# How do geopolitical risks and uncertainty shape US consumer confidence?

Moustapha Badran, Mohamed Awada, Joanna Darwiche, Whelsy Boungou

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Moustapha Badran, Mohamed Awada, Joanna Darwiche, Whelsy Boungou. How do geopolitical risks and uncertainty shape US consumer confidence?. Economics Letters, 2025, 247, pp.112115. 10.1016/j.econlet.2024.112115. hal-05083249

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# How do geopolitical risks and uncertainty shape US consumer confidence?

Moustapha Badran<sup>a</sup>, Mohamed Awada<sup>a,*</sup>, Joanna Darwiche<sup>b</sup>, Whelsy Boungou<sup>c</sup>

## ARTICLE INFO

**JEL codes:**

D12

E52

F13

F51

**Keywords:**

Geopolitical risk

Trade policy uncertainty

Monetary policy uncertainty

Consumer confidence

Behavioral economics

## ABSTRACT

This study examines the impact of trade and monetary policy uncertainty during geopolitical shocks on U.S. consumer confidence. Using monthly data from January 1993 to June 2024, we find that both trade and monetary policy uncertainties have significantly shaped U.S. consumer confidence during major geopolitical events. Moreover, trade policy uncertainty significantly worsens consumer confidence during heightened geopolitical tension, while monetary policy uncertainty tends to stabilize sentiment. Our findings suggest that policymakers should implement clear and consistent trade policies during geopolitical unrest and effectively use monetary policy to reassure consumers. Ultimately, this research underscores the importance of addressing trade and monetary uncertainties to bolster economic resilience and enhance consumer confidence in challenging times.

## 1. Introduction

> "I think we're on a knife-edge" regarding inflation, reflecting the 'uneasiness among Americans' due to rising costs and economic uncertainty."

Kenneth Rogoff, 2021

Uncertainty around monetary and trade policies have a significant negative impact on consumer confidence, quickly influencing financial outlook through a range of economic pressures (Mehta et al., 2020; Van der Wielen and Barrios, 2021; Gong et al., 2022). These pressures often lead to price volatility, higher borrowing costs, and increased market instability, which can erode consumers' sense of financial security (Istiak and Serletis, 2020). In turn, such uncertainty affects key factors that shape consumer decision-making, including inflation (Armantier et al., 2021), interest rates (Eggertsson et al., 2024), job security (Lee et al., 2020), and the cost of living (Handbury, 2021). As confidence declines, consumers tend to adopt a more cautious approach, adjusting their spending and savings behavior accordingly (Ghosh, 2022). This caution leads to reduced household expenditures, highlighting how economic anxieties shape consumer behavior (Mumtaz and Surico, 2018; Boungou and Yatié, 2024). Our research aims to analyze the impact of monetary and trade policy uncertainty on U.S. consumer confidence,

with a specific focus on periods of heightened geopolitical tension and shocks. We seek to understand whether policy uncertainty affects consumer confidence uniformly during these periods, or if specific policy responses, such as Federal Reserve actions, have the potential to mitigate or alter these effects.

In recent decades, global markets have faced shocks from geopolitical tensions like the U.S.-China trade war, Brexit, the Russia-Ukraine conflict, and the Gulf War, disrupting markets and driving unpredictable shifts in trade (Olasehinde-Williams, 2021) and monetary policies (De Souza et al., 2024), which increase uncertainty. Therefore, given this context, we focus on periods of geopolitical tension because these events amplify the relationship between trade and monetary policy uncertainty and consumer sentiment, making consumers more sensitive to policy shifts amid heightened fears of economic disruption. By isolating these periods, we can better understand the impact of policy uncertainty on consumer confidence when expectations are most vulnerable, in contrast to more stable times when these effects may be less pronounced. Thus, the interconnectedness between global events, policy changes, and consumer behavior highlights the broad and far-reaching impact of geopolitical tensions (Ganesan and Mallapragada, 2024).

