Fear of full employment

Labor and inflation at the Fed

Benjamin Braun

London School of Economics

Jérôme Deyris

Sciences Po

Monica DiLeo

Hertie School

Motivation

Endless debates about what actually drives inflation

Two “teams” during the 2020s Covid inflation (Bernanke & Blanchard, 2025; Stiglitz, 2023; Weber et al., 2024)
New explanations of the 1970s Great inflation (Alvarez et al., 2022; Drechsler et al., 2022; Goutsmedt, 2021; Judge, 2023)

Controversies, even decades later!

An unavoidable question

Yet, this question cannot be avoided by central bankers

If inflation is driven by demand-side factors, monetary policy has to step in, increasing rates to curb demand and restore price stability
If inflation is driven by supply-side factors, central bankers can adopt a wait and see approach as long as expectations remain anchored

While inflation’s true causes may be unobservable, the factors viewed by central bankers as driving inflation matter and can be observed

Research objectives

What drives inflation according to central bankers?

Which inflation drivers are the most prominent in their speeches and deliberations? How have these evolved over time?
To what extent are central bankers’ inflation diagnoses in line with economic fundamentals?
Do other political, institutional, and individual variables also matter?

A caveat

One ongoing research project, but two papers

Interests and prices

Descriptive exploration of all inflation drivers and their evolution in time
Analysis of within committee heterogeneity

Fear of full employment

Zoom on the Labor-based explanations of inflation
Analysis of its economic, institutional and political drivers over time

Today’s agenda

A new method for detecting causal claims about inflation with large language models (LLMs)

Descriptive results: Fifty years of inflation diagnosis

Zooming on Labor: when does the Fed blame labor for price increases?

Conclusion and avenues for future research

Detecting causal claims about inflation

Data - Sources

We pick the Fed for its global relevance and transparency

Public speeches (1970-2023):

Campiglio et al. (2025) + FRASER archives
- 8,253 public speeches (1970-2023)
- Long single speaker texts (~ 3,100 words)
- 1.1M sentences, 24M words

Private deliberations (1976-2019):

Web-scraped all FOMC meetings (Fed website)
- 477 verbatim transcripts (1976-2019)
- parsed into 123,039 interventions
- 0.5M sentences, 12M words

Data - inflation discussions

Identify 129,457 inflation-related sentences
- Sentence is flagged if it contains 1 inflation or price related word (list of keywords)
Create 60,150 inflation paragraphs
- (Semi-)consecutive inflation sentences are merged, and the preceding and following sentences are added (see appendix)

Causal claims about inflation

Our goal is to detect causal claims about inflation

Statements explicitly or implicitly suggesting a causal relationship between a variable and the general level of prices.

“on the inflation number […] one of the major factors is the effect of the structural deficit plus the projected decline of the dollar.”

“The unemployment rate was 4-1/2 percent… This suggests that we need to be vigilant for the possibility of increases in inflationary pressures.”

Traditional NLP methods

How to detect causal claims in a vast corpus of text?

Manual coding:
- Time and cost prohibitive
- Difficult to ensure consistency across coders
Traditional NLP (topic modeling, dictionary methods):
- Can process large corpora efficiently
- Struggles to identify causal relationships and nuanced claims (based on co-occurences)

Large language models

Recent papers show large language models (LLMs) can be powerful tools for text classification

On generic tasks (Cova, 2025; Do et al., 2022; Törnberg, 2024)
Specifically on conceptual relationships in text (Garg & Fetzer, 2025)

With a few caveats:

Concerns about their prompt sensitivity and stochastic nature, leading to potential bias in measurement and reproducibility issues

Demands a careful human validation process

Methods - Step 1: the codebook

Develop a codebook

300 excerpts manually coded by all three authors to
- Get a first sense of inflation paragraphs
- Identify the most frequent inflation drivers

