Ideas Have Consequences: Ash, Chen & Naidu (2026)

What this is. The paper’s core results, identification design, and estimating equations: enough to know what it found and how, without reading the full 43 pages. To replicate or extend, read the original at https://doi.org/10.1093/qje/qjaf042. Replication data are available at Harvard Dataverse.

TL;DR

The paper estimates the causal effect of economics training on U.S. federal judges. The treatment is attendance at the Manne Economics Institute for Federal Judges, an intensive two-week program run by Henry Manne and the Law and Economics Center (LEC) starting in 1976. The program was oversubscribed from its second year onward, with admission on a first-come, first-served basis, which the authors exploit as a source of quasi-random variation in the timing of attendance. The sample covers the universe of published opinions in U.S. Circuit Courts (1970-2005) and approximately 1 million District Court criminal sentencing decisions (1992-2011; main event-study analysis uses 1992-2003). After attending the program, judges used more economics language in their opinions (roughly 0.36 standard deviations more word-embedding similarity to the economics lexicon), voted more often against federal labor and environmental regulatory agencies (roughly 16 percentage points more), and imposed prison sentences more frequently (roughly 6 percentage points more in the short run). Effects are concentrated in economics-related cases; no significant shift appears in non-economics cases.

Core results

Significance is as reported; standard errors clustered by judge. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Manne attendance increased economics language use by ~0.36 SD short-run (all years)	Table I col. (1), p. 868; Figure III, p. 869	beta=0.363 (se=0.154), N=5,261; significant at 5%
R2	Effect on economics language is larger in the early period (pre-1987): ~0.43 SD	Table I col. (2), p. 868	beta=0.430 (se=0.1305), N=3,214; significant at 1%
R3	Manne attendance raised probability of voting against labor/environmental regulators by ~16 pp	Table I col. (5), p. 868; Figure IV, p. 872	beta=0.162 (se=0.0675), N=2,408; significant at 5%
R4	Conservative voting in economics cases increased by ~27 pp short-run (long-run conservative estimate: ~4 pp)	Table I col. (9), p. 868; col. (11) discussed p. 877	beta=0.265 (se=0.1316), N=804 short-run; beta=0.043 long-run conservative estimate
R5	No significant effect on conservative voting in non-economics cases	Table I cols. (13)-(16), p. 868	beta=0.02 to 0.06 across specs, all insignificant; consistent with economics-specific channel
R6	Manne attendance increased probability of imposing a prison sentence by ~6.2 pp short-run	Table II col. (1), p. 869; Figure V, p. 875	beta=0.0617 (se=0.0202), N=70,784; significant at 1%
R7	Long-run effect on prison sentence probability: ~3.5 pp	Table II col. (2), p. 869	beta=0.035 (se=0.0135), N=260,516; significant at 5%

Overall (paper’s conclusion). The U.S. law and economics movement, disseminated through the Manne program, shifted legal outcomes across the federal judiciary. The finding that neither the legalist view (judges apply law as written) nor the attitudinalist view (judges follow party) can explain the results points to a third channel: within-judge shifts in the intellectual framework used to decide cases. The persuasion rate for conservative voting in economics cases is approximately 8%, comparable in magnitude to major media-effects estimates such as the Fox News effect on Republican voting estimated by DellaVigna and Kaplan (2007).

Theory / model

The paper has no formal structural model. The theoretical framework proceeds in three steps.

Step 1: Two null benchmarks. A legal formalist would predict no effect: judges apply statutes and precedents as written, leaving no room for a training program to shift outcomes. An attitudinalist would likewise predict no effect: judges follow their appointing party platform, and training within the window of a sitting judge’s tenure cannot move a party affiliation. Both predict a zero treatment effect. The paper argues that neither model fits because judges in a common-law system have substantial discretion, and the empirical results confirm it.

Step 2: Why economics? The best prior evidence on ideological influence in courts came from Bonica et al. (2019), who found that changes in clerk ideology sometimes shift a Supreme Court justice’s votes; the Manne program extended and broadened that channel. The paper invokes the Bayesian persuasion framework of Gentzkow and Kamenica (2011) to explain why economics, as a rigorous social science, is especially persuasive with professional judges (p. 880). The Manne curriculum corresponds to a signal structure with commitment: the instructor, bound by academic or scientific norms, reveals the results of economic analysis even when the findings may conflict with the preferred conclusion of funders. In the Gentzkow-Kamenica (2011) framework, the principal will choose either an informative signal or none at all; thus, even if the judge knows the economist is biased for a particular outcome, the economist can still influence the judge to vote in the preferred direction some of the time, precisely because the economist is committed to revealing the signal generated by the economic analysis. The credibility of academic economic analysis means the signal is informative even to skeptical judges, raising the persuasion probability.

