Loneliness and Societal Pessimism: Longitudinal Evidence From Norway

Abstract

This study examines the longitudinal association between loneliness and societal pessimism, i.e., the belief that society is in decline and that people are unable to bring about positive change. Using longitudinal data from 4049 respondents in the Norwegian Generations and Gender Survey (GGS-II), we estimate conditional change score models to assess whether baseline loneliness predicts within-person increases in societal pessimism. Loneliness was measured using the six-item De Jong Gierveld scale, and societal pessimism was captured through a 13-item index covering a range of societal concerns. Results adjusted for baseline pessimism and a range of sociodemographic covariates show that loneliness at baseline was associated with greater increases in societal pessimism. These findings underscore the broader societal impact of loneliness, suggesting that it may not only affect individual wellbeing but also shape attitudes toward society and the future.

Keywords

loneliness pessimism declinism Norway

Introduction

Loneliness is the distressing experience that one’s social connections lack in quantity or quality (De Jong Gierveld et al., 2018). Many high-income countries face high loneliness rates, which has been referred to as the loneliness epidemic (Office of the U.S. Surgeon General, 2023). Research on the consequences of loneliness mostly focuses on health and mortality (Courtin & Knapp, 2017; Schutter et al., 2022). Loneliness may, however, also negatively shape the way people view the world around them (Hawkley & Cacioppo, 2010; Langenkamp, 2023).

This study sheds light on the impact of loneliness on people’s views on society, specifically societal pessimism. Societal pessimism, sometimes also referred to as declinism (Elchardus, 2017; Elchardus & Spruyt, 2016), entails the concern that society is in decline and that people are unable to effectively bring about positive change (Steenvoorden, 2015; Steenvoorden & Van der Meer, 2017). This outlook is not only a psychological phenomenon but also a socially and politically relevant one. Societal pessimism has been linked to political attitudes, such as increased support for populist parties (Elchardus & Spruyt, 2016; Steenvoorden & Harteveld, 2018), and personal life choices, including reduced intentions to have children (Ivanova, 2025; Ivanova & Balbo, 2024).

There is reason to suspect that loneliness and societal pessimism may be linked. The evolutionary theory of loneliness (Cacioppo & Cacioppo, 2018) suggests that loneliness increases vigilance for threats and heightens feelings of vulnerability, which may make lonely individuals perceive the world as more threatening. Prior work accordingly has showed that loneliness is associated with lower interpersonal trust (Langenkamp, 2023) and an elevated sense of social conflict and insecurity (Langenkamp et al., 2026), and linked loneliness to negative cognitive bias (Spithoven et al., 2017). Hawkley and Cacioppo (2010) described loneliness as “tantamount to feeling unsafe” (p. 220). Lonely people’s presumed negative outlook may extend to broader societal issues, potentially amplifying societal pessimism. Yet, the links between loneliness and societal pessimism have hitherto not been assessed empirically. Drawing on recent Norwegian panel data, we test the hypothesis that loneliness predicts increases in societal pessimism. We expand loneliness scholarship, which has traditionally focused on individuals’ proximal social sphere and networks, by considering how it may extrapolate to views on society in general. Establishing how loneliness and societal pessimism are linked may ultimately help clarify the mechanisms through which loneliness may shape personal life choices or political attitudes, and thereby provide additional impetus for governments to address loneliness.

Data and Methods

Sample

We use two waves of data from the Norwegian version of the second rendition of the Generations and Gender Survey (GGS-II) (Gauthier et al., 2025). The GGS-II is a cross-national panel survey designed to study family dynamics and intergenerational relationships. Although the GGS-II has been fielded in multiple countries, Norway is currently the only country for which two waves of GGS-II data are available.