To our knowledge, no empirical research has explored the impact of

<footnote><sup>a</sup> Montpellier Research in Economics (MRE), Faculty of Economics, University of Montpellier, France</footnote>

<footnote><sup>b</sup> Faculty of Business and Management, Saint Joseph University of Beirut, Lebanon</footnote>

<footnote><sup>c</sup> Paris School of Business, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: bdrn.m@hotmail.com (M. Badran), Mohamed.awada@umontpellier.fr (M. Awada), Joanna.darwiche@usj.edu.lb (J. Darwiche), w.boungou@psedu.paris (W. Boungou).</footnote>
uncertainty—specifically trade and monetary policy—on U.S. consumer confidence amid geopolitical tensions. Previous studies have examined various dimensions of uncertainty and consumer behavior, such as Hamilton et al. (2019), who explored how financial constraints shape consumer resilience and adaptive strategies. Furthermore, Chi (2023) analyzed the effects of economic policy uncertainty, on U.S. travel behavior during crises and Ahmed and Sleem (2024) investigated the role of climate and economic policy uncertainties in shaping U.S. consumer sentiment, none of these studies specifically address the impact of trade and monetary policy uncertainty on U.S. consumer confidence in the context of geopolitical risks.

This study aims to fill this gap by making several key contributions to the literature by: (i) examining the interactive effects of trade and monetary policy uncertainty, geopolitical risks, and consumer sentiment over a long-term horizon (January 1993 – June 2024); (ii) considering major geopolitical risk events, including the Russia-Ukraine war, to demonstrate how policy uncertainty and geopolitical risks shape U.S. consumer sentiment; and (iii) leveraging insights from behavioral economics to provide policymakers with valuable perspectives on how U.S. consumers perceive and respond to uncertainty amid heightened geopolitical tensions. By integrating psychological and economic frameworks, our study offers a deeper understanding of consumer behavior under stress. It contributes to evaluating the effectiveness of U.S. trade and monetary policies during geopolitical shocks, offering practical guidance for managing public sentiment in times of instability.

The U.S. is an ideal sample for this study due to its role as a global economic powerhouse with high exposure to geopolitical and trade risks. U.S. consumer sentiment is crucial for both domestic and international financial activity. Over the study period, the U.S. has faced major geopolitical and trade uncertainties (e.g. 9/11, the Subprime crisis, U.S.-China trade tensions, COVID-19) and monetary policy shifts (Fed's quantitative easing and tapering), providing a robust dataset (Sharif et al., 2020).

To achieve the objective of this study, we employ multiple methodologies, including the static generalized method of moments with a robust set of valid instruments and a peak detection technique to identify significant geopolitical shocks and sub-period analysis for enhanced robustness. Our results demonstrate that geopolitical risk, trade policy uncertainty, and monetary policy uncertainty negatively affect consumer sentiment. In particular, trade policy uncertainty exacerbates the decline in consumer confidence during geopolitical tension. Conversely, we observe a shifting impact of monetary policy uncertainty during significant geopolitical risk events (September 11 attacks in 2001; the Iraq war in 2003; and the Russo-Ukrainian war in 2022), where it tends to stabilize consumer sentiment and can even foster a sense of reassurance among consumers, encouraging spending and mitigating fears of economic instability.

## 2. Data and model

We use monthly data spanning from January 1993 to June 2024, collected from multiple sources. We use two variables to measure consumer sentiment: the University of Michigan consumer sentiment index (CSI) and the FRED consumer confidence index (CCI). Table A.1 in the Appendix provides detailed definitions, sources, and classifications for each variable used in the study.

