Perceptions of courts under polarization

Trust gap by government support

Each cell shows how much more, or less, supporters of the parties in cabinet on the interview date trusted the legal system than supporters of the opposition. The smaller figure underneath is the same gap for trust in the country's parliament, from the same respondents. A positive court gap means the government side trusted courts more.

What the matrix shows

The government side trusted courts more in 209 of the 267 country-rounds, and parliament more in 246. Averaged across countries, the absolute court gap rose from about 0.33 points in 2002 to 0.77 in 2023, while the parliament gap rose from 0.54 to 1.03. Courts appear to follow the same axis as parliament at lower amplitude in most countries, and the spread across countries is wide in every round.

In 14 country-rounds the two signs part company, with the government side trusting parliament more by at least half a point and courts less: Spain 2020, Spain 2022, Britain 2002, Britain 2004, Britain 2020, Israel 2014, Israel 2016, Israel 2022, Italy 2002, Italy 2022, Lithuania 2010, Netherlands 2010, Slovenia 2006, Slovenia 2020. Israel 2022 is the largest such case in the file, and it is also the cell with the fewest respondents on each side.

Trust gap by left-right self-placement

The same cells, with respondents placing themselves at 6 to 10 on the left-right scale compared with those at 0 to 4. A positive gap means the right trusted the institution more. This version needs no party linkage, so it covers countries outside ParlGov, and it is the one to read where governments changed mid-fieldwork.

Beyond the European Social Survey: the Integrated Values Surveys, 1981 to 2023

The European Social Survey reaches about 30 European countries from 2002. The Integrated Values Surveys, the World Values Survey trend file appended to its European sister survey the European Values Study, reach 107 countries from 1981. Both halves are built on the same common dictionary, so the courts and left-right items carry the same names and the two files append. Cells are again the right minus the left, with the parliament gap underneath, and countries run down the page by wave.

Two differences from the European figures above matter for reading the numbers. The Integrated Values Surveys ask about confidence in four steps, from a great deal to none at all, rather than trust on an eleven-point scale; the values here are reversed so that higher means more confidence and then stretched onto a 0 to 10 range so a gap can be set beside the European one. That stretch is arithmetic, not a claim that the two questions measure the same thing. And left and right come from a 1 to 10 self-placement with no midpoint, so the left is 1 to 4, the right is 7 to 10, and the two middle positions are dropped.

What the wider coverage adds

The European pattern holds outside Europe. The right is more confident in courts in 283 of the 376 country-years, and the court gap is smaller in absolute terms than the parliament gap in 229 of them. Averaged across country-years the court gap is 0.59 points against 0.73 for parliament, on the rescaled range.

The growth over time does not carry across. Within the 61 countries surveyed at least three times, the absolute court gap is wider at the last observation than at the first in 22 of them, so the doubling visible in the European series since 2002 does not appear as a global trend. The country mix changes from wave to wave, which is why this comparison is made within countries rather than across the pooled averages.

10 country-years run the opposite way, with the right more confident in parliament by at least half a point and less confident in courts: Armenia 2021, Australia 2005, Bulgaria 1997, India 2012, Italy 2005, Italy 2009, Lithuania 1997, Pakistan 2012, Poland 2008, Poland 2017.

Which division divides trust in courts

Round 5 of the European Social Survey, fielded in 2010, is the only round that carried a module on the police and the courts. It asked the same people the standard trust questions and a set of questions about their own experience, which makes it possible to take one country in one year and compute the gap in trust in the legal system under five different divisions, then set them side by side. Every cell below is a difference of weighted means of the same 0 to 10 item among the same respondents, so the five columns are directly comparable. The small figures give the size of the two groups.

What the comparison shows

Averaged across countries, the division between people who belong to an ethnic minority and everyone else is about the same size as the division between right and left, and larger than the division between government and opposition supporters. Being stopped by the police, or having been a victim of crime, divides trust less.