In 2020, a gross sample of 15,000 individuals aged 18 to 54 was selected from Statistics Norway’s population database, which includes all officially registered residents of Norway. Baseline and follow-up data collection took place in November-December 2020 and April-May 2024, respectively. All data were collected via web surveys. The number of baseline survey respondents was 5374 (response rate: 35.8%). Our analytical sample was restricted to the 4,049 respondents who also participated in Wave 2 (retention rate: 75.3%). Women, people with higher education, and people aged 40 and older were overrepresented in this sample, reflecting selective unit non-response and attrition (Dommermuth et al., 2025). In all analyses, we therefore used the supplied longitudinal weights calibrated to correct for this selection.

Measures

In both survey waves, societal pessimism was measured with 13 questions from the GGS-II global uncertainty question battery developed by Andersson et al. (2020). Respondents were asked “If you think about the future, how much do you worry about the following matters?”, and subsequently presented with the following items: terrorism; climate change; overpopulation; economic crisis; increased number of refugees; high unemployment; organised crime; military conflicts; global epidemics; weakened democracy; increased social inequality; political extremism; and future generations’ prospects. For each item, response categories were “very worrying”, “somewhat worrying”, “not particularly worrying”, and “not worrying at all”. These responses were scored from 0 (for “not worrying at all”) to 3 (for “very worrying”), and subsequently summed into an internally consistent societal pessimism scale ranging from 0 to 39 (Cronbach’s α (Wave 1): .85; Cronbach’s α (Wave 2): .87).

Loneliness was measured with the shortened version of the De Jong Gierveld loneliness scale (De Jong Gierveld & Van Tilburg, 1999). The scale consists of six items, three of which are phrased negatively (e.g., “I often feel rejected”) and three of which are phrased positively (e.g., “There are many people I can trust completely”), all of which have response categories of “yes”, “more or less”, and “no”. None of the six items directly refer to loneliness in order to avoid underreporting resulting from the stigma on loneliness (Barreto et al., 2022; De Jong Gierveld et al., 2018; Van den Broek et al., 2024).

In accordance with the scale’s manual (De Jong Gierveld & Van Tilburg, 1999), all items were dichotomized. For the negatively formulated items, the neutral and positive answers (“more or less”, “yes”) were coded as one and the negative answers (“no”) were coded as zero. For the positively formulated items, the neutral and negative answers (“more or less”, “no”) were coded as one and the positive answers (“yes”) were coded as zero. The six dichotomized items were summed into an internally consistent loneliness scale score ranging from 0 to 6 (KR-20 = .73).

A range of background characteristics, measured at baseline, was included in the model to address potential residual confounding. Gender was captured with a dichotomous variable coded as 1 for women and 0 for men. Age in years was included as a continuous variable. Partner status and parenthood status were captured with dichotomous variables indicating whether respondents had a partner or children, respectively. Another dichotomous variable was included for suboptimal self-reported general health. This variable was coded as 1 for respondents who rated their health as fair, bad, or very bad, as opposed to good or very good. Three levels of educational attainment were distinguished: (a) vocational secondary education or less; (b) upper secondary or post-secondary non-tertiary education; (c) tertiary education. A variable for paid work distinguished between respondents who reported being employed or self-employed as their main activity and their counterparts who reported other main activities. Lastly, financial difficulty was measured with a dichotomous variable distinguishing between people who reported that they had difficulties or great difficulties making ends meet and their counterparts who did not report such difficulties.

Analysis

We estimated conditional change score models to assess whether loneliness predicts increases in societal pessimism. The models were specified as follows:

Y_{i 2} - Y_{i 1} = β_{0} + β_{1} L_{i 1} + β_{2} Y_{i 1} + \sum_{j = 1}^{k} β_{j} X_{j i 1} + ε_{i}

in this equation, Y_i1 and Y_i2 refer to the societal pessimism score of person i at timepoint 1 (i.e., at baseline) and at timepoint 2 (i.e., at follow-up), respectively. The intra-personal change in societal pessimism from baseline to follow-up was regressed on L_i1 and Y_i1, which refer to baseline loneliness and baseline societal pessimism, respectively. By accounting for individuals’ baseline societal pessimism, our models reduced bias from reverse causation and unobserved time-invariant confounders (Berrington et al., 2006; cf. Van den Broek, 2024). The models were furthermore adjusted for the set of k baseline controls X described in the measures subsection. The term

