Narratives about the Macroeconomy

Layer 1 — Overview

Research Question

This paper investigates two related empirical questions in the context of the historic surge in US inflation in late 2021 and 2022: (1) What narratives—causal stories—do people invoke to explain why inflation increased? (2) How do those narratives shape economic expectations? A companion theoretical component asks how narrative heterogeneity affects aggregate macroeconomic outcomes.

Data and Methodology

The authors recruit more than 10,000 US households across five descriptive survey waves (November 2021, December 2021, January 2022, March 2022, May 2022) via Lucid, plus a separate expert survey of 111 academic economists with JEL-E publications in top journals, recruited simultaneously with the November 2021 household wave. Household samples are broadly representative of the US population in terms of gender, age, region, and income. The expert sample is highly credentialed: on average 18.6 years post-PhD, 2.7 top-five publications, and 5,534 Google Scholar citations.

Narratives are elicited through open-ended questions asking respondents to explain in their own words why inflation increased. Each text response is coded by two independent, blinded research assistants as a Directed Acyclic Graph (DAG) — a network of causal nodes representing factors (demand-side: government spending, monetary policy, pent-up demand, demand shift; supply-side: supply chain disruptions, labor shortage, energy crisis; miscellaneous: pandemic, government mismanagement, price gouging, Russia-Ukraine war) connected by directed causal edges. Inter-rater reliability is high: if one coder identifies a factor, the other does so 88% of the time; for specific causal connections between factors, agreement is 77%.

Three experiments study the causal effect of narratives on expectations: (1) A pent-up demand vs. energy crisis narrative provision experiment (April 2022, n=2,397 baseline, n=1,329 follow-up); (2) A monetary policy vs. energy crisis narrative provision experiment (June 2022, n=1,069 baseline, n=736 follow-up); (3) A 2×2 belief-updating experiment crossing narrative type (government spending vs. energy crisis) with information type (low vs. high government spending forecast) (April 2022, n=997).

Main Findings with Quantitative Magnitudes

Households’ narratives are substantially coarser than experts’: expert DAGs contain on average 4.3 factors and 3.6 causal links, while household DAGs contain only 3.5 factors and 2.8 links (both differences p < 0.01). Households focus predominantly on supply-side explanations: 57% invoke at least one supply-side factor vs. only 32% invoking any demand-side factor. The most common household narrative factors are supply chain disruptions (30%), labor shortage (27%), and general supply-side factors (22%); the leading demand-side factor is government spending, appearing in only 17% of household narratives, while loose monetary policy appears in just 5%. By contrast, 90% of experts invoke at least one supply-side factor and 84% at least one demand-side factor, with government spending mentioned by 50% of experts and monetary policy by 38%.

Among households who invoke at least one supply or demand narrative, only 34% mention both supply and demand factors; among the corresponding subsample of experts, 77% mention both. Government mismanagement—a politicized judgment of policy failure—appears in 32% of household narratives but only 1% of expert narratives. Price gouging appears in 8% of household narratives and 0% among experts.

Partisan polarization is large: Democrat-leaning respondents are 26 pp more likely to attribute inflation to the pandemic as a root cause (p < 0.01); Republican-leaning respondents are 38 pp more likely to blame government mismanagement (p < 0.01), and 19 pp more likely to mention high government spending (p < 0.01) and 14 pp more likely to mention high energy prices (p < 0.01).

Narratives are correlated with inflation expectations in OLS regressions controlling for demographics and survey wave fixed effects (n=2,951): households invoking government mismanagement predict 1.155 pp higher 1-year-ahead inflation (p < 0.01) and 0.805 pp higher 5-year-ahead inflation (p < 0.01). Energy crisis narratives predict 0.661 pp higher 1-year-ahead inflation (p < 0.01). Pent-up demand narratives predict 0.640 pp lower 5-year-ahead inflation (p < 0.05). Narrative variables explain approximately 10% of the out-of-sample variation in 1-year-ahead inflation expectations via LASSO, comparable to or exceeding the explanatory power of demographics and inflation experiences found in prior work.

In Experiment 1 (pent-up demand vs. energy crisis), providing the pent-up demand narrative reduces 12-month inflation expectations by 0.71 pp relative to the energy crisis treatment (p < 0.01, in the main survey), corresponding to 24% of a standard deviation. This effect persists in the follow-up survey one day later (−0.63 pp, p < 0.01).

In Experiment 2 (monetary policy vs. energy crisis), the monetary policy narrative reduces 12-month inflation expectations by 0.40 pp at the time of the main survey (p < 0.01) and by 0.62 pp in the follow-up (p < 0.01).