We apply the generalized method of moments (Demir and Danisman,

2021; Zhang et al., 2023) to address endogeneity arising from feedback effects<fnref n="1" /> and to ensure consistent estimates in static model.<fnref n="2" /> GMM effectively manages serial correlation and heteroskedasticity, providing robustness in long monthly datasets.<fnref n="3" /> To identify significant geopolitical shocks using the GPR index<fnref n="4" /> we apply peak detection methodology. We standardized the GPR index by calculating its Z-scores to detect periods exceeding 2 standard deviations above the mean,<fnref n="5" /> highlighting unusually high geopolitical risk. Next, we used a local maxima detection technique to ensure each observation was a true peak by comparing it to neighboring values. We then combined high Z-scores and local maxima criteria to create a dummy variable called significant GPR peaks (SGPRP) that captures periods of significant geopolitical shocks. We identify three significant spikes: the September 11 attacks in 2001, the start of the Iraq War in 2003, and the Russo-Ukrainian War in 2022. This method ensures that identified spikes in geopolitical risk are statistically significant and locally prominent. We added interaction terms between these dummy variables and both trade and monetary policy uncertainty to determine if these uncertainties' impact on consumer sentiment is amplified during geopolitical shocks, allowing us to explore how trade and monetary uncertainties influence consumer confidence in heightened geopolitical risk periods.<fnref n="6" /> Our model is specified as follows:

$$CSI_t(CCI_t) = \alpha_0 + \beta_1 GPR_t + \beta_2 TPU_t(MPU_t) + \beta_3 TPU_t * SGPRP_t(MPU_t * SGPRP_t) + \varepsilon_t$$

where $\alpha_0$ denotes the constant, $\beta_1$ captures the impact of geopolitical risk (GPR), $\beta_2$ and $\beta_3$ are the coefficients of interest, representing the interaction terms between trade policy uncertainty (TPU), monetary policy uncertainty (MPU), and SGPRP dummy variable that captures significant peaks in geopolitical shocks. $\varepsilon_t$ indicates the error term. We use the oil price uncertainty index, US-China tension index, energy-related uncertainty indexes and lagged variables<fnref n="7" /> of GPR, TPU, MPU as instrumental variables.<fnref n="8" />

<footnote n="1">¹ The feedback effect between consumer confidence and policy or geopolitical uncertainties suggests a two-way relationship, where not only do uncertainties impact consumer sentiment, but changes in consumer confidence also shape perceptions of risk, potentially influencing subsequent policy adjustments. This mutual dependence justifies the use of the GMM method to address endogeneity, ensuring unbiased estimates by accounting for the static interplay between these variables.</footnote>

<footnote n="2">² We use the static GMM model because it addresses endogeneity efficiently in our time series framework without involving lagged dependent variables or dynamic effects, making it more appropriate than dynamic panel GMM methods designed for panel data.</footnote>

<footnote n="3">³ We exclude the lagged dependent variable to focus on the contemporaneous effects of uncertainty on consumer sentiment, as our theoretical framework prioritizes real-time influences, the Durbin-Watson test shows no significant persistence (DW = 2.002), and including it could obscure results and complicate the GMM model.</footnote>

<footnote n="4">⁴ We use the geopolitical risk index developed by Caldara and Iacoviello (2022).</footnote>

<footnote n="5">⁵ We apply a threshold of two standard deviations above the mean, following Caldara et al. (2019), to identify significant peaks in GPR Index. This threshold is a widely recognized statistical criterion for detecting extreme or unusual events within time series data, effectively distinguishing regular fluctuations from significant deviations that indicate heightened risk.</footnote>

<footnote n="6">⁶ We test the cointegration using the Johansen cointegration test, which confirms that the variables in our model are cointegrated, ensuring reliable GMM estimations despite being I(1), as their relationships are not spurious.</footnote>

<footnote n="7">⁷ We use the lag t-1 for GPR, TPU and MPU.</footnote>

<footnote n="8">⁸ We conducted comprehensive tests, including the F-statistic for relevance and the Hansen test for exogeneity, confirming that our instrumental variables are both strongly correlated with the endogenous variables and uncorrelated with the error term, ensuring their validity and robustness.</footnote>
## 3. Results

Table 1 summarizes our main results, indicating that geopolitical risk negatively impacts U.S. consumer sentiment, consistent with Istiak (2023) and Makrychoriti and Spyrou (2023). Both trade and monetary policy uncertainty also have significant negative effects on consumer sentiment at the 1% level, as consumers respond with increased caution about spending and financial commitments. This reaction reflects concerns over potential economic instability and personal financial security during unpredictable periods, aligning with findings by Baker et al. (2016), Reis and Pinho (2021), Van der Wielen and Barrios (2021), and Gong et al. (2022).<fnref n="9" /> The interaction terms TPU * SGPRP and MPU * SGPRP reveal how the impact of TPU and MPU on consumer confidence is amplified or altered in periods of heightened geopolitical risk.