ɛ_{i}

refers to the individual error term. Besides a pooled model, models stratified by gender were estimated, given that both antecedents (e.g., Van den Broek, 2017; Van den Broek & Grundy, 2017) and consequences (e.g., Spithoven et al., 2017) of loneliness may differ between women and men. An additional pooled model with gender interaction terms for every parameter was estimated to formally test gender differences in coefficient estimates. A replication file with annotated Stata code for all analyses is available on the Open Science Framework: https://osf.io/5ds9b.

Missing Values

Data for at least one variable of interest were missing for 1554 respondents, representing 38.4% of the sample. The high rates of missingness are attributable to the presence in the dataset of respondents who did not fully complete the survey. The variables with the highest rates of missingness were baseline societal pessimism (n = 929), follow-up societal pessimism (n = 742), loneliness (n = 776), financial difficulties (n = 618), and self-reported health (n = 466). These missing data were addressed using multiple imputation by chained equations, a flexible approach suitable for handling variables of different types (Azur et al., 2011). The imputations were generated under the assumption that the data were missing at random (MAR), meaning that any systematic differences between observed and missing values were explained by variables included in the imputation model (Azur et al., 2011). Loneliness at follow-up was included as an auxiliary variable, and imputations were stratified by gender to strengthen the model. Fifty imputed datasets were created, and estimates were combined using standard multiple-imputation pooling procedures. Descriptive statistics for the imputed analytical sample are presented in Table 1. As a robustness check, all analyses were repeated on non-imputed data of the 2495 respondents with complete information.

Table 1.

Descriptive Statistics

Continuous variables	Full sample		Women		Men
Continuous variables	Mean score	Standard deviation	Mean score	Standard deviation	Mean score	Standard deviation
Societal pessimism^a (baseline)	19.3	(6.0)	20.5	(6.1)	18.1	(5.7)
Societal pessimism (follow-up)	19.6	(6.2)	20.8	(5.9)	18.5	(6.3)
Societal pessimism change	0.3	(5.7)	0.3	(5.5)	0.4	(5.9)
Loneliness	2.8	(1.9)	2.8	(1.9)	2.8	(1.9)
Age in years^a	35.8	(11.1)	35.9	(11.0)	35.7	(11.1)
Categorical variables	Percentage		Percentage		Percentage
Female	49.8%
Has partner	72.0%		74.8%		69.3%
Has children	52.5%		57.2%		47.9%
Less than good self-rated health	27.5%		27.2%		27.8%
Educational attainment:
Up to vocational secondary	29.9%		21.0%		38.8%
Upper secondary or post-secondary non-tertiary	24.1%		24.0%		24.1%
Tertiary	46.0%		55.0%		37.1%
Paid work	69.4%		65.7%		73.0%
Financial difficulty	7.0%		7.2%		6.8%
Number of respondents	4,049		2,319		1,730

^aScores represent values before mean centring.

Notes: Data are from the Norwegian version of the Generations and Gender Survey – Round 2 (GGS-II), waves 1 and 2; Data are weighted; Multiple imputation with chained equations was applied to deal with missing values.

Results

Main Results

Table 2 shows the results of the conditional change score models predicting within-person changes in societal pessimism. The model estimated on the full sample showed that, adjusted for baseline societal pessimism and the other variables in the model, higher levels of loneliness at baseline were associated with significantly larger increases in societal pessimism over time. The model furthermore showed that male gender and higher societal pessimism scores at baseline were associated with smaller increases in societal pessimism. No statistically significant effects on societal pessimism change were found for any of the other variables included. The model explained 20.5% of within-person changes in societal pessimism.

Table 2.

