New evidence on the relationship between income and subjective well-being: the mediating and moderating roles of psychological security

Abstract

Background

A proper understanding of the relationship between income and subjective well-being is helpful to enhance people's welfare. Research on the relationship between income and subjective well-being has not yet reached a universally recognized conclusion. Particularly, the proposition of the “Easterlin paradox” prompts scholars to explore the role of other factors in the relationship between income and subjective well-being. Grounded in social comparison theory and need theory, this paper examines the role of psychological security in the relationship between income and subjective well-being.

Methods

Data came from 6,953 Chinese urban residents were included in the study. Data cleaning, coding, and analysis were performed using SPSS. ANOVA and Bootstrap method were used to test the research hypothesis.

Results

There were significant differences in subjective well-being across income levels, with high-income residents having significantly higher life satisfaction and positive affect than low-income residents and significantly lower negative affect than low-income residents. Psychological security mediated the influence of income on life satisfaction, positive and negative affect. Psychological security positively moderated the relationship between income and life satisfaction. There is group heterogeneity in the effects of income and psychological security on SWB in terms of age and education.

Conclusions

The findings enrich the mediating and moderating role of psychological factors in the relationship between income and subjective well-being. From the perspective of public psychological security, it provides a reference basis for enhancing the public's subjective well-being.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-22286-2.

Background

Achieving happiness is the main motive and purpose of people's struggles and the ultimate goal of social development. March 20 was declared the International Day of Happiness by the 66th United Nations General Assembly, with the aim of increasing public awareness of the importance of happiness. Happiness has a beneficial effect upon people's health and is also a barometer of national governance performance. Indicators like Gross National Happiness and the Better Life Index are used to measure the development of society. However, China's happiness index ranks only 79th, according to the United Nations' World Happiness Report 2022. China is experiencing the most rapid urbanization in the world, with its urbanization rate attaining 66.16% by 2024(National Bureau of Statistics, http://www.stats.gov.cn/). While this accelerated urban transformation has contributed to gradual improvements in living standards, urban residents are faced with multiple stressors, including widening income inequality, escalating costs of housing and education, and heightened occupational competition [1]. Within the environment of socio-economic volatility, residents' social psychological issues become increasingly prominent, and the high cost of living and social pressure in the city reduce the quality of life [2]. Therefore, how to improve the happiness of China's urban citizens is increasingly receiving attention from society and the government.

Happiness is considered to be a subjective feeling, a person's subjective assessment of their own existence, and the term subjective well-being (SWB) is commonly used [3]. In the past decades, scholars have conducted extensive theoretical and empirical research on factors influencing SWB, such as economic status [4, 5], health status [6, 7], and demographic variables [8, 9]. Among them, income attracted the most attention. Income itself affects their living environment, thereby maximizing quality of life [10]. The Easterlin Paradox has motivated scholars to conduct continuous research on the relationship between income and happiness [11].

However, the results of research on the relationship between income and SWB have not been agreed upon. Traditionally, the view has been that the higher the income, the higher the happiness [12]. For example, Sacks et al. [13]found that the rich are happier than the poor, that per capita happiness is higher in richer countries than in poorer countries, and that economic growth is associated with growth in well-being. Nevertheless, with the gradual deepening of research, scholars have found that the relationship is nonlinear. Jebb et al. [14] proposed the point of income satiation, whereby happiness was no longer significantly improved by an additional income increase after a certain point. Easterlin et al. [15] specifically pointed out that in the short term, happiness is positively correlated with income, but over time, happiness does not increase with the increase in income, revising the Easterlin Paradox. A slight correlation was also observed in some studies [16, 17]. In addition, the relationship between income and life satisfaction (LS) differs from the relationship between income and positive affect (PA) or negative affect (NA) [18]. As we know, SWB is a psychological state caused by many factors, implying that other factors may have an impact on how income affects it. Throughout the previous literature, scholars have found that psychosocial factors such as aspiration [19], motivation [20], future time perspective [21], and financial satisfaction [22] play important roles in the relationship between income and SWB. These studies affirm the positive impact of income growth on SWB, except that the impact may be attenuated by the negative effects of other variables [19, 22]. Therefore, in the context of Chinese culture, this article will explore the relationship between income and subjective well-being and the important role that psychological security plays in the relationship.

Based on social comparison theory, the inherent impact of relative deprivation is implicit when seeking the link between income and SWB. Relative deprivation occurs when people perceive their income status to be disadvantaged compared to others [23], which negatively affects mental health [24]. As psychological security (PS) is an important determinant of mental health [25], individuals with high levels of relative deprivation feel less security [26], and insecure individuals are more prone to produce PA and reduce LS [27], leading to reduced well-being. Possible relationships between PS and SWB have also been identified in established studies. For example, Vermote et al. [28] argued that perceived insecurity predicts LS and negative emotions. However, few studies have directly focused on whether PS plays a role in the relationship between income and SWB. Moreover, certain research has demonstrated that variables can function as both moderators and mediators [29–31]. Based on the satisfaction of goals theory, the fulfillment of needs, desires, and goals leads to high levels of SWB [32]. Self-determination theory emphasizes psychological needs and goals [33]. Psychological need satisfaction increases an individual's happiness at work [34] and LS [35]. Chen and Chen [36] found that the relationship between achievement goals and SWB was weaker in individuals with low levels of basic psychological need satisfaction compared to those in groups with high levels of basic psychological satisfaction. Then, as an individual's psychological need to pursue security [37], which is also recognized as a basic human drive and goal [38], can PS moderate the relationship between income and SWB? Individuals with high security are likely to pursue higher-level needs when their basic security needs are met [25], do not worry about the negative effects of a distinctive viewpoint, and can effectively decrease negative emotions [39]. Individuals who feel insecure are more prone to worry about personal finances, experience more stress, and have lower self-efficacy, and lower self-efficacy may lead to a lack of confidence, resulting in anxiety and a lower quality of life [40]. Thus, we investigate how PS mediates and moderates the link between income and SWB.