The interaction term TPU * SGPRP shows a significant negative coefficient for both CSI and CCI, indicating that during periods of high geopolitical risk, trade policy uncertainty intensifies the decline in consumer sentiment. U.S. consumers perceive trade uncertainty as a sign of economic vulnerability, leading to fears of trade disruptions, rising import costs, job insecurity, and inflation. Trade uncertainty during volatile periods heightens consumer anxiety by threatening price increases and goods shortages. Consequently, when geopolitical tensions and trade uncertainty align, consumer confidence declines further, consistent with the inflationary channel through which GPR impacts sentiment (Caldara et al., 2024).

The interaction term MPU * SGPRP shows a positive coefficient for both CSI and CCI, suggesting that during high geopolitical risk, monetary policy uncertainty stabilizes rather than weakens consumer sentiment. Consumers may view Federal Reserve actions as stabilizing, expecting interventions to mitigate negative impacts. This belief reduces economic anxiety, as they anticipate measures like interest rate and liquidity adjustments, enhanced credit access, exchange rate stabilization, and measures to prevent sharp declines in housing and equity markets to support the economy (Cepni et al., 2023). In response to crises such as 9/11, the Iraq invasion in 2002, and the Ukraine-Russia conflict, the Federal Reserve implemented several monetary policy measures that positively impacted consumer behavior despite

<table><caption>**Table 1**
Consumer sentiment responses to trade and monetary policy uncertainty under significant geopolitical shocks.</caption><thead><tr><th rowspan="2"></th><th colspan="2">CSI</th><th colspan="2">CCI</th></tr><tr><th>[1]</th><th>[2]</th><th>[3]</th><th>[4]</th></tr></thead><tbody><tr><td>GPR</td><td>-9.8152***<br>(0.000)</td><td>-6.5274**<br>(0.042)</td><td>-2.2914*<br>(0.061)</td><td>-2.2501***<br>(0.000)</td></tr><tr><td>TPU</td><td>-0.1584***<br>(0.000)</td><td></td><td>-0.0181***<br>(0.000)</td><td></td></tr><tr><td>TPU*SGPRP</td><td>-3.1531**<br>(0.031)</td><td></td><td>-1.8066***<br>(0.001)</td><td></td></tr><tr><td>MPU</td><td></td><td>-0.1922***<br>(0.001)</td><td></td><td>-0.0219***<br>(0.002)</td></tr><tr><td>MPU*SGPRP</td><td></td><td>6.6386***<br>(0.000)</td><td></td><td>5.4654***<br>(0.000)</td></tr><tr><td>Intercept</td><td>8.1688***<br>(0.000)</td><td>1.3639***<br>(0.000)</td><td>8.3564***<br>(0.000)</td><td>1.8478***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>20.01</td><td>36.77</td><td>19.57</td><td>30.21</td></tr><tr><td>Prob > Chi2</td><td>0.0003</td><td>0.0000</td><td>0.0006</td><td>0.0000</td></tr><tr><td>Hansen p-value</td><td>0.3911</td><td>0.2214</td><td>0.3761</td><td>0.2305</td></tr><tr><td>R-squared</td><td>0.2235</td><td>0.1467</td><td>0.3261</td><td>0.1799</td></tr><tr><td>Nb. of Obs.</td><td>378</td><td>378</td><td>378</td><td>378</td></tr></tbody></table>

Note: *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

uncertainty. Key actions included rapid interest rate cuts to stimulate the economy, quantitative easing to inject liquidity and lower long-term interest rates, and forward guidance to communicate intentions for sustained low rates. These strategies aimed to stabilize expectations and encourage consumer spending, fostering a sense of security in uncertain times. Overall, the Fed's proactive measures have demonstrated its effectiveness in navigating economic challenges and supporting consumer confidence (Mishkin and White, 2014). All Hansen *p*-values are greater than 0.05, indicating that the instruments are valid and exogenous, as they do not appear correlated with the model's error term.