0.54 ethnic minority against everyone else, across 20 countries

0.52 right against left, across 27 countries

0.37 government supporters against opposition supporters, across 25 countries

0.32 crime victims against non-victims, across 27 countries

0.22 stopped by police against not stopped, across 27 countries

In 13 of the 18 countries where both can be measured, the minority division is wider than the government one. The second reading of the question expects a division over courts that does not follow the party line, and this is where it appears.

Three cautions belong with it. The minority samples are small, from 59 to 440 respondents, and twenty countries have enough minority respondents to measure at all. The division runs in opposite directions in different countries: minorities report more trust than the majority in Bulgaria, Switzerland, Germany, Spain, France, Britain, Greece, Ireland, Israel, Russia, and less in Czechia, Estonia, Lithuania, Netherlands, Ukraine. And the module ran once, in 2010, so it compares countries rather than tracking change.

What minorities say about courts

The same module asked what people think courts actually do. Respondents who belong to an ethnic minority were on average 6.5 percentage points more likely than others to say that, of two people falsely accused of an identical crime, the one from a different ethnic group is more likely to be found guilty. On the other items they answered much like everyone else: agreement that courts protect the rich and powerful, agreement that court decisions are politically influenced, how often judges take bribes, and how often courts decide fairly all differ by under two percentage points or a tenth of a point.

The difference minorities report is specific to ethnicity rather than a general suspicion of the courts.

Against what the courts were doing

The surveys measure how far apart two groups of citizens are. V-Dem measures, for the same country and the same year, how independent the courts were and how polarized the politics was. Attaching one to the other asks whether the division among citizens tracks the condition of the institution. Every European Social Survey country-round matched a V-Dem country-year, using the year in which the interviews were actually done rather than the round's label.

The first column correlates each indicator with the government-minus-opposition gap in trust in courts across all country-rounds. The second removes each country's own average first, so it compares a country with itself over time rather than richer countries with poorer ones. The third does the same for the difference between the court gap and the parliament gap.

What the correlations suggest

Where high courts are more independent, government and opposition supporters differ less about the legal system, and the relationship survives removing each country's own average (-0.23). Where politics is more polarized into camps, they differ more (+0.19). Both associations are modest and neither is an effect estimate.

Government attacks on the judiciary carry the largest correlation in the table. Within a country over time, they track the difference between the court gap and the parliament gap at +0.42. The V-Dem indicator runs in the direction its name does not suggest: a high score means the judiciary is left alone, and the score falls as a government attacks it. Poland's score falls from about +2.3 before 2015 to about -3.6 by 2020, while Denmark stays near +1.8 throughout. So in the years when a government is attacking the courts, the party division over parliament widens faster than the division over courts.

Poland is the clearest case in the file. Under the Law and Justice governments the gap between government and opposition supporters over parliament reached about two and a half points, while the gap over the legal system stayed below one. Attacking the courts did not, on this measure, make the courts the thing the two sides disagreed about most.

Affective polarization

Everything above measures a gap between two groups' average trust, which is divergence between groups. Affective polarization is a different quantity: how much warmer people feel toward their own side than toward the other. Two sources measure it directly and ask about courts of the same respondents.

The United States, the same people three times

The American National Election Studies panel rates both parties and the Supreme Court on the same 0 to 100 scale, and follows the same respondents from 2016 to 2024. Affective polarization is the warmth a partisan reports toward their own party minus the other one, and the last two columns are the correlation, within each party, between a person's affective polarization and their rating of the Court.

In 2016 Democrats and Republicans rated the Court 60.3 and 58.8, within two points of each other, and a person's partisan hostility told you nothing about their view of it. By 2024 the ratings are 33.8 and 65.1, and the same correlation runs -0.29 among Democrats and +0.34 among Republicans. Over eight years the Court was drawn into the affective structure of partisanship, having sat outside it.

The panel adds a caution. Among the same people from 2020 to 2024 the Democratic rating falls about twenty points while their affective polarization barely changes, and the two changes are uncorrelated. The fall was collective rather than a by-product of individuals growing more hostile.