Validation sample

800 excerpts manually coded by all three authors to
- Compute the inter-coder reliability
- Get a human benchmark (“gold standard”)

Test LLMs

12 models are tested, and compared to human benchmark
- Given the same prompt, and paragraphs
- Select the best model (F1 scores, precision & recall)

Six drivers of inflation

Category	Description
Labor	Wage-price spirals, employment dynamics, unions
Market Power	Corporate pricing behavior, competition, profits
Fiscal	Government spending, budgetary deficits
Energy	Oil, gas, electricity prices
Commodities	Food, raw materials
Exchange Rate	Currency fluctuations ; dollar strength (weakness)
Other	Supply chain disruptions, credit dynamics, etc.

Note: We code both explicit and implicit causal claims, capturing upward and downward price pressures

→ see examples

Validation sample

Develop a codebook

300 excerpts manually coded by all three authors to
- Get a first sense of inflation paragraphs
- Identify the most frequent inflation drivers

Validation sample

800 excerpts manually coded by all three authors to
- Compute the inter-coder reliability
- Get a human benchmark (“gold standard”)

Test LLMs

12 models are tested, and compared to human benchmark
- Given the same prompt, and paragraphs
- Select the best model (F1 scores, precision & recall)

Validation sample

Develop a codebook

300 excerpts manually coded by all three authors to
- Get a first sense of inflation paragraphs
- Identify the most frequent inflation drivers

Validation sample

800 excerpts manually coded by all three authors to
- Compute the inter-coder reliability
- Get a human benchmark (“gold standard”)

Model selection

Develop a codebook

300 excerpts manually coded by all three authors to
- Get a first sense of inflation paragraphs
- Identify the most frequent inflation drivers

Validation sample

800 excerpts manually coded by all three authors to
- Compute the inter-coder reliability
- Get a human benchmark (“gold standard”)

Test LLMs

12 models are tested, and compared to human benchmark
- Given the same prompt, and paragraphs
- Select the best model (F1 scores, precision & recall)

Final model run

We select o4-mini that performs best, with good (but heterogenous) F1-scores
We feed our full corpus, one excerpt at a time to OpenAI’s API
We obtain times series of causal claims about inflation (1970-2023)

Descriptive results

Most prominent inflation drivers

Labor is the most frequent explanation, closely followed by Energy.

Fiscal, Commodities, and Exchange rate are mentioned more than twice as infrequently.

Market power is by far the least discussed inflation driver.

Inflation drivers in time

Inflation in public VS in private

Zooming on Labor

What drives Labor blame?

First, two classic economic theories associate labor market developments with inflation

Philips curve: lower unemployment is associated with increased wage growth, and hence prices (Phillips, 1958)
NAIRU: Below the “natural rate of unemployment”, inflation accelerates as wage pressures build and expectations shift upward. (Friedman, 1968; Phelps, 1967)

Provide a useful guide for central bankers, to preemptively raise interest rates when labor markets tighten

H1. Employment and wages

Prominent theories since the beginning our period (Arbogast et al., 2023; Blinder, 2022) despite internal criticism (Tarullo, 2017; Yellen, 2019)

H1: Central bankers’ Labor blame should be higher when unemployment is low, especially below the NAIRU, and when wage growth is high

A strategic communication

Beyond economic fundamentals, central bankers’ attribution of inflation to Labor may be influenced by strategic considerations

Despite (because of) their independence, central banks have to carefully manage their reputation (Moschella, 2024)
Discrepancies between central bankers’ beliefs and their public communication (Braun et al., 2024; Deyris et al., 2025; Diessner, 2023)

There may be a gap between internal and external inflation diagnoses

H2. Reputation management

Blaming the labor market for inflation may come with reputational costs (Best, 2020), especially considering the Fed’s dual mandate (Galbraith et al., 2007; Goutsmedt, 2022)

H2: Central bankers’ Labor blame should be higher / more responsive to economic fundamentals in their private deliberations than in their public speeches

A partisan fear of labor induced inflation?

