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• Published: 10 June 2024

African elephants address one another with individually specific name-like calls

• Michael A. Pardo   ORCID: orcid.org/0000-0001-6978-848X 1 ,
• Kurt Fristrup   ORCID: orcid.org/0000-0002-7467-9314 2 ,
• David S. Lolchuragi 3 ,
• Joyce H. Poole 4 ,
• Petter Granli 4 ,
• Cynthia Moss 5 ,
• Iain Douglas-Hamilton 3 &
• George Wittemyer   ORCID: orcid.org/0000-0003-1640-5355 1 , 3  

Nature Ecology & Evolution ( 2024 ) Cite this article

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• Animal behaviour
• Behavioural ecology

Personal names are a universal feature of human language, yet few analogues exist in other species. While dolphins and parrots address conspecifics by imitating the calls of the addressee, human names are not imitations of the sounds typically made by the named individual. Labelling objects or individuals without relying on imitation of the sounds made by the referent radically expands the expressive power of language. Thus, if non-imitative name analogues were found in other species, this could have important implications for our understanding of language evolution. Here we present evidence that wild African elephants address one another with individually specific calls, probably without relying on imitation of the receiver. We used machine learning to demonstrate that the receiver of a call could be predicted from the call’s acoustic structure, regardless of how similar the call was to the receiver’s vocalizations. Moreover, elephants differentially responded to playbacks of calls originally addressed to them relative to calls addressed to a different individual. Our findings offer evidence for individual addressing of conspecifics in elephants. They further suggest that, unlike other non-human animals, elephants probably do not rely on imitation of the receiver’s calls to address one another.

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Acknowledgements

We thank the Office of the President of Kenya, the Samburu, Isiolo and Kajiado County governments, the Wildlife Research & Training Institute of Kenya, and Kenya Wildlife Service for permission to conduct fieldwork in Kenya. We thank Save The Elephants and the Amboseli Trust for Elephants for logistical support in the field, J. M. Leshudukule, D. M. Letitiya and N. Njiraini for assistance with the fieldwork, G. Pardo for blinding the playback stimuli and S. Pardo for input on the statistical analyses. We thank J. Berger, W. Koenig and A. Horn for comments on the manuscript. This project was funded by a Postdoctoral Research Fellowship in Biology to M.A.P. from the National Science Foundation (award no. 1907122) and grants to J.H.P. and P.G. from the National Geographic Society, Care for the Wild, and the Crystal Springs Foundation. Fieldwork was supported by Save the Elephants.

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Michael A. Pardo & George Wittemyer

Department of Electronic and Computer Engineering, Colorado State University, Fort Collins, CO, USA

Kurt Fristrup

Save The Elephants, Nairobi, Kenya

David S. Lolchuragi, Iain Douglas-Hamilton & George Wittemyer

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Contributions

M.A.P. conceived the study. M.A.P. and D.S.L. collected the data in Samburu, and J.H.P. and P.G. collected the data in Amboseli. M.A.P. and K.F. performed the statistical analysis, and M.A.P. created the figures. M.A.P. drafted the manuscript, and K.F., J.H.P. and G.W. edited it. C.M., I.D.-H. and G.W. provided resources and access to long-term datasets, and G.W. supervised the study.

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Correspondence to Michael A. Pardo .

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Extended data

Extended data fig. 1 schematic illustrating how spectral acoustic features were measured..

First, a spectrogram was calculated by applying a Fast Fourier Transform to the signal (Hamming window, 700 samples, 90% overlap). Then a mel filter bank with 26 overlapping triangular filters between 0-500 Hz was applied to each window of the spectrogram to produce a mel spectrogram. The mel spectrogram was then normalized by dividing the energy value in each cell by the total energy in that time window and these proportional energies were logit-transformed so they would not be limited to between 0 and 1. As features for the robust principal components analysis, we used the vector of energy in each of the 26 mel frequency bands as well as the vectors of delta and delta-delta values for each frequency band (representing the change and acceleration in energy over time, respectively). In the spectrogram and mel spectrogram in this figure, warmer colors indicate higher amplitudes (greater energy).

Extended Data Fig. 2 Scatterplots illustrating the separation in 3D space between calls from the same caller to different receivers.

Axes are the first three principal coordinates extracted from the proximity scores of a random forest trained to predict receiver ID. Each plot represents a single caller, each point is a single call, and receiver IDs are coded by both color and shape. This figure only includes calls where caller ID was known for certain, where the call was predicted correctly in at least 25% of random forest iterations, and where the caller made at least two such calls each to at least two different receivers.

Extended Data Fig. 3 Scatterplot illustrating the clustering in 3D space of calls from different callers to the same receiver.

Axes are the first three principal coordinates extracted from the proximity scores of a random forest trained to predict receiver ID. Each shape represents a different receiver and each color represents a different caller. This figure only includes calls where caller ID was known for certain, where the call was predicted correctly in at least 25% of random forest iterations, and where the receiver received at least one such call each from at least two different callers.

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Pardo, M.A., Fristrup, K., Lolchuragi, D.S. et al. African elephants address one another with individually specific name-like calls. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02420-w

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Published on 25.6.2024 in Vol 26 (2024)

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review

Authors of this article:

• Milica Barac 1 * , BS   ; 
• Samantha Scaletty 1 * , BS   ; 
• Leslie C Hassett 2 , MLS   ; 
• Ashley Stillwell 3 , DO   ; 
• Paul E Croarkin 4 , DO, MS   ; 
• Mohit Chauhan 5 , MD   ; 
• Sherry Chesak 6 , PhD   ; 
• William V Bobo 5 , MD, MPH   ; 
• Arjun P Athreya 1, 4 * , MS, PhD   ; 
• Liselotte N Dyrbye 7 * , MD, MPHE  

1 Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States

2 Mayo Clinic Libraries, Mayo Clinic, Rochester, MN, United States

3 Department of Family Medicine, Mayo Clinic, Phoenix, AZ, United States

4 Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States

5 Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, United States

6 Department of Nursing, Mayo Clinic, Rochester, MN, United States

7 Department of Medicine, University of Colorado School of Medicine, Aurora, CO, United States

*these authors contributed equally

Corresponding Author:

Liselotte N Dyrbye, MD, MPHE

Department of Medicine

University of Colorado School of Medicine

Mail Stop C290, Fitzsimons Bldg

13001 E 17th Pl. Rm #E1347

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Email: [email protected]

Background: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers.

Objective: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs).

Methods: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies.

Results: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements.

Conclusions: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

Introduction

Burnout is an occupational syndrome characterized by emotional exhaustion, depersonalization, and feelings of reduced personal accomplishment caused by chronic, unmitigated high levels of job-related stress [ 1 ]. Burnout is common among health care professionals (HCPs, also referred to as health care workers), impacting an estimated 35% to 54% of nurses and physicians, and between 45% and 60% of medical students and resident physicians in the United States [ 2 ]. Several studies also reveal a high prevalence of depression and anxiety in HCPs that preceded the coronavirus pandemic [ 3 - 9 ]. Data further suggests that burnout and other forms of distress have increased among HCPs as a result of the COVID-19 pandemic [ 10 - 12 ].