In Experiment 3 (information updating), respondents exposed to the government spending narrative increase 12-month inflation expectations by 1.79 pp in response to a high-spending forecast (p < 0.01), while those exposed to the energy crisis narrative show no significant reaction (0.34 pp, p = 0.205). In IV regressions instrumenting government spending expectations with the high/low forecast treatment, a 1 pp increase in perceived government spending growth raises inflation expectations by 0.378 pp among those holding the government spending narrative (p < 0.01) versus only 0.051 pp among those holding the energy narrative (p = 0.184; difference p < 0.01).

The New Keynesian DSGE model shows that a modest shift in perceived importance of monetary policy relative to productivity (raising ω_ν from 0.1 to 0.2, holding ω_g fixed) raises equilibrium consumption by 27 basis points and reduces equilibrium inflation by 27 basis points in the calibrated model with φ = 1.5; with a less reactive central bank (φ = 1.25), the same shift raises consumption by 30 basis points and reduces inflation by 62 basis points.

Scope Conditions

All empirical results are drawn from the US context during the 2021–2022 inflation surge. The authors note that the extent of partisan polarization in US narratives may not generalize to less politically polarized countries. The test-retest correlation of narrative factors across a three-day interval is 0.63 (p < 0.01), indicating significant but not perfect stability. The experiment results may partly reflect that narratives were especially malleable because the inflation surge was a relatively recent and salient phenomenon at the time of data collection.

Layer 2 — Q&A

Q1: How do the authors define and operationalize “narratives”?

A: The paper defines economic narratives as causal accounts for why an economic event occurred — agents’ assessments of cause-effect relationships across events. Each text response is coded as a Directed Acyclic Graph (DAG) where nodes are economic factors and directed edges represent perceived causal links. DAGs can represent both simple mono-causal accounts and complex multi-factor chains. The authors use a predefined coding scheme of 16+ factor categories spanning demand-side, supply-side, and miscellaneous nodes, with inflation as the terminal node.

Q2: What is the inter-rater reliability of the DAG coding, and what does it imply for the quality of the narrative data?

A: Two independent, blinded coders annotate each response. If one coder assigns a given factor, the other does so 88% of the time; for specific causal connections between factors, agreement is 77%. Approximately 95% of assigned factors and 89% of assigned connections make it to the final coded version. At the coarser level of “any demand-side factor,” agreement rises to 94%; for “any supply-side factor,” to 93%. Test-retest reliability across a three-day interval averages a correlation of 0.63 across all narrative factors (p < 0.01), comparable in magnitude to the measured persistence of economic preferences in prior work.

Q3: How do expert and household narratives differ in their structural complexity?

A: Expert DAGs contain on average 4.3 factors and 3.6 causal links, compared to 3.5 factors and 2.8 links for households (both p < 0.01). These differences persist even after controlling for response time and word count, indicating genuine differences in economic understanding rather than effort. Among agents who invoke at least one supply or demand factor, 77% of experts mention both, compared to only 34% of households.

Q4: What are the most prevalent factors in household narratives versus expert narratives, and why does this matter?

A: Supply chain disruptions (30%), labor shortage (27%), and general supply-side factors (22%) top household narratives, while monetary policy appears in only 5% of household DAGs. Expert narratives are more balanced: 90% cite supply-side factors and 84% cite demand-side factors, with government spending mentioned by 50% and monetary policy by 38%. This matters because factors with different persistence imply different trajectories for future inflation; households’ supply-side emphasis, combined with low awareness of monetary policy, shapes their inflation expectations in systematically different ways than experts.

Q5: What is the structure of household narrative clusters, and how fragmented are they?

A: Agglomerative hierarchical clustering using the Jaccard distance between DAG edge lists reveals 15 optimal clusters (Silhouette criterion), of which eight have at least 30 members. Four supply-side clusters account for 55% of households: pandemic-related supply chain disruptions (20%), general supply-side causes (18%), energy crisis often attributed to government mismanagement (11%), and labor shortages attributed to the pandemic or government spending (7%). The only clear demand-side cluster—combining government spending and loose monetary policy—captures just 8%. Simple mono-causal clusters attributing inflation to the pandemic alone (15%), government mismanagement alone (11%), and price gouging alone (4%) are collectively prominent, underscoring how fragmented and often single-factor household reasoning is.

Q6: How do partisan affiliations correlate with narrative content?

A: Republicans are 38 pp more likely than Democrats to attribute inflation to government mismanagement (p < 0.01), 19 pp more likely to mention high government spending (p < 0.01), and 14 pp more likely to mention high energy prices (p < 0.01). Democrats are 26 pp more likely to cite the pandemic as a root cause of inflation (p < 0.01) and more frequently cite pandemic-related supply chain issues and corporate greed. Government mismanagement appears in 32% of all household narratives (and is often portrayed as a root cause of spending, monetary policy, and energy prices) but in only 1% of expert narratives.

Q7: How did the composition of household narratives shift over time (November 2021 to May 2022)?