Last, electoral politics may also play a role in how central bankers respond to labor-related data

Left-wing governments were associated with high employment-high inflation outcomes (Bartels, 2016; Blinder & Watson, 2016; Hibbs, 1977; Kalecki, 1943)
The Fed has been shown to exhibit a partisan bias against Democrats (Clark & Arel-Bundock, 2012; Cusack, 2001; Galbraith et al., 2007)

This may make the Fed’s Labor blame conditional on who is in power

H3. Electoral politics

Given the constituencies and preferences of Left incumbents to run the economy hot, and the partisan bias of the Fed,

H3: Central bankers’ Labor blame should be higher / more responsive to economic fundamentals under Democrat incumbents than when Republicans parties are in power

Testing H1

Low unemployment, high Labor blame

We hypothesized that Fed officials blame more labor when unemployment is low and wage growth high (H1).

A simple scatterplot reveals a Rhetorical Philips Curve

Formally testing H1

This relationship seems strong, but could be caused by other confounding factors (e.g. more communication in years with more inflation)

→ Let’s test this relationship in a formal regression setting

Summary statistics for Labor mentions (DV)
Mean	Median	Min	Max	Variance
31.4	25	3	117	478.45

Since our DV is count data, and appears overdispersed (variance > mean), we opt for negative binomial specifications

Model specifications

Negative binomial regression:

\[ \begin{align} \log(E[Y_{t}]) = \beta_0 &+ \color{#2E6C7B}{\beta_1 U_{t} + \beta_2 U^2_{t} + \beta_3 N_t} \\ &+ \color{#E69F00}{\beta_4 \pi_t} \\ &+ \color{#009E73}{ \beta_5 W_{t} + \alpha_i} \\ &+ \color{purple}{\beta_4 \pi_t + \beta_6 R_t + \alpha_i} \\ &+ \color{#CC79A7}{\beta_4 \pi_t + \beta_6 R_t + \beta_7 G_t + \alpha_i} \\ &+ \epsilon_t \end{align} \]

Progressive specification:

Model 1: Quadratic specification
Model 2: Controlling for inflation
Model 3: Nominal wage growth + Chair dummies
Model 4: Real wage growth + Chair dummies
Model 5: GDP growth + Chair dummies

Variables:

\(Y_t\) = count of labor mentions in quarter \(t\)
\(U_t\) = unemployment (Greenbook, centered)
\(N_t\) = quarterly thousand words
\(\pi_t\) = inflation (Greenbook, centered)
\(W_t\) = nominal wage growth (AHE, centered)
\(R_t\) = real wage growth (AHE, centered)
\(G_t\) = GDP growth (Greenbook, centered)
\(\alpha_i\) = Fed Chair dummies

Model results

	(1)	(2)	(3)	(4)	(5)
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
Unemployment	-0.270***	-0.272***	-0.232***	-0.244***	-0.229***
	(0.029)	(0.030)	(0.040)	(0.037)	(0.036)
Unemployment²	0.060***	0.060***	0.069***	0.072***	0.067***
	(0.013)	(0.013)	(0.014)	(0.014)	(0.014)
Inflation		0.004		0.019	0.067*
		(0.021)		(0.028)	(0.029)
Real wage growth (t-1)				-0.005	-0.002
				(0.021)	(0.020)
Nominal wage growth (t-1)			0.023
			(0.032)
GDP growth					0.081***
					(0.022)
Total words (in thousands)	0.004***	0.004***	0.003**	0.003**	0.002**
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Num.Obs.	167	167	166	166	166
AIC	1358.2	1360.1	1337.4	1339.2	1328.4
BIC	1373.7	1378.8	1371.6	1376.6	1368.8
Chair dummies			✓	✓	✓

The missing transmission channel

Fed officials Labor blaming is reactive to employment :