To assess the robustness of our initial findings and gain a more nuanced understanding and deeper look of how trade and monetary uncertainty affect consumer confidence during significant geopolitical risks, we performed sub-period regressions. We separated the data into two distinct periods: before and during each major geopolitical event previously detected (september 11 attacks in 2001, Iraq war in 2003, and the Russo-Ukrainian war in 2022). This allowed us to compare the impact of policy uncertainty on U.S. consumer confidence across these two time frames. The results are reported in the tables below (Tables 2-4).

The tables above reveal that during the three key geopolitical events, both trade and monetary policy uncertainties have a negative impact on U.S. consumer confidence. Our sub-regression analysis indicates that heightened geopolitical risk amplifies the adverse effects of trade policy uncertainty on consumer confidence (Wu et al., 2023). Specifically, in times of elevated geopolitical risk, increases in trade policy uncertainty lead to a sharper decline in consumer confidence, as consumers become more cautious, often postponing major purchases and financial commitments due to concerns about potential trade disruptions and price instability.

Among the events analyzed, the September 11 attacks had the most significant effect on consumer confidence, compared to the Iraq War in 2003 and the Russo-Ukrainian War in 2022. This heightened impact is likely due to both the unprecedented nature of a direct attack on U.S. soil and the subsequent heightened fears around national security, which intensified economic uncertainty and consumer caution.<fnref n="10" />

In contrast, our findings show that during these geopolitical events, the negative impact of monetary policy uncertainty on consumer

<table><caption>**Table 2**
The impact before and during the September 11 attack.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre-September 11 attack</th><th colspan="2">During September 11 attack</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>TPU</td><td>-0.0623***<br>(0.001)</td><td>-0.0065***<br>(0.000)</td><td>-0.1057***<br>(0.000)</td><td>-0.0101***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>34.22</td><td>34.66</td><td>12.39</td><td>11.79</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hansen p-value</td><td>0.4522</td><td>0.3396</td><td>0.1299</td><td>0.2851</td></tr><tr><td>R-squared</td><td>0.3413</td><td>0.4613</td><td>0.1344</td><td>0.1259</td></tr><tr><td>MPU</td><td>-0.2105***<br>(0.000)</td><td>-0.0232***<br>(0.000)</td><td>-0.1081***<br>(0.000)</td><td>-0.01029***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>21.65</td><td>33.91</td><td>20.21</td><td>13.24</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hansen p-value</td><td>0.3411</td><td>0.2677</td><td>0.1853</td><td>0.1745</td></tr><tr><td>R-squared</td><td>0.3777</td><td>0.4021</td><td>0.2915</td><td>0.2712</td></tr><tr><td>Nb. of Obs.</td><td>103</td><td>103</td><td>16</td><td>16</td></tr></tbody></table>

Note: The pre-September 11 period is defined as January 1993 to August 2001; the during September 11 period is defined as September 2001 to December 2002; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

<footnote n="9">⁹ Boungou and Yatié (2024) also highlight the negative impact of uncertainties on the performance of crypto-assets.</footnote>

<footnote n="10">¹⁰ We use Economic Policy Uncertainty (EPU) in place of Trade Policy Uncertainty (TPU), and the results remain unchanged. Detailed results are available in the appendix.</footnote>
<table><caption>**Table 3**
The impact before and during the Iraq war in 2003.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre-Iraq war</th><th colspan="2">During Iraq war</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>TPU</td><td>-0.1077***<br>(0.000)</td><td>-0.0137***<br>(0.000)</td><td>-0.1359***<br>(0.000)</td><td>-0.016***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>24.22</td><td>16.32</td><td>11.99</td><td>10.43</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hasen p-value</td><td>0.1321</td><td>0.1239</td><td>0.2775</td><td>0.2311</td></tr><tr><td>R-squared</td><td>0.1678</td><td>0.2414</td><td>0.1149</td><td>0.1429</td></tr><tr><td>MPU</td><td>-0.1854***<br>(0.005)</td><td>-0.0218***<br>(0.000)</td><td>-0.0348***<br>(0.000)</td><td>-0.0031***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>12.65</td><td>14.89</td><td>14.23</td><td>10.31</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hansen p-value</td><td>0.3032</td><td>0.2811</td><td>0.1412</td><td>0.1923</td></tr><tr><td>R-squared</td><td>0.2931</td><td>0.3021</td><td>0.1616</td><td>0.1423</td></tr><tr><td>Nb. of Obs.</td><td>121</td><td>121</td><td>22</td><td>22</td></tr></tbody></table>