A: The energy crisis narrative rose sharply from 12% in January 2022 to 28% in March 2022, coinciding with Russia’s invasion of Ukraine in late February 2022. The Russia-Ukraine war narrative went from virtually zero before February 2022 to 28% in March 2022. By contrast, pandemic references, which climbed from 44% in November 2021 to 55% in January 2022, fell back to 47% in March 2022 and 39% in May 2022. Labor shortage references fell sharply from 32% in January 2022 to 15% in May 2022. These abrupt shifts suggest household narratives respond to major news events and, by extension, could drive rapid revisions in inflation expectations around such events.

Q8: What is the correlational evidence that narratives predict inflation expectations, and how large is the explanatory power?

A: OLS regressions on pooled data from November 2021–January 2022 (n=2,951), controlling for survey wave fixed effects and sociodemographics, show: government mismanagement narratives predict 1.155 pp higher 1-year inflation expectations (p < 0.01) and 0.805 pp higher 5-year expectations (p < 0.01); energy crisis narratives predict 0.661 pp higher 1-year expectations (p < 0.01); monetary policy narratives predict 1.005 pp higher 1-year expectations (p < 0.01); pent-up demand narratives predict 0.640 pp lower 5-year expectations (p < 0.05). LASSO out-of-sample prediction using DAG factor dummies and connection dummies explains approximately 10% of variation in 1-year-ahead inflation expectations — comparable to the 10% within-sample R² found by D’Acunto et al. (2021) for grocery price exposure, and substantially above the 2–7% found by Giglio et al. (2021) for investor characteristics explaining stock return expectations.

Q9: What does Experiment 1 (pent-up demand vs. energy crisis) show about the causal effect of narratives?

A: Providing the pent-up demand narrative (relative to the energy crisis narrative) increases the fraction of respondents invoking pent-up demand by 37.8 pp in the follow-up survey (baseline: 2.8%, p < 0.01) and reduces the fraction invoking the energy crisis by 7.9 pp (p < 0.01), establishing successful first-stage uptake. In the main survey (n=2,397), the pent-up demand treatment reduces 12-month inflation expectations by 0.71 pp relative to the energy treatment (p < 0.01), equivalent to 24% of a standard deviation; the effect persists at −0.63 pp in the follow-up one day later (p < 0.01). The energy crisis treatment has no significant effect on expectations relative to a pure control (−0.02 pp, p = 0.911), suggesting that energy crisis implications were already salient at the time.

Q10: What does Experiment 2 (monetary policy vs. energy crisis) add, given it was conducted after significant Fed tightening?

A: The experiment was run in June 2022, when 61% of respondents were already aware the Fed had raised rates. The monetary policy narrative increases the fraction invoking monetary policy by 39 pp and reduces the energy fraction by 50 pp relative to the energy group (both p < 0.01). The monetary policy narrative reduces 12-month inflation expectations by 0.40 pp in the main survey (p < 0.01) and 0.62 pp in the follow-up (p < 0.01). The mechanism is that attributing past inflation to loose monetary policy — which has since been tightened — leads respondents to infer lower future inflation, consistent with the narrative about persistence of the underlying cause.

Q11: What does Experiment 3 demonstrate about how narratives filter the interpretation of new information?

A: In the 2×2 design, all respondents first receive either a government spending narrative or an energy crisis narrative, then either a low (−4%) or high (+6%) government spending forecast from the Survey of Professional Forecasters. Among those with the government spending narrative, the high-spending forecast raises 12-month inflation expectations by 1.79 pp (p < 0.01); among those with the energy crisis narrative, the high-spending forecast raises inflation expectations by a non-significant 0.34 pp (p = 0.205). The IV estimate shows that a 1 pp increase in expected government spending growth raises inflation expectations by 0.378 pp for those holding the spending narrative (p < 0.01) vs. 0.051 pp for those holding the energy narrative (p = 0.184); this difference is highly significant (p < 0.01). Importantly, the first-stage effect on expected government spending growth is similar across narrative groups (4.7 pp vs. 6.8 pp, difference not significant), ruling out differential interpretation of the forecast itself as the mechanism.

Q12: How do the authors formalize narratives in the DSGE model, and what is the key mapping result?

A: Narratives are formalized as subjective causal models (SCMs): linear mappings from N observable factors to inflation, π_t = ψ_1(i)z_{1,t} + … + ψ_N(i)z_{N,t}, combined with perceived AR(1) processes for each factor. The “subjective inflation narrative” of agent i is summarized by perceived contribution shares ω_z(i). The paper’s Proposition 2 gives closed-form expressions for equilibrium inflation and consumption as functions of these perceived shares, without imposing that they be correct or identical across agents. The key result is that subjective causal models always affect equilibrium outcomes so long as the perceived persistence parameters differ across factors — the mechanism being that different narratives produce different inflation expectations, which feed back into consumption and pricing decisions.