Step 3: Persuasion rate. The paper benchmarks its effect estimates against media-effects studies using the persuasion rate formula from DellaVigna and Gentzkow (2010) (p. 877):

$p = 100 \times \frac{\Delta y}{\Delta e} \cdot \frac{1}{1 - y_0},$

where $\Delta y$ is the change in the outcome, $\Delta e = 1$ (all attenders exposed), and $y_0 = 0.46$ is the pre-treatment mean conservative vote rate in economics cases. Setting $\Delta y = 0.043$ (Table I, col. (11), the most conservative long-run estimate) gives $p \approx 8%$, not that different from the Fox News persuasion rate of 11.6% estimated by DellaVigna and Kaplan (2007) (p. 877).

Method

Economics language measurement. The first outcome measure is constructed using word embeddings (p. 858-860). Judicial opinions are preprocessed (lowercased, punctuation removed) and represented as word lists. The paper uses the word2vec algorithm from Mikolov et al. (2013) to map each word to a dense vector. The starting point for the economics lexicon is the Ellickson (2000) index of 11 economics-related terms used in legal scholarship (externalit*, transaction_cost, efficien*, deterr*, cost_benefit, capital, game_theo, chicago_school, marketplace, law1economic, law2economic). The economics language measure for opinion $i$ is the cosine similarity in word-embedding space between the opinion vector and the Ellickson lexicon vector:

$\text{EconLang}_i = \cos(\text{emb}(\text{opinion}_i),\, \text{emb}(\text{lexicon})),$

where emb(·) maps a document to its embedding-space centroid. The measure is approximately normally distributed and captures contextual use of economics reasoning, not just raw word counts. The paper also constructs an alternative supervised-learning measure (predicting whether an opinion is on an economics topic), which gives consistent results (Online Appendix D).

Identification. The identification strategy exploits the quasi-random timing of Manne program attendance. From 1977 through the late 1980s, the program was oversubscribed, with admission on a first-come, first-served basis. This means that among judges who applied, the specific year of attendance was largely determined by luck of timing rather than by judge characteristics. The paper’s baseline sample restricts to ever-attenders (excludes never-attenders, who differ on pretreatment levels and trends) and exploits within-judge variation in the years before versus after attendance.

Peer-share controls. To address SUTVA concerns (judges communicate within circuits), the paper augments the main specification with a judge-specific peer attendance variable: $\bar{Z}^{-j}_{ct}$ = share of peer judges (weighted by caseload) on the same court who have attended Manne (p. 866-867). This is included interacted with circuit-judge fixed effects, allowing a judge-specific spillover effect.

Empirical specifications

The outcome $Y_{ijct}$ is a decision, vote, or text metric for case $i$ by judge $j$ in court $c$ during year $t$. The baseline difference-in-differences specification is (equation (1), p. 863):

Y_{ijct} = \alpha_j + \alpha_{ct} + \gamma Z^{\text{post}}_{jt} + \mathbf{X}'_{ijct}\beta + \varepsilon_{ijct}, \tag{1}

where $\alpha_j$ is a judge fixed effect, $\alpha_{ct}$ is a court-year fixed effect, $Z^{\text{post}}{jt}$ is an indicator for years after judge $j$ attended the Manne program, $\mathbf{X}{ijct}$ includes optional elastic-net-selected judge characteristics interacted with five-year time effects (predicting timing of attendance), and $\varepsilon_{ijct}$ is an error term clustered by judge. The court-year fixed effect absorbs time-varying court-level confounders including case-mix changes, since cases are quasi-randomly assigned to judges within court-year cells.

The design differs from the standard Callaway and Sant’Anna (2021) staggered DiD in that parallel trends are required in the already-treated group (not only the not-yet-treated), because many judges attend early and restricting to not-yet-treated comparators would cost too much statistical power (p. 862).