*Note:* The pre-Iraq war period is defined as January 1993 to February 2003; the during Iraq war period is defined as March 2003 to December 2004; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

<table><caption>**Table 4**
The impact before and during the Russo-Ukrainian War in 2022.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre-Russo-Ukrainian War</th><th colspan="2">During Russo-Ukrainian War</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>TPU</td><td>-0.0738***<br>(0.000)</td><td>-0.0081***<br>(0.000)</td><td>-0.183***<br>(0.000)</td><td>-0.0338***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>14.65</td><td>13.61</td><td>9.42</td><td>8.88</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0001</td></tr><tr><td>Hasen p-value</td><td>0.1242</td><td>0.3411</td><td>0.1242</td><td>0.1112</td></tr><tr><td>R-squared</td><td>0.2533</td><td>0.2421</td><td>0.2099</td><td>0.1903</td></tr><tr><td>MPU</td><td>-0.0176***<br>(0.000)</td><td>-0.0091***<br>(0.000)</td><td>-0.0060**<br>(0.025)</td><td>-0.0001***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>19.13</td><td>14.21</td><td>18.24</td><td>19.33</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hansen p-value</td><td>0.2822</td><td>0.2219</td><td>0.1212</td><td>0.1932</td></tr><tr><td>R-squared</td><td>0.4028</td><td>0.4122</td><td>0.5642</td><td>0.6259</td></tr><tr><td>Nb. of Obs.</td><td>348</td><td>348</td><td>28</td><td>28</td></tr></tbody></table>

*Note:* The pre-Russo-Ukrainian war period is defined as January 1993 to January 2022; the during Russo-Ukrainian war period is defined as February 2022 to June 2024; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

confidence is somewhat moderated. This effect appears due to

stabilizing policy adjustments, which may lessen the usual adverse influence of monetary uncertainty on consumers. Consumers seem to anticipate monetary responses to geopolitical crises, which alleviate their concerns by offering stability through lower interest rates, liquidity support, and measures that maintain credit access and stabilize asset values. These interventions collectively reinforce consumer confidence in their financial security and purchasing power during turbulent periods. As Bekaert et al. (2013) suggest, while monetary uncertainty often breeds caution, in the context of geopolitical risk, it may signal a protective response aimed at safeguarding economic stability. This expectation helps foster a more favorable consumer outlook even amid heightened uncertainties.

## 4. Conclusion

This study provides valuable insights into how trade and monetary policy uncertainties influence U.S. consumer sentiment from January 1993 to June 2024. We find that trade and monetary policy uncertainty have played a significant role in shaping U.S. consumer confidence throughout major geopolitical events over recent decades. Our analysis reveals that trade policy uncertainty significantly undermines consumer confidence during periods of geopolitical tension, amplifying consumer risk aversion. Conversely, monetary policy uncertainty appears to have a stabilizing effect, as consumers anticipate supportive policy adjustments in response to crises. These findings suggest that, during geopolitical unrest, policymakers should prioritize clear and consistent trade policies to minimize consumer unease, while leveraging adaptive monetary measures to reassure consumers and bolster economic resilience.