Results of Conditional Change Score Models of Change in Societal Pessimism

	Full sample		Women		Men		Women vs. Men
	Coeff.	[95% CI]	Coeff.	[95% CI]	Coeff.	[95% CI]	ΔCoeff.	[95% CI]
Societal pessimism^a at baseline	−0.441***	[-0.474,-0.408]	−0.440***	[-0.478,-0.403]	−0.441***	[-0.498,-0.385]	0.001	[-0.066,0.068]
Loneliness	0.169**	[0.058,0.280]	0.193**	[0.061,0.324]	0.139	[-0.039,0.318]	0.053	[-0.162,0.268]
Female	0.842***	[0.475,1.209]
Age in years^a	0.021	[-0.001,0.044]	0.013	[-0.014,0.040]	0.027	[-0.008,0.063]	−0.014	[-0.057,0.029]
Has partner	0.178	[-0.286,0.642]	−0.153	[-0.760,0.454]	0.525	[-0.231,1.281]	−0.678	[-1.649,0.293]
Has children	0.053	[-0.452,0.558]	0.321	[-0.301,0.944]	−0.185	[-0.981,0.611]	0.506	[-0.463,1.476]
Less than good self-rated health	0.011	[-0.487,0.508]	0.158	[-0.402,0.717]	−0.163	[-1.000,0.674]	0.321	[-0.658,1.299]
Educational attainment:
Up to vocational secondary	Ref.		Ref.		Ref.		Ref.
Upper secondary or post-secondary non-tertiary	−0.219	[-0.744,0.306]	−0.096	[-0.796,0.604]	−0.376	[-1.179,0.427]	0.280	[-0.808,1.367]
Tertiary	0.278	[-0.198,0.754]	0.115	[-0.474,0.704]	0.356	[-0.364,1.075]	−0.241	[-1.153,0.671]
Paid work	−0.369	[-0.824,0.086]	−0.020	[-0.565,0.524]	−0.747	[-1.515,0.022]	0.726	[-0.201,1.653]
Financial difficulty	0.707	[-0.273,1.687]	−0.389	[-1.407,0.629]	1.792*	[0.160,3.424]	−2.181*	[-4.049,-0.313]
Intercept	−0.591	[-1.304,0.121]	0.148	[-0.796,1.091]	−0.378	[-1.470,0.714]	0.526	[-0.943,1.994]
R² (pooled)	.205		.241		.182
Number of respondents	4,049		2,319		1,730

^aCentred on sample mean.

*p < .05, **p < .01, ***p < .001.

Table 2 also presents models stratified by gender, along with formal tests of gender differences in the coefficient estimates. With the exception of financial difficulty, which had a significantly stronger effect on changes in societal pessimism among men than among women, no significant gender differences in coefficients were observed. Thus, we found no evidence that the effect of loneliness on changes in societal pessimism differs between women and men. However, it is worth noting that, possibly due to the smaller size of the male subsample, the estimated association between loneliness and societal pessimism change did not reach statistical significance in the model for men. Therefore, while our analyses do not suggest different associations by gender, the evidence for men should be interpreted with caution. Adjusted predictions of societal pessimism change based on the pooled, women-only and men-only models, respectively, are presented in Figure 1.

Figure 1.

Adjusted predictions of societal pessimism change by baseline loneliness level; with 95% confidence intervals

Alternative Specifications

In our main models, the association between loneliness at baseline and societal pessimism change was constrained to be linear. Rubinstein et al. (1979) suggested, however, that defeatism, which is closely related to societal pessimism, may arise only at high levels of loneliness, but not at moderate levels. This implies that the relationship between loneliness and societal pessimism change could be non-linear. We therefore re-estimated our models with an added quadratic term for loneliness. The results, presented in Appendix B of the online supplemental material, showed that the quadratic term was not statistically significant in either the pooled model or any of the gender-specific models. We thus found no evidence that our main models were mis-specified.

Our main results were moreover based on analyses of data in which missing information was imputed. Results of sensitivity analyses on a sample restricted to the 2495 respondents who provided complete information on all variables of interest are presented in Appendix C of the online supplementary material. These results were overall consistent with the main findings, reinforcing the conclusion that loneliness predicts increases in societal pessimism.