Q13: What are the quantitative implications of narrative shifts in the calibrated DSGE model?

A: The baseline calibration uses standard New Keynesian parameters (β=0.99, γ=1, ς=5, Calvo price duration=4 quarters, φ=1.5, ρ_a=0.9, ρ_g=0.8, ρ_ν=0.5) with a scenario of a 10% productivity decline, 10% government spending increase, and policy rate 2 pp below the Taylor rule. Under rational expectations, π_t=3.68% and c_t=−11.79%. Raising the perceived importance of monetary policy in household and firm inflation narratives from ω_ν=0.1 to ω_ν=0.2 (lowering ω_a by the same amount, holding ω_g fixed) increases equilibrium consumption by 27 basis points and reduces equilibrium inflation by 27 basis points. With a less reactive central bank (φ=1.25), the same narrative shift raises consumption by 30 basis points and reduces inflation by 62 basis points. The paper notes that these effects are approximately linear in the narrative shift, meaning the directional implication holds across a wide range of narrative configurations.

Q14: How does narrative heterogeneity across households affect aggregate outcomes in the model?

A: When households hold heterogeneous narratives, aggregate outcomes depend on the joint distribution of perceived factor importance (ω_z(i)) and perceived factor persistence (ρ_z(i)) across agents, rather than on average values alone. Specifically, the model shows that if households who assign higher importance to a given factor also perceive that factor as more persistent, the aggregate effect on expectations and consumption is amplified beyond what the average narrative predicts. Additionally, narrative heterogeneity generates consumption heterogeneity even when the efficient allocation requires all households to consume the same amount, representing a welfare-relevant distortion absent under rational expectations.

Q15: What is the practical implication for central bank communication?

A: Under full-information rational expectations, central bank narrative communication about the drivers of inflation is irrelevant because agents already hold the correct model. Once subjective causal models can deviate from the truth, central bank narrative provision shifts aggregate equilibrium outcomes (inflation and consumption) in a benchmark New Keynesian model. The paper argues that central banks need to measure the distribution of household narratives to know whether their communication shifts agents toward or away from the rational expectations equilibrium — moving agents in the direction of the correct narrative produces better aggregate outcomes from the central bank’s perspective, conditional on inflation being above target and output below first-best.

Key Concepts

Economic Narrative (as used in this paper): An agent’s causal account for why a given economic event occurred — specifically, an assessment of cause-effect relationships that explains the drivers of an economic outcome. Distinguished from more general notions of “story” in that causality is the core; the paper does not count descriptions of correlation or simple statements of fact as narratives.

Directed Acyclic Graph (DAG) representation of narratives: Each narrative is coded as a network of factor nodes connected by directed edges indicating perceived causation. Acyclicity rules out feedback loops in a respondent’s causal account. Factors with nonzero ψ(i) are included; the direction of edges indicates causal flow. This representation allows quantitative comparison across respondents via adjacency matrices or Jaccard distances between edge lists.

Subjective Causal Model (SCM) of inflation: The paper’s formal theoretical counterpart to a narrative: a linear mapping π_t = Σ_n ψ_n(i) z_{n,t} in which individual i assigns perceived marginal effect ψ_n(i) to each factor z_n, combined with a perceived AR(1) law of motion for each factor. The SCM does not need to be correct or shared across agents. The rational expectations equilibrium is the special case where all agents’ SCMs match the true data-generating process.

Perceived contribution share (ω_z): The ratio ψ_z(i)·z_t / π_t — agent i’s perceived percentage contribution of factor z to current inflation. This is the sufficient statistic for the effect of household narratives on inflation expectations and, through the NK model, on equilibrium aggregate outcomes. The aggregate distribution of ω_z(i) and perceived persistence ρ_z(i) determines the consumption Euler equation at the aggregate level.

Government mismanagement (as a narrative factor): A coding category that captures explicit reference to policy failure or low-quality decision-making by policymakers in a politicized sense — distinct from the economic factors of government spending or monetary policy. It represents households’ attribution of inflation to the incompetence or malfeasance of officials, rather than to any specific economic mechanism. This factor appears in 32% of household narratives but only 1% of expert narratives.

Narrative cluster: A group of respondents whose DAGs are mutually similar (measured by Jaccard distance between edge lists) and whose typical DAG differs from other clusters. Identified via agglomerative hierarchical clustering. The paper identifies eight substantively meaningful clusters, ranging from supply-chain-focused to mono-causal pandemic or mismanagement narratives, with no single cluster capturing more than 20% of households.

Test-retest reliability of narratives: The correlation between the same respondent’s narrative elicited on two occasions three days apart. The paper estimates an average correlation of 0.63 across all narrative factors (p < 0.01), interpreted as indicating significant stability in households’ causal beliefs rather than survey noise. Comparable in magnitude to test-retest correlations of economic preferences in other studies.