This is concerning because the well-being of HCPs impacts the quality of patient care and patients’ access to care. Several meta-analyses and systematic reviews have reported associations between burnout and negative impacts on the quality of care provided to patients, including increasing the risk of medical errors [ 13 ], malpractice claims [ 14 ], nosocomial infections [ 15 ], and mortality [ 16 ]. Additionally, other studies have found that HCPs who report experiencing burnout are more likely to reduce their time taking care of patients and quit, all of which negatively impact patient’s access to care and add a burden to the global health care system [ 2 ]. The impacts of burnout go beyond the workplace, as HCPs with reported burnout are at increased risk of cardiovascular diseases [ 17 , 18 ], suicidal ideation [ 13 , 19 ], substance use disorders [ 20 ], uncontrolled stress [ 21 ], car accidents [ 22 ], and quality of life [ 23 ].

Contributors of burnout in HCPs are multifactorial and complex. While most factors contributing to burnout originate from system-level factors within the work environment, some risk factors originate from the personal domain or challenges in the personal-professional interface, such as work-home conflict ( Figure 1 ). Due to the complexity of the factors involved, no model exists for predicting when an individual HCP or group of HCPs are at risk for developing burnout or other forms of distress. In response to the negative outcomes of burnout for HCPs and patients, the National Academies of Science, Engineering, and Medicine recommends health care organizations monitor (through frequent surveys) and respond to burnout. This approach is retrospective, as the time required for health care organizations to administer surveys, HCPs to complete them, and the additional time needed to analyze and interpret results all delay any response to burnout. A better approach would be a proactive one, where organizations or individual HCPs could predict and respond to high levels of job stress before the manifestation of burnout and associated personal and professional consequences result.

Previous studies and reviews suggest heart rate (HR) [ 24 ], heart rate variability (HRV) [ 24 ], sleep [ 25 ], and skin temperature [ 26 ] vary in response to stress. Additionally, sleep or fatigue also relates to the risk of burnout [ 27 ], depression [ 28 ], and other related conditions [ 29 ]. These types of data can be collected passively from wearable devices. Over the past 5 years, the adoption of wearable devices worldwide has more than doubled [ 30 ]. Therefore, data collected passively from wearable devices could potentially provide an avenue for detecting individuals at risk for high job stress, burnout, depression, and other related conditions. If predictive, such real-time information obtained passively from wearable devices could dramatically shift the current reactive paradigm to a proactive one, potentially leading to meaningful intervention before patients and HCPs experience adverse health consequences of burnout.

Previous systematic reviews suggest wearable devices may have some utility in predicting depression severity and stress levels [ 31 ]. To our knowledge, there is no review that investigates this relationship among HCPs or explores the ability of wearable devices to detect burnout risk. Hence, a scoping review was conducted to identify and summarize studies exploring associations between burnout, anxiety, depression, and stress, with data obtained from wearable devices in cohorts of HCPs.

Data Sources and Search Strategy

A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases (and their coverage periods) were Ovid: MEDLINE (1946 to Present and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily), Embase (1974+), Healthstar (1966+), APA PsycInfo (1987+), Cochrane Central Register of Controlled Trials (1991+), Cochrane Database of Systematic Reviews (2005+), Web of Science Core Collection via Clarivate Analytics (1975+), Scopus via Elsevier (1788+), EBSCOhost: Academic Search Premier, CINAHL with Full Text (1981+), and Business Source Premier.

The search strategy was designed and conducted by a medical librarian (LCH) with input from the study’s investigators (APA and LND). Controlled vocabulary supplemented with keywords was used. The actual strategies listing all search terms used and how they are combined are available in the Multimedia Appendix 1 .

Review Strategy

The initial search yielded 505 papers. Two reviewers (MB and SS) independently identified and screened the titles and abstracts of potentially eligible papers. The inclusion criteria of the initial round of screening were as follows: the study must include a validated measure of burnout, stress, anxiety, or depression and the study must include only data from a wearable device worn by an HCP. For this work, we defined HCP as being a medical student, resident, practicing physician, or registered nurse in a hospital or outpatient clinical setting. The full-text reviews of the papers that resulted from the initial screening, data extraction, and quality assessment were also performed independently and in pairs by 2 reviewers (MB and SS). Papers were not excluded due to their calculated quality score. During this process, 475 papers were omitted because they did not satisfy the inclusion criteria (n=472) or were duplicates (n=3). After the initial screening, the full text of 30 papers was assessed for eligibility. Any disagreement was resolved by consensus with other senior reviewers (APA and LND) and the final source list was created, with senior reviewers blinded to reviews of each other and primary reviewers (MB and SS). The study selection process is illustrated in Figure 2 . Tables 1 and 2 provide descriptions of the final 10 papers published from April 2017 to December 2021 included in this review.

a HR: heart rate.

b STC: step count.

c STAI: State-Trait Anxiety Inventory.

d EDA: electrodermal activity.

e EMA: ecological momentary assessment.

f PHQ-9: Patient Health Questionnaire.

g HRV: heart rate variability.

h RHR: resting heart rate.

i RR: respiratory rate.

j Not available.

k PSS: Perceived Stress Scale.

l MBI-HSS: Maslach Burnout Inventory–Human Services Survey.

m PROMIS : Performance of the Patient-Reported Outcomes.

n SC: skin conductance.

a STAI: State-Trait Anxiety Inventory.

c HR: heart rate.

d HRV: heart rate variability.

Extraction Strategy

Data extraction was mostly completed by a single researcher (MB). Other researchers (APA and SS) helped refine data extraction and review the tables. The following information was extracted from the papers and is included in Tables 1 and 2 : sample population (size and occupation), anxiety, burnout, stress or depression assessment instrument, additional measurements used, wearable device used, measured physiological variable, study duration, primary findings, and the author-determined quality assessment score.

Quality Assessment

The methodological quality of nonrandomized or observational studies was assessed by 2 reviewers (MB and SS) using the Newcastle Ottawa Quality Assessment Form for Cohort Studies [ 42 ]. The Newcastle-Ottawa Scale is a validated scale of 8 items in 3 domains: selection, comparability, and outcome. Studies are rated from 0 to 9, with those studies rating 0-2 (poor quality), 3-5 (fair quality), and 6-9 (good or high quality). All 10 studies received a Newcastle-Ottawa Scale rating of good or high quality.

Roles of Participating Health Care Professionals

Among the 10 reviewed studies, 8 were conducted in the United States, 1 study was conducted in Portugal [ 35 ], and another one was conducted in Canada [ 36 ]. Seven studies recruited either resident physicians (postgraduate medical trainees), practicing physicians, or a combination of both, primarily within the same specialty (eg, orthopedic surgery and emergency medicine). Two studies recruited registered nurses [ 32 , 40 ] and 1 study recruited medical students [ 35 ]. Sample sizes ranged from 20 to 775 participants per study (see Table 1 ). Only 3 studies had more than 100 participants [ 32 , 33 , 40 ].

Wearable Devices, Physiological Variables Collected, and Duration of Observation

Table 1 summarizes the sample population, sample size, physiological variables collected from wearable devices, and psychometrics used in the 10 studies. The devices used, length of data collection, and primary findings are listed in Table 2 . Out of the 10 studies, 9 used wrist-worn biosensors, such as the Fitbit Charge (n=4) [ 32 , 33 , 35 , 40 ] WHOOP (n=2) [ 34 , 38 ], Basis B1 (n=1) [ 35 ], Empatica E4 (n=1) [ 40 ], and the Microsoft Smart Band 2 (n=1) [ 35 ]. Sensors embedded within wrist-worn biosensors included optical heart sensors, electrical heart sensors, accelerometers, and skin temperature sensors. The other device used was a HealthPatch, an adhesive patch with 2 ECG electrodes used to measure HR and HRV. A variety of physiological variables were collected, with sleep being the most common, measured in 7 studies. Studies ranged in length of data collection, from a single 12-hour shift to a 14-month period. Only 5 studies collected data for more than 10 weeks [ 32 - 34 , 37 , 39 ].