# Appendix

Tables A.1, A.2, A.3 and A.4

<table><caption>**Table A.1**
Variables definitions, types and sources.</caption><thead><tr><th>Variables</th><th>Types</th><th>Sources</th></tr></thead><tbody><tr><td>University of Michigan consumer sentiment index</td><td>Dependent variable</td><td>https://www.stlouisfed.org/</td></tr><tr><td>FRED consumer sentiment index</td><td>Dependent variable</td><td>https://www.stlouisfed.org/</td></tr><tr><td>Geopolitical risk index</td><td>Independent variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>Trade policy uncertainty</td><td>Independent variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>Monetary policy uncertainty</td><td>Independent variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>Economic policy uncertainty</td><td>Independent variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>Oil price uncertainty index</td><td>Instrumental variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>US-China tension index</td><td>Instrumental variable</td><td>https://www.policyuncertainty.com/</td></tr><tr><td>Energy-related uncertainty indexes</td><td>Instrumental variable</td><td>https://www.policyuncertainty.com/</td></tr></tbody></table>
<table><caption>**Table A.2**
The impact before and during the September 11 attack using economic policy uncertainty.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre-September 11 attack</th><th colspan="2">During September 11 attack</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>EPU</td><td>-0.0714**<br>(0.012)</td><td>-0.0032**<br>(0.017)</td><td>-0.131***<br>(0.005)</td><td>-0.1666***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>21.33</td><td>22.16</td><td>11.32</td><td>11.28</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hasen p-value</td><td>0.2533</td><td>0.2126</td><td>0.1176</td><td>0.1397</td></tr><tr><td>R-squared</td><td>0.2144</td><td>0.2423</td><td>0.1759</td><td>0.1647</td></tr><tr><td>Nb. of Obs.</td><td>103</td><td>103</td><td>16</td><td>16</td></tr></tbody></table>

Note: The pre-September 11 period is defined as January 1993 to August 2001; the during September 11 period is defined as September 2001 to December 2002; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

<table><caption>**Table A.3**
The impact before and during the Iraq war in 2003 using economic policy uncertainty.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre-Iraq war</th><th colspan="2">During Iraq war</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>EPU</td><td>-0.1028***<br>(0.000)</td><td>-0.0117***<br>(0.000)</td><td>-0.1111***<br>(0.000)</td><td>-0.1761***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>22.14</td><td>21.89</td><td>10.38</td><td>11.22</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hasen p-value</td><td>0.1132</td><td>02,319</td><td>0.1843</td><td>0.1232</td></tr><tr><td>R-squared</td><td>0.2045</td><td>0.2754</td><td>0.1195</td><td>0.1855</td></tr><tr><td>Nb. of Obs.</td><td>121</td><td>121</td><td>22</td><td>22</td></tr></tbody></table>

Note: The pre-September 11 period is defined as January 1993 to August 2001; the during September 11 period is defined as September 2001 to December 2002; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

<table><caption>**Table A.4**
The impact before and during the Russo-Ukrainian War using economic policy uncertainty.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Pre Russo-Ukrainian War</th><th colspan="2">During Russo-Ukrainian War</th></tr><tr><th>CSI</th><th>CCI</th><th>CSI</th><th>CCI</th></tr></thead><tbody><tr><td>EPU</td><td>-0.0678***<br>(0.000)</td><td>-0.0076***<br>(0.000)</td><td>-0.1344***<br>(0.000)</td><td>-0.1285***<br>(0.000)</td></tr><tr><td>Wald Chi2</td><td>46.30</td><td>33,88</td><td>34.21</td><td>32.22</td></tr><tr><td>Prob > Chi2</td><td>0.0000</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Hasen p-value</td><td>0.1237</td><td>0.1349</td><td>0.1136</td><td>0.1212</td></tr><tr><td>R-squared</td><td>0.5734</td><td>0.6721</td><td>0.3497</td><td>0.4148</td></tr><tr><td>Nb. of Obs.</td><td>348</td><td>348</td><td>28</td><td>28</td></tr></tbody></table>

Note: The pre-September 11 period is defined as January 1993 to August 2001; the during September 11 period is defined as September 2001 to December 2002; *p*-values are represented in parentheses. ***, **, * stand for significance at levels 1%, 5% and 10% respectively.

## Data availability

Data will be made available on request.

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