When unemployment decreases, labor blame increases: a fall in 1pp unemployment increase of 25% of labor blame
This relationship is quadratic, leading to accelerating blame around the NAIRU: a further 1pp blame => 43% increase
It is robust to macroeconomic controls

However, wage growth appears insignificant in all specifications

The missing transmission channel

A “baseless fear of full employment”

This suggests that Fed officials are responsive to employment, irrespective of its actual contribution to (wage) inflation

Fed own research shows a fading link between employment and wage inflation (Ratner & Sim, 2022)
But old habits die hard (Tarullo, 2017)

Over the long run, the Fed may indeed have cultivated a “baseless fear of full employment” (Galbraith et al., 2007)

A publicly hidden fear?

Reputation management

Again, a simple scatterplot suggests strategic communication

Model specifications

Negative binomial regression: \[ \begin{align} \log(E[Y_{t}]) = \beta_0 &+ \beta_1 U_{t} + \beta_2 U^2_{t} + \beta_3 \pi_t + \beta_4 N_t + \alpha_i \\ &+ \color{#E69F00}{\beta_5 FOMC_t} \\ &+ \color{#CC79A7}{\beta_6 (U_t \times FOMC_t) + \beta_7 (U^2_t \times FOMC_t)} \\ &+ \color{#009E73}{\beta_8 W_t} \\ &+ \color{purple}{\beta_3 \pi_t + \beta_9 R_t + \beta_{10} G_t} \\ &+ \epsilon_t \end{align} \] Progressive specification:

Model 6: Model 2 + FOMC dummy + Chair
Model 7: + FOMC interactions (U×FOMC, U²×FOMC)
Model 8: + Economic controls (nom. wage growth)
Model 9: + Economic controls (real wage growth, inflation, real GDP growth)

Variables:

\(Y_t\) = count of labor mentions in quarter \(t\)
\(U_t\) = unemployment (Greenbook forecast)
\(\pi_t\) = inflation (Greenbook, centered)
\(N_t\) = total quarterly words (in 1000s)
\(FOMC_t\) = Forum (1 = FOMC, 0 = Speech)
\(W_t\) = nominal wage growth (AHE, centered)
\(R_t\) = real wage growth (AHE, centered)
\(G_t\) = GDP growth (Greenbook, centered)
\(\alpha_i\) = Fed Chair dummies

Model results

	(6)	(7)	(8)	(9)
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
Unemployment (GB)	-0.236***	-0.151***	-0.133**	-0.131**
	(0.034)	(0.041)	(0.043)	(0.040)
Unemployment^2 (GB)	0.073***	0.070***	0.065***	0.063***
	(0.013)	(0.017)	(0.017)	(0.016)
Forum (FOMC=1)	0.157+	0.168	0.170	0.150
	(0.094)	(0.112)	(0.112)	(0.109)
Unemp*Forum		-0.166***	-0.167***	-0.175***
		(0.048)	(0.048)	(0.047)
Unemp^2*Forum		-0.001	-0.001	-0.003
		(0.022)	(0.022)	(0.021)
Inflation	0.024	0.022		0.076**
	(0.024)	(0.024)		(0.027)
Real wage growth (t-1)				0.006
				(0.019)
Nominal wage growth (t-1)			0.037
			(0.030)
GDP growth				0.092***
				(0.020)
Total words (in thousands)	0.004***	0.005***	0.005***	0.004***
	(0.001)	(0.001)	(0.001)	(0.001)
Num.Obs.	334	334	333	333
AIC	2362.0	2351.2	2343.6	2328.6
BIC	2407.7	2404.6	2396.9	2389.5
Chair dummies	✓	✓	✓	✓

Marginal effects (Model 9)

Interpretation

The fear of full employment is stronger in private than in public (H2):

The interaction between Unemployment and the FOMC variable is highly significant and negative:
- Fed officials are more responsive to unemployment privately than publicly
The base effect is positive but not significant

What about politics?