Methodological Wearable Data Reporting

Only 2 studies explicitly stated the sampling frequency used when processing data from the wearable device [ 33 , 39 ]. Four of the studies discussed how the data were processed; however, the level of detail varied [ 32 , 33 , 35 , 40 ]. Three of the studies indicated the cutoff values for physiological variables or explained how outliers were addressed [ 32 , 33 , 40 ]. Only 4 studies explicitly stated how much raw data were retrieved from the devices [ 32 - 34 , 36 ].

Reported Relationships Among Burnout, Depressive Symptoms, Stress, and Anxiety With Data Obtained From Wearable Devices

Of the 10 included studies, 6 included a measure of burnout ( Table 1 ) [ 34 , 36 - 40 ]. Four of these 6 studies used the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) [ 43 ]. In a cross-sectional study of 114 nurses, no relationship was found between MBI-HSS score and skin conductance, a measure of autonomic nervous activity, collected through an Empatica E4, for 1 shift [ 40 ]. Another study investigated the relationship between MBI-HSS score, self-reported work hours, physical activity, and sleep, as measured by a Fitbit, in a cohort of 59 residents [ 36 ]. No relationship was found between the change in burnout score and data collected from the Fitbit over 2 weeks. In the third study, no relationship was found between MBI-HSS score and sleep, as measured by a WHOOP, over the course of 4 weeks [ 38 ]. Last, in a study of 75 medicine and psychiatry residents, no relationship was found between burnout score and sleep or activity levels, as measured by Basis B1 health-tracking device, during their first 6 months of residency [ 39 ].

Two studies measured burnout using scales other than the 22-item MBI-HSS (widely considered the gold standard) [ 34 , 37 ]. In a study of 21 orthopedic residents and surgeons, no association was found between baseline abbreviated MBI scores and WHOOP measures collected over 12 weeks [ 34 ]. The final study investigated the association between burnout, as measured by a commonly used single-item measure, and sleep and activity level, as measured by a Fitbit. In this study, of 28 emergency medicine residents, there was no association between burnout scores and sleep or activity levels over the course of the 16-week study [ 37 ].

Depressive Symptoms, Stress, and Anxiety

A 14-month study of 775 medical residents found a relationship between depressive symptoms, as measured by the 9-item Patient Health Questionnaire [ 44 ], and step count (STC) and sleep as measured by a Fitbit Charge 2 [ 33 ]. Medical residents whose depressive symptoms worsened over the period of the study had a significantly higher skew in their hourly STC distributions and spent less time in bed than those whose symptoms did not worsen. In a study of 83 medical students, Perceived Stress Scale-4 scores related to HR and HRV, were measured by a Microsoft Smartband 2, at baseline and during an examination [ 35 ].

In a 10-week study of 113 nurses led by Feng et al [ 32 ], no relationship was found between the level of anxiety, as measured by the State-Trait Anxiety Inventory (STAI) [ 45 ], and wearable sensor data (eg, sleep and HR) collected using Fitbit Charge 2 smartwatch. Weenk et al [ 41 ] conducted a study of 20 surgeons and surgical residents who completed an abbreviated version of the STAI before and after performing surgery, and wore a HealthPatch. This adhesive patch calculates stress using an HR and HRV-dependent algorithm for 48 to 72 hours [ 41 ]. There was no correlation found between the STAI score and HealthPatch data.

Device Use Compliance and Experience

Seven studies reported data on participant adherence or experience with wearable devices. Chaukos et al [ 39 ] reported that 25 (40%) of their participants wore their device for more than 50% of the time for the first 3 months of the study, while another 13 (21%) participants wore the device for more than 75% of the time for the first 3 months. Other studies, such as one conducted by Sochacki et al [ 38 ] reported that of the 26 participants, 5 did not complete the minimum WHOOP compliance (4 weeks). Surgeons involved in a study by Jevsevar et al [ 34 ] reported a high percentage of device compliance at 83.2% of the total collection window, similar to the 93% compliance rate reported by Mendelsohn et al [ 36 ] and Sochacki et al [ 38 ]. Weenk et al [ 41 ] reported that 6 of 20 individuals experienced problems with their HealthPatch, similar to Marek et al [ 37 ] who reported 1 of 30 participants dropped out due to fitness tracker intolerance. Problems included connection failure (n=2), loss of skin contact (n=2), and skin irritation (n=2). Feng et al [ 32 ] noted similar compliance between day-shift participants and night-shift participants (number of recordings day-shift: mean 44.6, SD 3.1 sessions; night-shift: mean 45, SD 20.2 sessions).

Risk of Bias

A risk of bias of assessment was completed for the 8 cohort studies and 1 cross-sectional study ( Figure 3 ). While the risk of bias was generally low across the studies, none included a comparison group of participants who did not wear a device.

To our knowledge, this is the first scoping review to investigate the use of wearable technologies for the prediction of burnout, anxiety, depression, and stress in HCPs. Among the 10 studies identified, a range of wearables collected data on HR, HRV, respiratory rate, skin temperature, sleep, and activity levels from a single shift of work and up to 14 months of data collection in relatively small samples of physicians, medical students, and nurses. In these studies, no relationships were found between collected physiological data from wearables and burnout or anxiety. One study reported a relationship between STC, time in bed, and depressive symptoms, and another between HR, HRV, and acute stress (during an examination). Identified studies had methodological limitations, including short duration which limits the capture of naturalistic variations in the workplace stressors.

In this review, 3 studies measured HRV [ 34 , 35 , 41 ] and only 1 found a significant relationship between HRV and acute stress. A previous systematic review involving non-HCPs identified 2 studies demonstrating relationships between HRV and acute stress-induced conditions and 1 study demonstrating a relationship between HRV and stress levels measured by catecholamine levels [ 31 ]. This previous systematic review also identified 1 study where in a setting of laboratory-induced stress, HRV parameters related to STAI score. These studies, however, differed substantially from the ones included in this review. For example, none of them collected physiological data longer than 24 minutes, stress was induced in a laboratory setting (vs occurring naturally in a work setting), and only 1 study compared physiological data with a self-reported stress measure (ie, STAI score).

Given these early findings, further research focusing on the following elements of rigor are warranted. First, the length of observation should be long enough (at least 2 or 3 consecutive quarters of a calendar year) to allow sufficient quanta of wearable data to capture fluctuations in and chronicity of workplace stress. Studies should systematically collect data using validated instruments measuring burnout (eg, MBI-HSS [ 43 ]), depression (eg, Center for Epidemiologic Studies Depression Scale [ 46 ] and Patient Health Questionnaire-9 [ 44 ]), and anxiety (eg, General Anxiety Disorder-7 [ 47 ]). Investigators may also want to consider designing cohorts comprising groups of HCPs defined by their type of medical specialty or practice location. For example, it is possible that workplace stressors, patient acuity, and job demand fluctuate between primary care and surgical specialties and between outpatient practices and hospital-based practices. Hence, the burnout biomarkers may vary between practices. Considering that burnout is defined as when job demands exceed job resources, it is possible that the workplace (eg, patient acuity and hospital bed size) and related staffing factors (eg, workload, shift length, and availability of support staff) impact physiological biomarkers collected from wearables. Hence, future studies should consider collecting organizational variables to better understand the systemic contributors of burnout. Additionally, given the era of decentralized health care practice (eg, nontraditional shift days/hours and remote care with augmented reality), studies engaging with HCPs may benefit from no-contact passive monitoring and a digital app interface for survey collection (ie, decentralized trail). Finally, there is a bioethics component to understand how wearables can be successfully integrated into workforces’ burnout management. Greater attention needs to be paid to participant engagement, including addressing comfort with wearing the device, resolving discrepancies in wearable-derived data versus self-reported data, and understanding factors that influence perceptions of fatigue but not recorded sleep [ 37 , 48 , 49 ].