The event-study specification replaces the post-indicator with leads and lags (equation (2), p. 863):

Y_{ijct} = \alpha_j + \alpha_{ct} + \sum_{k \in K} \gamma_k Z^k_{jt} + \mathbf{X}'_{ijct}\beta + \varepsilon_{ijct}, \tag{2}

where $Z^k_{jt}$ are indicators for $k$ years before/after Manne attendance ($k \in K$, with the year before attendance and the first year of the window excluded following Borusyak, Jaravel, and Spiess (2024)). The event-study plots for each of the three main outcomes (Figures III, IV, V) show minimal pre-trends and a jump at the time of attendance.

Specific implementations by outcome:

• Economics language (R1-R2): Sample limited to majority-opinion authors on economics cases; circuit court level (N approx 5,261 in the short-run sample). The early-period (pre-1987) subsample isolates the heyday when classes were most oversubscribed (N=3,214).
• Voting against regulators (R3): Binary outcome: whether judge voted against the government in a labor or environmental agency case. Sample: ever-attenders in the event-study window (N=2,408 short run; 4,244 long run).
• Conservative voting (R4-R5): Binary vote direction from the Songer-Auburn database (hand-coded 5% sample of circuit court cases through 2002). Smaller N (N=804 short-run economics cases) due to the limited Songer-Auburn coverage.
• Criminal sentencing (R6-R7): Binary outcome: any prison sentence given. OLS. District court cases; event-study (short-run) sample 1992-2003, N=70,784; long-run sample uses all ever-attenders across all years (1992-2011), N=260,516. Sentence length analyzed separately via Poisson regression; no significant effect found (Table II cols. (3)-(4)), consistent with limited judicial discretion over length under mandatory guidelines in this period.

Observations are weighted so that judge-years count equally (reweighting by judge-year to prevent courts and years with more cases from having disproportionate influence).

Datasets used

Dataset	Role in paper	Wiki page
Bloomberg Law circuit court opinions	~200,000 cases (1970-2005); source of judicial opinions, three-judge panel composition, vote direction, and case topic codes	No page yet
TRAC district court criminal sentencing records	~1 million cases (1992-2011); prison sentence given, sentence length in months, crime type, judge identity; event-study analysis uses 1992-2003 window	No page yet
Federal Judicial Center (FJC) judge biographies	Judge biographical data: appointment party, birth cohort/region, education, career history	No page yet
Manne Program attendance records	Hand-compiled from Butler (1999) and FOIA requests to the LEC at George Mason University; attendance year for each of ~840 ever-attending federal judges, 1976-1998	No page yet
Songer-Auburn database	Hand-coded 5% sample of circuit court cases through 2002; liberal/conservative/neutral vote coding for 5% of cases	No page yet

Sample scope: circuit courts 1970-2005 (economics language and regulatory voting); district courts 1992-2011 (criminal sentencing; event-study window 1992-2003). Judge-year is the effective unit of observation. Observations weighted to treat judge-years equally.

When to read the full paper

Use the original if you are:

• Studying how ideas and intellectual frameworks influence policy decisions;
• Interested in the history and effects of the law and economics movement on the U.S. judiciary;
• Replicating (the Online Appendix covers the complete robustness battery including never-attenders, alternative clustering, unweighted regressions, and SUTVA checks);
• Extending the analysis to other ideological training programs, other policy domains, or other countries with analogous judge training programs;
• Benchmarking text-as-data measures of ideological style against supervised alternatives.
• Extending the decision-quality evidence from Baye and Wright (2011), who show Manne-trained judges are less likely to have antitrust decisions reversed on appeal, to the full breadth of judicial outcomes studied here.

Attribution and rights

Source: peer-reviewed, The Quarterly Journal of Economics (2026), pp. 845-887. This distillation was extracted by an LLM on 2026-06-28 and is not human-verified or independently reproduced.

The paper is distributed under the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0). Commercial re-use requires permission from the publisher.

Attribution (CC BY-NC 4.0). Ash, Elliott, Daniel L. Chen, and Suresh Naidu. “Ideas Have Consequences: The Impact of Law and Economics on American Justice.” The Quarterly Journal of Economics (2026), 845-887. DOI: 10.1093/qje/qjaf042. © 2025 The Author(s). Published by Oxford University Press on behalf of President and Fellows of Harvard College. Licensed under CC BY-NC 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.