Democrats in power

A simple scatterplot is harder to read

Model specifications

Negative binomial regression: \[ \begin{align} \log(E[Y_{t}]) = \beta_0 &+ \beta_1 U_{t} + \beta_2 U^2_{t} + \beta_3 \pi_t + \beta_4 N_t + \alpha_i \\ &+ \color{#E69F00}{\beta_5 Pres_t} \\ &+ \color{#CC79A7}{\beta_6 (U_t \times Pres_t) + \beta_7 (U^2_t \times Pres_t)} \\ &+ \color{#009E73}{\beta_8 W_t} \\ &+ \color{purple}{\beta_3 \pi_t + \beta_9 R_t + \beta_{10} G_t} \\ &+ \epsilon_t \end{align} \] Progressive specification:

Model 10: Model 2 + President (Dem=1) + Chair
Model 11: + President interactions (U×Pres, U²×Pres)
Model 12: + Economic controls (nom. wage growth)
Model 13: + Economic controls (real wage growth, inflation, real GDP growth)

Variables:

\(Y_t\) = count of labor mentions in quarter \(t\)
\(U_t\) = unemployment (Greenbook forecast)
\(\pi_t\) = inflation (Greenbook, centered)
\(N_t\) = total quarterly words (in 1000s)
\(Pres_t\) = President (1 = Democrat, 0 = Republican)
\(W_t\) = nominal wage growth (AHE, centered)
\(R_t\) = real wage growth (AHE, centered)
\(G_t\) = GDP growth (Greenbook, centered)
\(\alpha_i\) = Fed Chair dummies

Model results

	(10)	(11)	(12)	(13)
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
Unemployment (GB)	-0.267***	-0.220***	-0.231***	-0.205***
	(0.035)	(0.062)	(0.063)	(0.059)
Unemployment^2 (GB)	0.067***	0.069***	0.070***	0.066***
	(0.013)	(0.019)	(0.019)	(0.018)
President (Dem=1)	0.383***	0.402***	0.418***	0.444***
	(0.083)	(0.116)	(0.114)	(0.109)
Unemp*President		-0.065	-0.049	-0.073
		(0.081)	(0.078)	(0.077)
Unemp^2*President		-0.015	-0.019	-0.020
		(0.028)	(0.027)	(0.026)
Inflation	0.018	0.021		0.066*
	(0.025)	(0.026)		(0.028)
Real wage growth (t-1)				-0.013
				(0.019)
Nominal wage growth (t-1)			0.001
			(0.031)
GDP growth				0.091***
				(0.020)
Total words (in thousands)	0.002**	0.002**	0.002*	0.002*
	(0.001)	(0.001)	(0.001)	(0.001)
Num.Obs.	167	167	166	166
AIC	1330.3	1332.5	1321.8	1305.7
BIC	1367.8	1376.2	1365.4	1355.5
Chair dummies	✓	✓	✓	✓

Marginal effects (Model 13)

Interpretation

The fear of full employment is stronger under Democratic Presidential administrations (H3):

The main effects for Presidential party are highly significant:
- at any level of unemployment, Fed officials mention labor as a cause of inflation at higher frequency under Democrats
But not the interaction between Presidential Party and Unemployment:
- The slope / responsiveness is similar

Conclusion

Main results (I)

Big textual data in archives and LLMs provide new avenues to study central banks, their ideas and how they evolve over time.
- Despite periods of doubts in the 1990s and 2010s, labor based explanations of inflation are still dominant
Labor blame is positively associated with employment, creating a rhetorical Philips curve
- However, we found no evidence of any link between Labor blame and wage growth

Main results (II)

This “fear of full employment” (Galbraith et al., 2007) is genuine, rather than rhetorical
- It appears in private deliberations, but fades in public speeches
This fear is also political, appearing exacerbated under Democrat Presidents
- Controlling for macroeconomic fundamentals, Fed officials blame more Labor for inflation under a Left incumbent

Future avenues and open questions

Find an angle on the historical paper. Suggestions welcome!
Exploring individual level variation: role of monetary policy preferences (Bordo & Istrefi, 2023) or political partisanship (Pagliuca, 2025).
Linking (individual) inflation diagnoses to actual policy decisions: adding Labor salience in a Taylor rule?