The use of wearables to detect the functioning states of human beings is an active and rapidly evolving field. Several wearable-based studies have been shown to aid in the detection of mental health conditions or resilience in quality of life [ 50 ] through mindfulness practices including physical activity [ 51 ] and sleep [ 52 - 54 ] monitoring. Prior work has demonstrated that aspects of physical functioning when combined with data during the day could predict variations in aspects of QoL and mental well-being [ 55 - 58 ]. Work by Campbell et al [ 59 - 64 ] has demonstrated the ability of daily journaling, wearables, and mobile assessments to detect depressive symptoms and mental states in patients with schizophrenia. These prior efforts in the field of mental health and the work summarized in this scoping review demonstrate the promise of wearables in predicting states of one’s functioning, including burnout. However, a consensus is lacking on the best approaches to collecting, processing, and reporting physiological data, much like CONSORT (Consolidated Standards of Reporting Trials) [ 65 ] for reporting randomized trials and STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) [ 66 ] guidelines for reporting observational studies. Standardization of variables should include the creation of a guideline for reporting the sampling frequency, device adherence, and other information regarding device parameters that impact data collection. Such standardization would assist with generalizing findings, validating predictive algorithms, informing meta-analysis, and the use of data for retraining predictive models regardless of the wearable’s make and model. Additionally, there needs to be consensus around approaches to address bioethics, privacy, and confidentiality concerns of participants [ 67 , 68 ]. Predictive technologies, informed by personal biometric or physiologic data, may help improve work conditions but could also place individuals’ privacy or perhaps even their job security at risk.

This study has limitations. Only studies that included physicians, resident physicians, medical students, and nurses and were published in English were included. Following the 2019 pandemic, physicians identifying as 2 or more races experienced the highest levels of burnout onset, according to a report by the American Medical Association [ 69 ]. Furthermore, there are known disparities in the access to, and the use of digital health technologies in underrepresented minorities [ 70 , 71 ]. Therefore, it is vital to understand the factors that cause burnout in these groups of professionals and remove barriers to access to personalized wellness technologies using wearables that may help understand and mitigate burnout. In the context of the use and access of digital health for burnout, 8 of the 10 studies reported the gender breakdown of participants, and only 1 study reported the race of their participants. With the urgent need to broaden access to digital health solutions to study and understand burnout, future efforts should (1) follow reporting guidelines (eg, set by National Institutes of Health in the Human Subjects sections) to report on participant characteristics by ethnicity, race, and gender, and (2) innovate study procedures (eg, decentralized protocols) that improve the recruitment and engagement of underrepresented minorities in digital health studies of burnout. Although we sought to include validated measures of burnout, stress, depression, and anxiety, the instruments used in the studies varied in their psychometric strengths. Finally, most studies lacked power calculations, making findings, effect sizes, or impact of dropouts difficult to interpret from the perspective of the generalizability of biomarkers.

Despite the popularity of wearable devices, only 10 studies were identified that explored relationships between physiological data and burnout, depressive symptoms, stress, or anxiety. Most of these studies had substantial methodological limitations, and nearly all reported limited data collection and processing information, participant experience with the wearable device, and device compliance. Standardizing study procedures, common data elements, and reporting of wearable data are needed to strengthen the rigor of digital health studies. Addressing these limitations will result in improvements in wearable device research, including data standardization and reporting, that will validate their use in providing early intervention for HCP wellness. Additional research is warranted to explore the potential of wearable devices, perhaps augmented with other system-level data (eg, work shift lengths and absenteeism), to predict burnout and other forms of distress, hopefully leading to meaningful action before it has an adverse impact on HCPs and patient care.

Acknowledgments

This study was partially supported by the Mayo Clinic Summer Undergraduate Research Fellowship, National Science Foundation (grant 2041339); National Institutes of Health (grant R01 NR020362); the Mayo Clinic Center for Individualized Medicine, and the Mayo Clinic Center for Clinical and Translational Science. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or National Institutes of Health.

Authors' Contributions

APA and LND contributed equally as co-Corresponding Authors. APA may be contacted at [email protected].

Conflicts of Interest

PC has received research support from the National Institutes of Health (NIH), National Science Foundation (NSF), Brain and Behavior Research Foundation, and the Mayo Clinic Foundation. PC has received research support from Pfizer, Inc. He has received equipment support from Neuronetics, Inc, and MagVenture, Inc. He received grant-in-kind supplies and genotyping from Assurex Health, Inc for an investigator-initiated study. He served as the primary investigator for a multicenter study funded by Neuronetics, Inc and a site primary investigator for a study funded by NeoSync, Inc. PC served as a paid consultant for Engrail Therapeutics, Sunovion, Procter and Gamble Company, Meta Platforms, Inc, and Myriad Neuroscience. PC is employed by the Mayo Clinic. LD is a coinventor of the Well-Being Index and its derivatives which Mayo Clinic has licensed. LD receives royalties. WB’s research has been supported by the NIMH, NINR, NSF, the Blue Gator Foundation, the Watzinger Foundation, and the Mayo Foundation for Medical Education and Research. He has contributed chapters to UpToDate concerning the pharmacological management of patients with bipolar spectrum disorders. MCs research has been supported by NSF and the Mayo Foundation for Medical Education and Research.

Search strategy.

PRISMA-ScR checklist.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 24.06.23; peer-reviewed by T Pipe, P Punda; comments to author 01.12.23; revised version received 01.01.24; accepted 20.03.24; published 25.06.24.

©Milica Barac, Samantha Scaletty, Leslie C Hassett, Ashley Stillwell, Paul E Croarkin, Mohit Chauhan, Sherry Chesak, William V Bobo, Arjun P Athreya, Liselotte N Dyrbye. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Longer-term challenges for fiscal policy in the euro area

Prepared by Edmund Moshammer

Published as part of the  ECB Economic Bulletin, Issue 4/2024 .

1 Introduction

In the future, various longer-term challenges are likely to exert pressure on public finances in the euro area. On top of the existing fiscal burdens – as reflected in the high debt ratios in a number of euro area countries, which were exacerbated by the pandemic and the subsequent energy crisis – there are several important longer-term challenges for fiscal dynamics. This article starts by reviewing some of the most important challenges and discussing their fiscal relevance, with a focus on demographic ageing (Section 2), the end of the “peace dividend” (Section 3), digitalisation (Section 4) and climate change (Section 5). Acknowledging the uncertainties surrounding any quantification of these challenges, Section 6 then presents some tentative – purely indicative – estimates of the additional fiscal effort that could be required to ensure the long-term sustainability of public finances in the presence of such developments. The implications of digitalisation are excluded from that exercise, given the particular uncertainty that surrounds their quantification. Section 7 then provides some concluding remarks.