Discussion

In the current study, we demonstrated a longitudinal association between loneliness and societal pessimism. We also found that male gender and higher baseline levels of societal pessimism were associated with smaller increases in societal pessimism over time. One of our study’s key strengths is its relatively large, nationally representative sample. Its longitudinal design moreover allowed us to capture change over time and to address potential issues of reverse causation. In additional analyses presented in Appendix D, we demonstrate that societal pessimism at baseline also predicted increases in loneliness over time, highlighting the bidirectional nature of the association and underscoring the importance of our longitudinal approach for disentangling these reciprocal effects.

Our study is also not without limitations. Firstly, our societal pessimism measure differed from the various measures used in other studies (e.g., Ivanova & Balbo, 2024; Steenvoorden, 2015; Steenvoorden & Van der Meer, 2017). The heterogeneity in how societal pessimism is operationalized makes direct comparisons across studies challenging and underscores the need for more standardized approaches.

Secondly, our baseline data were collected around the onset of the COVID-19 pandemic. This may have had an impact on both loneliness and societal pessimism. Norway furthermore is a Western European country with a highly developed welfare state, and research has shown that there is typically relatively little societal pessimism in such countries (Steenvoorden & Van der Meer, 2017). Replication in countries with less advanced welfare state arrangements and in more stable periods will therefore be important to assess our findings’ robustness and generalizability.

Another limitation is that information on personality traits was not available in our dataset. This is unfortunate, because neuroticism shares the tendency to interpret situations pessimistically with societal pessimism and it is also associated with loneliness (Buecker et al., 2020). As such, it remains unclear to what extent the observed association reflects loneliness specifically, rather than a more general pessimistic or negative outlook. Future studies that include measures of personality would therefore be better positioned to assess whether loneliness contributes to societal pessimism above and beyond dispositional pessimism.

Despite these limitations, the current study extends existing work on the negative bias of lonely people (e.g., Spithoven et al., 2017), and further elevates the conversation on why addressing loneliness is important (cf. Office of the U.S. Surgeon General, 2023). The findings presented here highlight a link between the personal and the public. Loneliness is not simply about the individual and their views on social relationships (or lack thereof), but may also shape broader worldviews. One plausible mechanism is generalization from interpersonal to societal domains. Individuals who experience their personal social environment as deficient may infer that social interactions are unreliable or conflictual more broadly, and project these expectations onto society as a whole. This process of extrapolation might contribute to more pessimistic views about societal functioning. In this way, micro-level social experiences may inform macro-level perceptions, a dynamic that has been suggested to shape political attitudes (Elchardus & Spruyt, 2016; Steenvoorden & Harteveld, 2018) and personal life choices (Ivanova, 2025; Ivanova & Balbo, 2024). Future research could build on this work by testing integrative models in which societal pessimism mediates associations between loneliness and downstream societal attitudes and life-course decisions.

Supplemental material

Suppplemental Material - Loneliness and Societal Pessimism: Longitudinal Evidence From Norway

Suppplemental Material for Loneliness and Societal Pessimism: Longitudinal Evidence From Norway by Thijs van den Broek & Jack Lam in Journal of Social and Personal Relationships

Footnotes

ORCID iD

Thijs van den Broek

Ethical Considerations

Ethical approval was not required for this secondary analysis of anonymized public-release versions of existing datasets.

Consent to Participate

All respondents of the Norwegian GGS-II surveys used here provided informed consent (Dommermuth & Lappegård, 2021).

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Access to the datasets used in the current study can be requested for scholarly reuse via .

Open Science Statement

As part of IARR's encouragement of open research practices, the authors have provided the following information: This research was not pre-registered. The data used in the research can cannot be publicly shared but are available upon request. The data can be obtained at: https://www.ggp-i.org/. A replication file with annotated Stata code for all analyses is available on the Open Science Framework: .

Supplemental material

Supplemental material for this article is available online.

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