Sixteen other elections, where it does not repeat

The Comparative Study of Electoral Systems asks respondents to rate every significant party from 0 to 10 and asks the same people about trust in the judiciary. Affective polarization here is the spread of a person's party ratings. Within electorates the two are close to unrelated, and splitting by ideological side, as the American case requires, the signs oppose in 8 of 15 studies while almost every correlation sits under 0.2.

Turkey in 2023 is the clearest exception, with a correlation of +0.20 on the right and the two sides 4.2 points apart on trust in the judiciary. On this evidence the United States is the outlier rather than the template.

Canada

Statistics Canada's Canadian Social Survey asks how much confidence people have in the justice system and courts on a five point scale, every few months since 2022, and publishes the results by province and by sociodemographic group. The figures below are the share choosing 4 or 5, meaning high confidence.

The national figure barely shifts, hovering near half, but the country is not uniform underneath it. Quebec sits about twenty points above British Columbia in every wave.

Who has confidence, and who does not

The group breakdown is where Canada speaks to the European finding above. The lowest confidence anywhere in the table belongs to First Nations respondents, at 22.2 per cent. The highest belongs to recent immigrants and non-permanent residents, above sixty per cent.

That split matters for how the European result should be read. Calling a group an ethnic minority does not settle which way its confidence runs. Immigrant-origin minorities in Canada report more confidence in the courts than the average, as they do in western Europe, while Indigenous people report far less, as Roma and Russian-speaking minorities do in central and eastern Europe. The division is between groups that arrived and groups that were subordinated in place.

Britain since 1983

Ipsos has asked the same question in Britain every year since 1983: whether people generally trust each of a list of professions to tell the truth. Judges are on that list, which makes it the longest continuous series on public trust in the judiciary anywhere in this project. Trust in judges has run near eighty per cent for most of four decades, at 77 per cent in 1983, 80 in 2009, 74 in 2023 and 78 in 2025.

Two things break the comparison with everything above. The measure is whether a profession tells the truth rather than confidence in an institution, and the mode changed from face to face to telephone in 2020, so points on either side of that line are not strictly comparable. Eleven years between 1984 and 2012 cannot be recovered at all, because Ipsos published them only as an unlabelled line on a chart.

The 2025 breakdown carries the finding most useful here. Trust in judges runs at 91 per cent among Conservative and Liberal Democrat identifiers and 89 among Labour ones, against 60 among Reform UK identifiers. A 31 point spread by party sits in a country whose party gap on the European Social Survey court item is modest, which suggests the division follows a newer party alignment than the left and right axis picks up.

Ipsos publishes the index free but reserves all rights to it, so the series is held in the project repository and is not republished here as a download.

How a respondent becomes a government or opposition supporter

1. The party the respondent reports voting for in the last national election, from the ESS country-specific vote variable. In Germany and Lithuania the list vote is used.
2. That code is matched to a Party Facts identifier through the Party Facts import of the ESS (Bederke and Döring 2023), then to a ParlGov party through the Party Facts external-parties table.
3. ParlGov's cabinet table gives the parties holding cabinet seats on the interview date. A voter is on the government side if any party on the list they voted for held a seat, on the opposition side if the list is linked to ParlGov but none of its parties did. Votes for "other", blank ballots, and parties absent from ParlGov are left out, and the share left out is reported in the coverage file.

Two hand-written tables complete the chain. manual_parlgov_links.csv adds about a hundred links that the automatic chain misses: alliances that Party Facts records under one generic identifier, party lineages that Party Facts splits and ParlGov merges, and the 2023 round in Bulgaria, Estonia, Israel, Latvia and Sweden, which Party Facts had not imported. cabinet_supplement.csv adds twenty-six cabinets formed after ParlGov's June 2023 cutoff, each checked against a government or parliament source on 16 September 2026. That check corrected six rows, including a Polish cabinet that had been recorded as having no governing party when it was a Law and Justice cabinet with full powers for a fortnight, and a Spanish one that listed two parties holding no cabinet seat. Parties absent from ParlGov's 2023 release, such as Poland 2050, Sumar's smaller components, Hlas and NSC, cannot be linked, so their voters are excluded rather than misclassified.