2 Fiscal costs of ageing societies

The euro area is experiencing demographic ageing. The region is witnessing a significant decline in fertility rates, coupled with steady increases in life expectancy, resulting in an ageing population. At the level of the European Union as a whole, average remaining life expectancy at the age of 65 has increased over the last two decades, rising from 17.8 years in 2002 to 19.5 years in 2022. [ 1 ]

This demographic ageing presents challenges for government finances. With the number of elderly citizens increasing relative to the working-age population, pay‑as-you-go pension systems face mounting financial pressures. Furthermore, ageing populations typically require more extensive healthcare services and long‑term care.

Developments in ageing-related public spending vary across euro area countries. The recently published 2024 Ageing Report provides long-term projections for the key drivers of ageing-related costs and their components (which comprise pensions, health care, long-term care and education) in EU Member States over the period 2022-2070. [ 2 ] In the baseline scenario, which assumes unchanged policies, the euro area on aggregate will face an increase in ageing-related expenditure of 1.4 percentage points of GDP relative to today, but this could increase to 4.0 percentage points in a risk scenario. And even in the baseline scenario five countries may need to increase their ageing-related spending by over 3 percentage points of GDP (Chart 1). The increase in the public cost of pensions has the highest variability across countries, given the varied nature of demographics and pension system arrangements at country level (e.g. the extent to which retirement ages are linked to life expectancy). The increased burden of ageing will require policy reforms or structurally increased savings in other areas.

Additional fiscal efforts required owing to ageing populations

(percentages of GDP)

Sources: 2024 Ageing Report and ECB calculations. Notes: This chart shows, for each component, the average increase in ageing-related costs from 2023 to 2070, weighted by the cumulative product of the reciprocal interest-growth differential. This increase can be interpreted as the constant additional budget balance needed in all years to meet the fiscal burden of an ageing population. Public spending on pensions is net of tax revenues.

3 Fiscal costs of the end of the “peace dividend”

Russia’s war of aggression against Ukraine has prompted far-reaching discussions on security, military spending and geopolitical stability. NATO members in the euro area have responded to this challenge by announcing and implementing large increases in defence spending, which represents a significant reversal of previous trends. As the Cold War thawed, all major economies reduced their defence expenditure (Chart 2, panel a). The United States and the United Kingdom more than halved their spending, reducing it from over 10% of GDP in the 1950s to less than 5% as of the 1990s. Germany and France, in turn, reduced their spending from over 4% of GDP to less than 2% today. Using this “peace dividend”, governments refocused their budgets, targeting new priorities such as increased social welfare spending. [ 3 ] After Russia’s annexation of Crimea in 2014, all NATO members agreed to spend at least 2% of GDP on defence. [ 4 ] Since then – and especially following Russia’s full-scale invasion of Ukraine – the vast majority of euro area countries have increased their defence expenditure (Chart 2, panel b). If all euro area countries (including those that are not NATO members) were to increase their defence expenditure to 2% of GDP, this would result in an estimated €71 billion of additional spending annually – equivalent to 0.5% of euro area GDP. [ 5 ]

Public spending on defence

a) Long-term decline since the peak of the Cold War

(spending as a percentage of GDP, 1954-2022)

b) Changes since Russia’s annexation of Crimea in 2014

(spending as a percentage of GDP)

Sources: Stockholm International Peace Research Institute (SIPRI), NATO and Eurostat. Notes: In panel a, data are sourced from SIPRI. In panel b, the asterisks denote non-NATO countries, where data are sourced from Eurostat and the blue bars refer to 2022. Data for other countries are sourced from NATO (press release from 7 July 2023).

Additional defence spending could potentially increase GDP growth in the EU, with positive implications for fiscal sustainability in the longer term, if it (i) is concentrated in R&D-intensive investment, (ii) does not crowd out other productive investment, and (iii) focuses on EU-based sources. According to the European Commission, using EU-based suppliers in defence contracts and, accordingly, shifting towards sourcing defence equipment and services from within the EU’s internal market could stimulate economic growth in the longer term. The Commission recently announced the European Defence Industrial Strategy, which encourages EU Member States to make strategic investments in their defence capabilities while promoting intra-EU collaboration and cooperation. [ 6 ] One of the key pillars of this strategy involves ensuring that defence products are readily available through the European Defence Technological and Industrial Base. This is about incentivising Member States to procure defence equipment and services from EU suppliers, thereby strengthening domestic defence industries, reducing reliance on external sources and enhancing resilience to any potential geopolitical shocks. According to the Commission, this has the potential to support the growth and development of EU-based defence companies, fostering innovation, job creation and technological advancement within the region. It would also produce multiplier effects across different sectors and ultimately increase fiscal revenues.

The economic impact of Russia’s war of aggression extends far beyond the realm of military spending. In the two years since the invasion of Ukraine, EU Member States and institutions have committed an estimated 0.55% of the EU’s annual GDP in bilateral short-term support. [ 7 ] Furthermore, the EU has also established a €50 billion Ukraine Facility covering the period 2024‑27. The World Bank estimates that Ukraine’s overall recovery and reconstruction needs will total around $486 billion over the next ten years. [ 8 ]

Moreover, in 2022 and 2023, governments were also forced to react to the resulting energy crisis and the high levels of inflation that followed. Indirectly, the war in Ukraine triggered a large temporary fiscal policy response at European level aimed at counteracting the high energy prices and the ensuing inflation, thus pointing to the multifaceted challenges posed by the ongoing conflict. [ 9 ] While governments should continue to roll back these energy-related support measures in 2024 to allow the disinflation process to proceed sustainably, the longer-term challenge of improving energy security in the EU will remain.

As the war in Ukraine is still ongoing and the geopolitical landscape is also characterised by instability in the Middle East and other parts of the world, the full long-term fiscal cost of the end of the peace dividend remains uncertain and is very difficult to estimate. For instance, the fragmentation of global trade could have severe implications for producers and consumers alike. If firms restructure their production chains in order to source inputs from countries that are geographically closer, rather than those with the most efficient production capabilities, their production costs will typically increase. [ 10 ] While the indirect fiscal effects are very difficult to quantify, they could be sizeable. [ 11 ] As a result, there continues to be significant uncertainty regarding the long-term fiscal consequences of these developments.

4 Fiscal costs of closing the digitalisation gap

The rising importance of digital value chains and transformative technologies is necessitating substantial investment in digital infrastructure and digital public services in order to maintain competitiveness. Before establishing the Recovery and Resilience Facility (RRF) in 2021, the European Commission estimated the EU’s digital investment gap vis-à-vis the United States and China at €125 billion per year (equivalent to around 0.9% of the EU’s GDP), calling for the resulting costs to be shared between the private and the public sector. [ 12 ] This will involve significant investment in digital infrastructure, particularly telecommunications networks.

In 2022, the EU adopted the Digital Decade Policy Programme 2030, a set of targets and objectives aimed at catching up in the area of digital transformation, supported by public investment. Around 70% of all funding for that programme – €117 billion in total – will come from the RRF, with €16.6 billion having been disbursed to fund the digital transition by March 2024 (Chart 3, panel a). [ 13 ] Under EU rules, at least 20% of all disbursed RRF funds must be spent on the digital transition. However, most Member States are exceeding this minimum threshold in their revised Recovery and Resilience Plans, with country‑specific allocations of RRF funds to the digital transition ranging from the minimum of 20% in Croatia and Slovenia to 48.1% in Germany. The degree of digitalisation still varies considerably across countries. In order to gauge progress towards the targets set, a Digital Economy and Society Index (DESI) has been devised (Chart 3, panel b). This is a composite index comprising 32 sub-indicators, 11 of which are directly linked to the Digital Decade. The short time horizon limits any causal inference, but estimates suggest that there is a significant correlation between DESI scores and GDP per capita, further reinforcing the ongoing Digital Decade agenda. [ 14 ] Digital investment that results in the strengthening of economic growth may, ultimately, also boost fiscal revenues.