Thank you!

Appendix

Inflation Keywords


inflation	cpi	pce
price increas	price stab	overall price
price level	consumer price	producer price
price index	price pressure	price shock
higher price	raise price	raising price
rising price

→ Back to the corpus creation

“Enhanced” triplets

→ Back to the corpus creation

Example 1: Energy and Commodities

Excerpt:

“In recent months, turmoil in North Africa and the Middle East has reduced the global supply of oil and likely added a substantial risk premium to the price of a barrel of crude as well. What do these fast-rising commodity prices mean for inflation for the rest of the year and beyond? I believe that the inflation rate will reach a peak around the middle of this year and then edge back downward. In other words, we are seeing a temporary bulge in inflation before we return to an underlying level of about 11⁄4 to 11⁄2 percent annually. There are several reasons for thinking the inflation bulge will be short-lived. First, commodity prices are not likely to keep increasing indefinitely at a rapid rate.” (Williams, speech, 4 May 2011)

Code(s): Energy, Commodities

→ Back to the codebook

Example 2: Fiscal and Exchange Rates

Excerpt:

“As I look at these projections, Mr. Chairman, I think that the risk is probably on the up side in terms of higher inflation and growth than projected. I think the staff’s projections are very reasonable. The only point of departure I have is on the inflation number, which I think will be higher. Some of the reasons have already been stated, so I’ll skip over those but one of the major factors is the effect of the structural deficit plus the projected decline of the dollar.” (Forrestal, FOMC, 30 January 1984)

Code(s): Fiscal, Exchange Rates

→ Back to the codebook

Example 3: Market Power

Excerpt:

“Many manufacturers are continuing to implement staff reduction plans, and I just don’t sense any fundamental improvement in the underlying employment conditions. Inflation continues to look constructive. Intensive competitive conditions seem to be an absolute lid on significant price increases. The people I talk to say the pricing environment for all products is really very tough.” (Keehn, FOMC, 30 June 1992)

Code(s): Market Power

→ Back to the codebook

Example 4: Labor and Other Factors

Excerpt:

“On the one hand, such an outlook suggests that the downside risks for the economy are limited in the short run. On the other, the longer-run inflation outlook is not so sanguine. Despite moderate decreases in PPI and CPI inflation so far in 1997, there is a heightened concern about an inflationary impulse emanating from observed price increases, a tight labor market, and a continuing easy credit market. In addition, we have seen anecdotal evidence along the lines of what Bill McDonough mentioned with respect to the apparently shifting pattern of health care cost increases from a declining to a rising trend that will no longer tend to offset at least a portion of the wage increases. It does not seem to me that the current stance of monetary policy will mitigate the inflation impulse that I outlined. Growth rates in the broad monetary aggregates, which conceivably are back on track, are running at or above our announced targets.” (Melzer, FOMC, 25 March 1997)

Code(s): Labor, Other (credit, health care)

→ Back to the codebook

Example 5: No Inflation Driver

Excerpt:

“I see job creation eventually falling to about 100,000 a month by the end of 2019. Just as I think we need to put monthly fluctuations in inflation data in perspective, I think we need to put job growth in context. In the very near future, we’re not going to need to create jobs at the intense pace we’ve been experiencing over the past few years.” (Harker, speech, 1 June 2017)

Code(s): None

→ Back to the codebook

Prompt

We are exploring central bank communication by looking at central banker policy deliberations. We want to understand how they discuss the causes of inflation. Please read the excerpt, and classify it using the following labels:

“None” if the excerpt does not discuss inflation.
“None” if the excerpt describes inflation but does not make any reference to the causes of inflation.
“Labor” if the excerpt states that wages, workers’ demands, or other developments in labor markets cause inflation.
“Market power” if the excerpt states that business’ price-setting behaviour, profits, markups or competition dynamics cause inflation.
“Fiscal” if the excerpt states that government spending, deficit, public expenses, taxes, and other fiscal or budgetary considerations cause inflation.
“Energy” if the excerpt states that changes in the price or supply of energy cause inflation.
“Commodities” if the excerpt states that changes in the price of non-energy commodities (such as food, agriculture, raw materials, etc.) cause inflation.

Prompt

“Exchange rate” if the excerpt states that the value of the dollar against other currencies causes inflation.
“Other” if the excerpt discusses other causes of inflation that are not captured by the previous categories (such as supply chain bottlenecks, regulations, productivity, credit dynamics, shelter, healthcare costs, or anything not listed here).

You can give excerpts multiple labels (for example, “Market power, Fiscal”).

IF the excerpt has the label “None”, it should have no other labels.

Reply ONLY with the assigned label(s) (for example, “Labor, Other”). DO NOT EXPLAIN YOUR ANSWER. This is the excerpt:

→ Back to the codebook

Model selection

→ Back to the Model selection

Pipeline overview

Robustness tests

These results are robust to

Other modeling strategies (Poisson, Pseudo-Poisson).
Alternative data sources, including both Tealbook forecasts
Substituting our unemployment variables for the unemployment gap (UGAP) — the difference between the unemployment rate and the Congressional Budget Office’s estimated NAIRU for each period

Robustness tests

These results are robust to

Dummy variable for the 1993 transparency shock
Leave-one-out analysis excluding one presidential administration at a time
Subgroup regressions to account for different speaker distribution in public / private
15y rolling window regressions

References

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Arbogast, T., Van Doorslaer, H., & Vermeiren, M. (2023). Another strange non-death: The NAIRU and the ideational foundations of the federal reserve’s new monetary policy framework. Review of International Political Economy, 1–26.

Bartels, L. M. (2016). Unequal democracy: The political economy of the new gilded age. Princeton University Press.

Bernanke, B., & Blanchard, O. (2025). What caused the US pandemic-era inflation? American Economic Journal: Macroeconomics, 17(3), 1–35. https://doi.org/10.1257/mac.20230195

Best, J. (2020). The quiet failures of early neoliberalism: From rational expectations to keynesianism in reverse. Review of International Studies, 46(5), 594–612. https://doi.org/10.1017/S0260210520000169

Blinder, A. S. (2022). A monetary and fiscal history of the united states, 1961–2021. In A monetary and fiscal history of the united states, 1961–2021. Princeton University Press.

Blinder, A. S., & Watson, M. W. (2016). Presidents and the US economy: An econometric exploration. American Economic Review, 106(4), 1015–1045. https://doi.org/10.1257/aer.20140913

Bordo, M., & Istrefi, K. (2023). Perceived FOMC: The making of hawks, doves and swingers. Journal of Monetary Economics, 136, 125–143. https://doi.org/10.1016/j.jmoneco.2023.03.001

Braun, B., Carlo, D. D., Diessner, S., & Düsterhöft, M. (2024). Structure, agency, and structural reform: The case of the european central bank. Perspectives on Politics, 1–20. https://doi.org/10.1017/S1537592723002992

Campiglio, E., Deyris, J., Romelli, D., & Scalisi, G. (2025). Warning words in a warming world: Central bank communication and climate change. European Economic Review, 178, 105101. https://doi.org/10.1016/j.euroecorev.2025.105101

Clark, W. R., & Arel-Bundock, V. (2012). Independent but not indifferent: Partisan bias in monetary policy at the fed (2166647). Social Science Research Network. https://papers.ssrn.com/abstract=2166647

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