Coverage in the 2023 round

The round most exposed to the cabinet supplement. Fieldwork dates come from the interview records; the cabinet column lists every cabinet in office at some point during fieldwork.

Data and code

Every table behind this page is published as CSV and as JSON at the same address, with no key and no rate limit. The R and Python snippets below reproduce the headline numbers from those files, so nothing here needs the raw survey microdata, which cannot be redistributed.

# Reproduces the headline numbers on this page. The full script is R/reproduce.R in the repo.
base <- "https://courts-polarization.pages.dev/data/"
gov  <- read.csv(paste0(base, "ess_gov_gaps.csv"))
ax   <- read.csv(paste0(base, "ess5_axis_gaps.csv"))

# courts divide government from opposition less than parliament does, in every round
aggregate(cbind(court = abs(gov$trstlgl_gap), parl = abs(gov$trstprl_gap)),
          by = list(round = gov$essround), FUN = mean)

# which division divides trust in courts most, ESS Round 5 (2010)
sapply(c("government", "left_right", "ethnic_minority",
         "crime_victim", "police_contact"),
       function(k) mean(abs(ax[[paste0(k, "_gap")]]), na.rm = TRUE))

# the country-rounds where courts and parliament part company
subset(gov, trstlgl_gap < 0 & trstprl_gap > 0.5,
       select = c(cntry, year, trstlgl_gap, trstprl_gap))

# Same numbers in pandas. The pipeline that builds these files is src/ in the repo.
import pandas as pd
base = "https://courts-polarization.pages.dev/data/"
gov = pd.read_csv(base + "ess_gov_gaps.csv")
ax  = pd.read_csv(base + "ess5_axis_gaps.csv")

gov.groupby("essround")[["trstlgl_gap", "trstprl_gap"]].agg(lambda s: s.abs().mean())

{k: ax[f"{k}_gap"].abs().mean() for k in
 ["government", "left_right", "ethnic_minority", "crime_victim", "police_contact"]}

gov.query("trstlgl_gap < 0 and trstprl_gap > 0.5")[["cntry", "year", "trstlgl_gap", "trstprl_gap"]]

curl -O https://courts-polarization.pages.dev/data/ess_gov_gaps.csv
curl -s https://courts-polarization.pages.dev/data/ess5_axis_gaps.json | jq '.[0]'
curl -s https://courts-polarization.pages.dev/data/context_summary.json | jq -r '.[] | [.indicator, .r_court_gap_within_country] | @tsv'

Levi, Ron (2026). Perceptions of courts under polarization.
Global Justice Lab, Munk School of Global Affairs & Public Policy,
University of Toronto. https://courts-polarization.pages.dev

@misc{levi2026courts,
  author = {Levi, Ron},
  title  = {Perceptions of courts under polarization},
  year   = {2026},
  note   = {Global Justice Lab, Munk School of Global Affairs and Public Policy,
            University of Toronto},
  url    = {https://courts-polarization.pages.dev}
}

# the surveys themselves, which must be cited separately
ESS ERIC. European Social Survey rounds 1-11, integrated files. doi:10.21338/ess1e06_7 ... ess11e04_2
Haerpfer, C. et al. (2022). World Values Survey Trend File 1981-2022, v4.1. doi:10.14281/18241.27
Doering, H. and P. Manow. ParlGov, development release, 4 November 2023.
Bederke, P. and H. Doering (2023). Party Facts ESS linking. doi:10.5281/zenodo.8421232
Coppedge, M. et al. (2026). V-Dem Country-Year dataset v16. CC BY-SA 4.0.

The pipeline that builds these files from the raw microdata is in the project repository: src/download_ess.py, src/build_gov_gaps.py, src/build_ivs_gaps.py, src/build_ess5_justice.py, src/extract_vdem.R, src/build_context.py and src/build_site.py. One command, python3 reproduce.py, runs all of them and prints the numbers quoted on this page. Survey microdata is not redistributed here; each download script fetches it under the provider's own terms.