Digital RRF expenditure and DESI scores

a) RRF disbursements targeting digital objectives: breakdown by policy area

(EUR billions; as at March 2024)

b) DESI 2023 scores

(as a percentage of target scores for 2030)

Sources: European Commission and ECB calculations. Note: In panel b, the target for each of the four broad categories is a maximum score of 25 points.

5 Fiscal effects of climate change

Climate change poses major fiscal challenges for euro area economies. From the direct costs of extreme weather events to the broader economic implications of transitioning to a low-carbon future, the fiscal impact of climate change is multifaceted and requires comprehensive analysis and action. As outlined in the ECB’s climate and nature plan 2024-2025, central banks will need to improve their understanding of these drivers in order to deliver on their core objectives.

Extreme weather events – which may increase in frequency and severity as a result of climate change – pose immediate and tangible risks. The economic costs of floods, storms, heatwaves and droughts have increased sharply in recent decades, placing a substantial financial burden on governments. [ 15 ] Costs relating to disaster relief, infrastructure repair and healthcare services in the aftermath of such events place strain on public finances, diverting resources from other essential areas. At the same time, the burden of climate change is distributed unevenly across euro area countries. For example, the European Commission’s PESETA IV project estimates that welfare losses from climate change in southern Europe will be several times larger than in the north of Europe, mostly because of higher temperatures and water scarcity. [ 16 ] This uneven burden is further exacerbated by the fact that some countries which have historically suffered significant losses also have large insurance protection gaps. [ 17 ] Against that background, a recent European Commission discussion paper sheds light on the potential fiscal repercussions of extreme climate events. [ 18 ] The paper estimates that in a scenario where temperatures rise by 2°C globally in the long term, eight euro area countries could see their public debt-to-GDP ratio rise by over 2 percentage points by 2032 owing to extreme weather events.

Transitioning to a low-carbon economy entails significant upfront costs and policy challenges. Mitigation measures (such as investment in renewable energy infrastructure, energy efficiency improvements and other emission reduction strategies) require substantial financial resources and long-term planning. Green investment, both public and private, will be essential in order to facilitate the transition to a sustainable economy. [ 19 ] Carbon-pricing mechanisms such as carbon taxes offer a potential source of revenue that could offset some of the fiscal costs of climate policies. [ 20 ] Recent IMF estimates based on a New Keynesian dynamic general equilibrium model suggest that primary deficits in advanced economies could increase by around 0.4 percentage points of GDP over the next few decades as a result of a policy package designed to achieve net-zero emissions in 2050. [ 21 ] However, this assumes that a large share of public spending on green investment and subsidies is financed through carbon tax revenues.

The macroeconomic and financial consequences of climate change and related policies can also have an indirect impact on public finances. The economic consequences of climate change (which include productivity losses, disruptions to supply chains and declines in agricultural output) can dampen GDP growth. The resulting contraction in economic activity can, in turn, erode government revenues and result in higher debt servicing costs. Model simulations conducted by the Network of Central Banks and Supervisors for Greening the Financial System (NGFS) suggest that some euro area countries could experience significant real output losses. When conducting such analysis, the cost of different transition policies [ 22 ] needs to be set against the reduction in physical risks from climate-related events. For instance, in the “net-zero by 2050” scenario, which limits global warming to 1.5°C through stringent climate policies and innovation, real output losses are fairly limited (Chart 4, panel a); however, the costly transition policies lead to spikes in inflation and relatively persistent increases in interest rates (which rise by 1 percentage point on average; Chart 4, panel b). Increases in interest rates tend to reflect the inflationary pressure created by carbon prices, as well as increased demand for investment. [ 23 ] The higher interest rates in the NGFS’s “net‑zero by 2050” scenario are the single most important driver of the long-term interest-growth differential. For instance, for a country with debt totalling 60% of GDP, a 1 percentage point increase in the interest-growth differential would, over time, result in the annual debt service burden rising by 0.6 percentage points of GDP. Naturally, these simulations are based on strong assumptions and contain a large degree of model uncertainty. [ 24 ] Several aspects – including the drivers of rising long-term interest rates and the role of monetary policy – need to be investigated further, and the ECB is actively contributing to those research efforts.

Under EU rules, at least 37% of all RRF funds disbursed must be spent on the green transition. While Member States often choose to spend significantly higher shares (ranging from 37.4% in Lithuania to 68.8% in Luxembourg and Malta), RRF funds can only cover a limited proportion of a country’s climate expenditure needs.

Simulating the impact of climate change under different transition scenarios

a) Impact on real GDP growth rates

(percentage point changes; averages for the period 2024-50)

b) Impact on long-term interest rates

Sources: NGFS long-term scenarios (Phase IV) and ECB calculations. Notes: See footnote 22 for a description of NGFS scenarios. Countries are ordered on the basis of the average cross-scenario impact. Data refer to geometric means over the period 2024‑50 and are not available for Croatia, Cyprus, Luxembourg or Malta. NGFS simulations employ three different models (GCAM, MESSAGEix-GLOBIOM and REMIND-MAgPIE), and the results presented here are averages of the findings for those three models.

6 Cumulative impact

This section provides a rough and purely indicative estimate of the possible fiscal burden arising from the developments described in the previous sections. A single indicator aggregates the various components (Chart 5 and Box 1), estimating the fiscal adjustment that each euro area country would need to implement as of 2024 and maintain throughout the simulation horizon. [ 25 ] The shared long-term target is a government debt-to-GDP ratio of 60% (as referred to in the Treaty) by 2070. [ 26 ] This fiscal gap measure is indicative and requires further analysis and interpretation to reach normative conclusions. Countries will need to ascertain and execute their respective adjustment paths. Moreover, the implementation of more ambitious structural reforms – notably those that support long-term growth – would help to reduce the fiscal burden, which is computed here on the basis of currently projected long-term growth rates. This is also the reason why the issue of digitalisation is not included in this exercise, as the benefits of digitalisation could potentially compensate for some of the fiscal costs incurred.

Overview of fiscal efforts required in response to specific challenges

Sources: 2024 Ageing Report, European Commission’s Debt Sustainability Monitor 2023, NGFS Phase IV simulations, IMF’s October 2023 Fiscal Monitor, NATO, Eurostat and ECB calculations. Notes: The chart depicts the required immediate and permanent one-off improvement in the ratio of structural primary balance to GDP to bring the debt ratio to 60% of GDP by 2070, incorporating financing for any additional expenditure until 2070 arising from an ageing population, defence and climate. See Box 1 for a description of the methodology.

Achieving a government debt-to-GDP ratio of 60% by 2070 from today’s debt levels would require euro area governments to immediately and permanently increase their primary balances by 2% of GDP on average (dark blue and yellow bars in Chart 5). 16 euro area countries would require fiscal adjustments just to maintain their current debt levels, with necessary average savings of 1.4% of GDP (blue bars). Going further and reducing debt to 60% of GDP would, on average, require additional savings totalling 0.6% of GDP in the euro area, with high-debt countries having the largest adjustment needs (yellow bars).

