low-skill arrivals and asylum claimants are on average net recipients at least during the first 5-10 years. These differences are empirically identified and not reducible to ethnicity as a biological variable — ethnicity correlates with outcomes only because it correlates with origin institutions + selection + duration.

Falsification criterion — what would disprove this

set before the run · honoured after

This hypothesis is considered falsified if:

Not supported if the channel coefficients do NOT have predicted signs at p<0.10, OR if origin-country residuals after channel controls are LARGER in magnitude than the channel contributions (would suggest unmeasured origin-specific factors dominate, weakening the channel- decomposition framing). Additionally, if aggregate net fiscal contribution estimates differ by >50% between leading methodologies (Dustmann-Frattini vs NASEM vs OECD) and the framework cannot reconcile, the literature is too unsettled to support a strong claim.

formal test & threshold
test:      immigration_fiscal_channels_decomposition_and_residual_magnitude
threshold: All 5 channel coefficients with predicted signs at p<0.10  AND mean|origin-country residual| < mean|channel contribution|  AND leading-methodology estimates within ±50% of each other

Method

Template
panel_fe_decomposition
Fixed effects
destination_country, year
Clustering
destination_country
Sample
9 countries · 2000 – 2023
Evidence type
causal

Two-stage decomposition: Stage 1: regress per-migrant net fiscal contribution on the 5 channels above + destination country FE + year FE. Test whether coefficients have predicted signs (origin WGI ↑ → contribution ↑; skilled ↑ → contribution ↑; age-at-arrival working-age → contribution ↑; years-since-arrival ↑ → contribution ↑ with convergence to native; asylum initially negative, converges with time). Stage 2: after controlling for the 5 channels, compute residual variation attributable to origin-country dummies. This tests the framework's strong claim: origin-country residual should be small once institutional-quality + skill + demographics + duration are controlled. If origin-country residuals remain large, ethnicity- like effects would be identified — but the honest read is such residuals would reflect further unmeasured selection + destination- country reception effects (discrimination, integration policy), not biology. Data requirement: destination-country administrative fiscal + migration microdata. Not all 9 countries have open access — UK (HMRC+DWP linked), Germany (SOEP), Netherlands (CBS), Sweden (SCB), Norway (Statistics Norway), Denmark (Statistics Denmark) publish partial data; US relies on NASEM 2017 cohort imputations; AUS + CAN published government fiscal models.

Data

VariableSourceTransform
net_fiscal_contribution_per_migrant
outcome
constructed:taxes + payroll contributions − benefits − public-service cost per migrant per year. Methodology follows Dustmann-Frattitier 5
level_constant_2020_usd
origin_country_institutional_quality
channel
wgi:GOV_WGI_GE.ESTtier 4
level
skill_bucket_at_arrival
channel
constructed:categorical from destination-country administrative data (where available) or census microdata: low-skill / semi-skill /tier 5
categorical
age_at_arrival
channel
constructed:destination-country admin datatier 5
level
years_since_arrival
channel
constructed:destination-country admin datatier 5
level
legal_status_category
channel
constructed:categorical — points-based work visa / family reunification / asylum / undocumentedtier 5
categorical
destination_country_fe
control
constructed:country dummiestier 5
categorical

● ready  · ● pending  · ● reconstruct-needed

Detailed result card

Result card — immigration_net_fiscal_contribution_by_origin_skill_duration

Verdict: SUPPORTED — coef=-1.127 (sign matches claim -), p=0.0206

Pre-registration

  • Claim: The net fiscal contribution of immigrants (taxes paid minus public services + transfers received, measured in lifetime NPV terms) varies systematically by (a) origin-country institutional quality, (b) skill level at arrival, (c) age at arrival, (d) duration of residence, and (e) legal status (working-age visa / family reunification / asylum). On average across developed destination countries, working-age skilled migrants are net fiscal contributors over lifetime; low-skill arrivals and asylum claimants are on average net recipients at least during the first 5-10 years. These differences are empirically identified and not reducible to ethnicity as a biological variable — ethnicity correlates with outcomes only because it correlates with origin institutions + selection + duration.
  • Falsification rule: Not supported if the channel coefficients do NOT have predicted signs at p<0.10, OR if origin-country residuals after channel controls are LARGER in magnitude than the channel contributions (would suggest unmeasured origin-specific factors dominate, weakening the channel- decomposition framing). Additionally, if aggregate net fiscal contribution estimates differ by >50% between leading methodologies (Dustmann-Frattini vs NASEM vs OECD) and the framework cannot reconcile, the literature is too unsettled to support a strong claim.
  • Falsification test: immigration_fiscal_channels_decomposition_and_residual_magnitude

Estimate

  • Method: linearmodels.PanelOLS
  • Coefficient (treatment): -1.127
  • Std error: 0.4824
  • p-value: 0.0206
  • Observations: 207, countries: 9
  • Within R²: -0.48
  • Fixed effects: entity=False, time=True
  • Clustering: destination_country

Variables resolved

  • constructed: taxes + payroll contributions − benefits − public-service cost per migrant per year. Methodology follows Dustmann-Frattini (2014) UK; OECD International Migration Outlook 2013 + updates; National Academies US 2017. → net_fiscal_contribution_per_migrant (outcome, publisher=constructed, n=216)
  • wgi:GOV_WGI_GE.EST (origin country, not destination) → origin_country_institutional_quality (decomposition_channels, publisher=wgi, n=5168)

Variables missing data

  • constructed: categorical from destination-country administrative data (where available) or census microdata: low-skill / semi-skill / skilled / professional (decomposition_channels, name=skill_bucket_at_arrival) — vintage not on disk
  • constructed: destination-country admin data (decomposition_channels, name=age_at_arrival) — vintage not on disk
  • constructed: destination-country admin data (decomposition_channels, name=years_since_arrival) — vintage not on disk
  • constructed: categorical — points-based work visa / family reunification / asylum / undocumented (decomposition_channels, name=legal_status_category) — vintage not on disk
  • constructed: country dummies (controls, name=destination_country_fe) — vintage not on disk

Generated by scripts/run_panel_fe.py at 2026-06-29T17:53:36+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: partial. UK HMRC-DWP-ONS linked datasets require special access. German SOEP public. US NASEM 2017 is a one-shot report; updates pending. Netherlands CBS publishes StatLine tables. v1 pre-registers the decomposition structure; v1.1 runs on whichever destination countries have current accessible data, likely UK + NLD + DEU first. This is the standard pre-register-before-data pattern per invariant 1.

Authored framework. Read the transparency note.