The additional challenges discussed above, excluding digitalisation, could widen the euro area’s average fiscal deficit by approximately a further 3% of GDP. [ 27 ] Of those challenges, demographic ageing is expected to result in the largest fiscal burden over the next five decades, potentially necessitating additional spending of up to 4% of GDP for some countries, and 1.2% for the euro area on average. As regards the NATO target for defence expenditure, four of the NATO members in the euro area are already spending the targeted amount of 2% of GDP, while the other 12 face additional burdens of up to 1% of GDP, resulting in an average burden of 0.5% of GDP at euro area level. For the four non-NATO countries – Ireland, Cyprus, Malta and Austria – there is no formal requirement to spend a specific amount on defence. However, Chart 5 plots the gap vis-à-vis 2% of GDP in the light of the changing geopolitical environment. [ 28 ] For climate change, assuming a “net-zero by 2050” scenario which limits global warming to 1.5°C, we estimate an average cost increase totalling 1.1% of GDP at the level of the euro area as a whole. This is driven by the 0.4 percentage point increase in the primary deficit-to-GDP ratio that was calculated by the IMF and the additional interest burden on debt stocks that was projected by the NGFS. [ 29 ]

The necessary fiscal adjustment is large by historical standards, but not without precedent. At the same time, for all of the challenges discussed above, there is considerable cross-country heterogeneity in the required fiscal efforts, with estimates of gaps ranging from 0.5% to almost 10% of GDP. In the past, large fiscal adjustments were mainly observed in response to major fiscal crises and in the presence of sizeable debt overhangs. Belgium, Ireland and Finland maintained cyclically adjusted primary surpluses of over 5% of GDP on average for more than a decade in the 1990s and early 2000s. [ 30 ] In some countries, the fiscal pressures discussed may not strengthen in the short term; however, there is no room for complacency, as the longer the adjustment is postponed, the larger the eventual adjustment cost will be.

Moreover, additional fiscal burdens may well emerge in the medium term. For instance, the model-based simulations used in this article exclude the digitalisation gap, the long-term implications of which are still hard to grasp. Furthermore, one does not need to go back very far in time to find a large fiscal shock appearing out of the blue: the euro area’s government debt-to-GDP ratio increased by a total of 13 percentage points in 2020 in response to the COVID-19 pandemic. At the same time, the simulation of climate change is based on simplified assumptions and on the unlikely premise that limiting global warming to 1.5°C is still feasible. It also does not capture the impact of societal repercussions (such as conflict), tipping points or macroeconomic effects (such as changes to prices and productivity). This suggests that there could be substantial additional fiscal costs associated with climate change. [ 31 ] On the upside, however, the simulation may understate the potential positive economic side effects of increased public spending, such as spending on digitalisation. While the demographic ageing and climate change scenarios are built on a set of internally consistent assumptions, which also capture macroeconomic effects, the modelling of defence spending does not take account of the possible macroeconomic impact (e.g. the potential for the benefits of technological progress to spill over from the defence sector to the wider economy).

Box 1 Methodology of the fiscal gap indicator

In order to make the diverse fiscal long-term pressures comparable in a single indicator per country, we compute the immediate and permanent improvement in the structural primary balance required to bring the debt ratio to 60% of GDP by 2070. In addition to accounting for the adjustment need to stabilise and then reduce the initial debt level to the target level, the indicator incorporates financing for any additional expenditure arising from an ageing population, defence needs and climate change.

Deriving the fiscal gap and its components

Government debt in euro at the end of any given year is the sum of four components: (i) the debt at the end of the previous year, (ii) the interest accrued on that debt, (iii) the negative primary balance, and (iv) any debt-deficit-adjustment (DDA). Expressed in terms of GDP, in an economy with a balanced budget and zero DDA, debt-to-GDP grows every year proportional to the interest-growth differential (IGD). The IGD is the ratio between (i) one plus the average nominal interest rate and (ii) one plus the nominal GDP growth rate. However, the development of government debt is also determined by future primary balances and any DDA. From the above accounting identity we can apply the net present value (NPV) approach, discounting future flows by the annual IGDs and thus making them comparable across different time horizons. For instance, for reducing the current debt ratio by a given percentage, a government could apply a certain amount of savings in the current year or the same savings discounted by IGD in the following year. More generally, the difference between (i) the NPV of government debt as a percentage of GDP at a future date, and (ii) current government debt equals the NPV of the (negative) primary balances plus any DDA flows between today and the future date.

We define as our fiscal gap indicator the necessary permanent improvement in the ratio of the structural primary balance to GDP as of 2024 to reach a government debt of 60% of GDP by 2070. To determine the NPV of the fiscal flows needed to meet the target, we take (i) the 2023 government debt as a percentage of GDP, (ii) subtract the NPV of 60% of GDP debt discounted from 2070 to 2023, and (iii) add the NPV of negative primary balances plus DDA flows from 2024 to 2070. This NPV is then converted into a steady flow of primary balances that guarantee the attainment of the final target.

This approach can also be used to provide a breakdown of the fiscal gap into the different drivers. Looking at the equation below, we split the effort to reach the 60% debt ratio by 2070 into five components. These are the adjustments needed to (i) achieve the 2023 debt ratio ( d 0 ) by 2070 taking into account the starting primary balance and any DDA, (ii) reduce the 2070 debt ratio to 60% of GDP, (iii) cover ageing-related costs, (iv) cover additional defence expenditure needs, and (v) cover climate change-related costs.

g a p = ∑ 1 a t - 1 d 0 - d 0 a T - ∑ p b B a s e t - d d a t a t + d 0 - 60 % a T + ∑ a g e t a t + ∑ d e f t a t + ∑ c l i m a t e t a t

In this equation, a t and a T are the NPV discount factors at period t and in 2070 respectively, and Σ refers to the sum of flows from 2024 to 2070.

Assumptions for fiscal pressures and future interest-growth differentials

Our approach is similar to the S1 indicator presented in the European Commission’s Debt Sustainability Monitor (DSM) 2023, also with regard to the assumptions for primary balances, the interest-growth differential and ageing costs. [ 32 ] There are, however, three notable differences in the approach used here. First, the one-off fiscal adjustment is assumed to happen in 2024, compared with a two-year delay in the DSM. Second, we assume a constant structural primary fiscal balance over the projection horizon in order to avoid double-counting of legislated climate and defence measures. Third, we include these two additional components, which do not feature in the Commission’s indicator.

7 Conclusions

Issues such as demographic ageing, increased defence expenditure, digitalisation and climate change will result in significant fiscal burdens in the decades ahead. These developments will be challenging enough in isolation, and countries will face all of them simultaneously. Consequently, action needs to be taken today – especially in high-debt countries facing elevated interest rates and the associated risks. [ 33 ] Economic policies should seek to gradually reduce high levels of public debt and prepare for the future, which will also help to ensure a sound environment for the conduct of the euro area’s single monetary policy.

This figure peaked at 20.2 years in 2019 (i.e. pre-pandemic).

See European Commission, “ 2024 Ageing Report: Economic & Budgetary Projections for the EU Member States (2022-2070) ”, European Economy – Institutional Papers , No 279, April 2024.

See the article entitled “ Social spending, a euro area cross-country comparison ”, Economic Bulletin , Issue 5, ECB, 2019.

Only three of the 32 current NATO members achieved that target in 2014. By 2023, however, the number had risen to 11, and it is expected to reach 18 by the end of 2024. See “ Pre-ministerial press conference by NATO Secretary General Jens Stoltenberg ”, 14 February 2024.

See also Freier, M., Ioannou, D. and Vergara Caffarelli, F., “EU public goods and military spending”, Box 16 in “ The EU’s Open Strategic Autonomy from a central banking perspective – Challenges to the monetary policy landscape from a changing geopolitical environment ”, Occasional Paper Series , No 311, ECB, March 2023.

See the Commission’s website for more details.

See Kiel Institute for the World Economy, “ Ukraine Support Tracker ” database.

See World Bank, “ Ukraine – Third Rapid Damage and Needs Assessment (RDNA3): February 2022 – December 2023 ”, February 2024.

See the article entitled “ Fiscal policy and high inflation ”, Economic Bulletin , Issue 2, ECB, 2023, and the box entitled “ Update on euro area fiscal policy responses to the energy crisis and high inflation ” in the same issue.

See Di Sano, M., Gunnella, V. and Lebastard, L., “ Deglobalisation: risk or reality? ”, The ECB Blog , 12 July 2023.

Restructuring production chains in order to prioritise geographical proximity over efficiency could result in increased production costs, a fall in employment and disruption to supply chains. This would ultimately have an impact on government revenues from corporate taxation, personal income tax, sales taxes and international trade. Additionally, it could also discourage investment in innovation, further hampering long-term economic growth and tax revenues.

See European Commission, “ Identifying Europe’s recovery needs ” (SWD/2020/98 final), 27 May 2020.

See “Delivering the Digital Decade with EU investments”, Chapter 5 of European Commission, “ Implementation of the Digital Decade objectives and the Digital Rights and Principles ” (SWD/2023/570 final), 27 September 2023.

See Olczyk, M. and Kuc-Czarnecka, M., “Digital transformation and economic growth – DESI improvement and implementation”, Technological and Economic Development of Economy , Vol. 28, No 3, 2022, pp. 775-803.

The global economic losses are estimated to total $4.3 trillion. See World Meteorological Organization, “ Atlas of Mortality and Economic Losses from Weather, Climate and Water-Related Hazards (1970-2021) ”, 22 May 2023.

See Feyen, L., Ciscar, J.C., Gosling, S., Ibarreta, D. and Soria, A. (eds.), “ Climate change impacts and adaptation in Europe ”, JRC PESETA IV final report, 2020.

See ECB and EIOPA, “ Policy options to reduce the climate insurance protection gap ”, Discussion Paper, April 2023.

See Gagliardi, N., Arévalo, P. and Pamies, S., “ The Fiscal Impact of Extreme Weather and Climate Events: Evidence for EU Countries ”, European Economy Discussion Papers , No 168, European Commission, July 2022.

In Europe, for instance, an estimated €275 billion of Next Generation EU and REPowerEU funds will be used to support investment in clean technology, while €118 billion has been set aside to help fund the transition to clean energy between now and 2027 under the Cohesion Policy.

See the article entitled “ Fiscal policies to mitigate climate change in the euro area ”, Economic Bulletin , Issue 6, ECB, 2022.

See Chapter 1 of the IMF’s October 2023 Fiscal Monitor .

NGFS Phase IV simulates the impact in terms of physical and transition risks of five transition scenarios relative to a hypothetical baseline scenario with no physical or transition risk. “Net-zero by 2050” is an ambitious scenario that limits global warming to 1.5°C through stringent climate policies and innovation, reaching net-zero CO₂ emissions around 2050. “Delayed transition” assumes that annual global emissions do not start to decline until 2030, with strong policies then being needed to keep global warming below 2°C. “Below 2°C” is a scenario where the stringency of climate policies is gradually increased, giving a 67% chance of keeping global warming below 2°C. “NDCs” (nationally determined contributions) is a scenario where all current NDCs are implemented (including NDCs that have been pledged but not yet implemented). The “fragmented world” scenario assumes delayed and divergent climate policy ambition globally, leading to elevated transition risks in some countries and high physical risks everywhere owing to the overall ineffectiveness of the transition.

For these macroeconomic scenarios, the NGFS applies the NiGEM model, under which central banks follow the Taylor rule and long-term fiscal solvency is ensured. Furthermore, there is an assumption that 50% of the carbon price will be passed straight on to consumer prices. In the NiGEM model, the high levels of investment can result in persistently higher real interest rates owing to several interrelated factors. First, heightened demand for investment can lead to a crowding-out effect, whereby increased competition for available funds in capital markets drives borrowing costs up. And second, inflation expectations can, if influenced by increased investment activity, prompt lenders to demand higher nominal interest rates, driving up real interest rates. At the same time, the concrete formulation of central bank behaviour has major implications for the interest rate path in the model simulations.

See, for example, the article entitled “ The macroeconomic implications of the transition to a low-carbon economy ”, Economic Bulletin , Issue 5, ECB, 2023 and the box entitled “ Assessing the macroeconomic effects of climate change transition policies ”, Economic Bulletin , Issue 1, ECB, 2024.

See also the section entitled “Fiscal Policy Sustainability and Structural Spending Pressures” in Chapter 1 of the IMF’s April 2024 Fiscal Monitor , which presents details of a comparable exercise and reaches similar conclusions. The IMF shows that advanced economies are facing additional public spending pressures equivalent to 7.4% of GDP by 2030. This comprises increases of 1 percentage point for interest payments, 2 percentage points for climate spending (under the “net‑zero by 2050” scenario), 2.9 percentage points for demographic ageing, 0.6 percentage points for defence spending, and 1 percentage point for industrial policy and the UN’s Sustainable Development Goals.

The government debt-to-GDP ratio of 60% is referred to in Article 126(2) of the Treaty on the Functioning of the European Union and specified in Protocol No 12 annexed to the Treaty.

The exclusion of digitalisation stems mainly from the limited number of reliable forecasts and the lack of clarity regarding interaction with other key macroeconomic and financial variables.

See also European Commission, “ Defence Investment Gaps Analysis and Way Forward ”, Joint communication to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions, 18 May 2022. For Luxembourg, a target of 1.7% of GDP is assumed, given its commitment to spending 2% of gross national income.

Climate shock scenario data, which are only available until 2050 in the source material, are constant-extrapolated. The Greek NGFS climate shock is adjusted to reflect the fact that debt with fixed rates and long maturities accounts for a significant share of total debt.

See the box entitled “ Past experience of EU countries with sustaining large primary budget surpluses ”, Monthly Bulletin , ECB, June 2011.

The recently published UN Emissions Gap Report found that even in the most optimistic scenario, the chance of limiting global warming to 1.5°C is only 14%, leaving open a large possibility that global warming will exceed 2°C or even 3°C. See United Nations Environment Programme, “ Emissions Gap Report 2023: Broken Record – Temperatures hit new highs, yet world fails to cut emissions (again) ”, November 2023; and Elderson, F., “ “Know thyself” – avoiding policy mistakes in light of the prevailing climate science ”, keynote speech at the Delphi Economic Forum IX, 12 April 2024.

See European Commission, “ Debt Sustainability Monitor 2023 ”, Institutional Papers , No 271, 22 March 2024.

See Adrian, T., Gaspar, V. and Gourinchas, P.-O., “ The Fiscal and Financial Risks of a High-Debt, Slow-Growth World ”, IMF Blog , 28 March 2024.

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