In summary, based on social comparison theory and need theory, we model the mediating and moderating role of income in relation to SWB by incorporating PS. This paper makes the following contributions: (1) Many studies have focused on the relationship between income and SWB, but the results are not consistent, and few studies have examined the relationship between income and both cognitive and affective dimensions of SWB. We focus on the relationship between income and the dimensions of SWB (LS, PA and NA) to provide a basis for improving Chinese urban residents’ well-being. (2) Existing studies have identified a possible relationship between PS and SWB. However, few studies have focused on the role of PS in the relationship between income and SWB. This paper introduces PS as a mediating and moderating variable for analyzing the association between income and SWB.

Theoretical background and hypotheses

Theoretical background

Social comparison theory was proposed by the American social psychologist Festinger, which considers social comparison as a social psychological phenomenon in which individuals obtain self-evaluation by comparing with others in the absence of objective standards [41]. Wood [42] defines social comparison as the process of thinking about information about one or more other people in relation to oneself. According to the social comparison theory, when individuals make upward comparisons and recognize that they are not in an advantageous position, they will form a relative deprivation position and generate a sense of relative deprivation, which brings about many negative feelings [23]. Social comparison is widely present in the formation of cognition and emotions, affecting self-evaluation, emotion, and behavior [43]. Esping-Andersen and Nedoluzhko [44] pointed out that socio-economic comparisons generated by individuals' comparisons with others lead to relative deprivation and reduce SWB. Easterlin [45] suggested that income effects on happiness stem from social comparisons. Therefore, the relationship between income and SWB can be verified via social comparison theory.

Maslow, a humanistic psychologist, proposed the hierarchy of needs from the perspective of individual needs, including physiological needs, safety needs, love and belonging, esteem, and self-actualization [37]. The fulfillment of an individual's needs leads to happiness, while unmet needs lead to unhappiness. Deci & Ryan [33] introduced the concept of basic psychological needs in the framework of Self-Determination theory, stating that individuals show happiness when their basic psychological needs are satisfied. From need theory, one of the reasons for the association between income and SWB is that income contributes to the fulfillment of basic psychological needs, which in turn manifest well-being [32].

Income and SWB

SWB refers to a person's subjective evaluation of the quality of life and focuses on how and why people experience life positively, including both cognitive judgments and affective reactions [3]. The cognitive dimension refers to cognitively judgmental evaluations of life based on one's own criteria, which can also be interpreted as the degree of satisfaction with life [3]. The affective dimension focuses on the affective responses over time, including both PA and NA [46].

Income is one of the core concepts of this study, and in previous studies, scholars have categorized wealth, money, and income as economic factors that affect SWB. Existing studies usually use GDP per capita [13], personal income [47], and household income [20, 22] for measurement.

Previous studies have found that individuals with higher incomes are more likely to experience SWB. For instance, Wang & Yan et al. [48] found that the income of Chinese urban residents is an important factor in SWB. Wang & VanderWeele [49] found that higher income individuals reported higher SWB. Income can directly bring more advantages to life, such as material wealth and healthcare [10], improving the quality of life. LS can be significantly predicted by income [46]. Moreover, the lack of resources brought about by income loss can cause individuals to experience psychological stress [50], triggering negative emotions. On the contrary, higher income is associated with more positive feelings such as contentment and amusement [51]. Therefore, the following hypotheses are put forward.

• H1a: There are significant differences in LS across income levels, with high-income residents significantly more satisfied than low-income residents.

• H1b: There are significant differences in PA across income levels, with high-income residents having significantly higher PA than low-income residents.

• H1c: There are significant differences in NA across income levels, and the NA of high-income residents was significantly lower.

The mediating role of PS

The concept of security was first studied in Freud's Psychoanalytic Theory, and the term PS was first formalized by the humanistic psychologist Maslow. Maslow et al. [25] viewed PS as a feeling of confidence, safety, and freedom from fear and anxiety, and in particular, a feeling of fulfillment of one's various present and future needs. PS can be viewed as a motivation to pursue security, such as needs [37], drives and goals [38], and can also be viewed as a subjective experience. Wang & Long et al. [48] argued that PS is a subjective emotional experience caused by an external stimulus. We consider that PS is a comprehensive feeling experienced by individuals in the process of pursuing security.

Social comparison theory states that people tend to evaluate themselves by comparing themselves with others [41]. If an individual realizes that his income or other needs are lower than others by perceiving and evaluating the internal and external environments, he will experience a psychological state of relative deprivation, which may be accompanied by negative feelings [23]. Further, relative deprivation and SWB have a negative correlation [49]. In terms of attachment theory, improving or increasing attachment security can help maintain an individual's LS [52] and increase job satisfaction [53]. Attachment insecurity can lead to individuals experiencing negative affect, which negatively affects SWB [54]. It has been shown that income has a significant effect on security. For example, Han et al. [55] note that patients with higher and lower incomes in terms of security differed significantly, and there was a stronger tendency toward insecurity with lower incomes. Wang and Liu [56] note that the relationship between security and income is not a perfectly linearly increasing relationship, but overall, the group with higher income also has relatively higher security. Therefore, we believe that income can indirectly affect SWB through PS and propose the following hypotheses.

• H2a: PS mediates the relationship between income and LS.

• H2b: PS mediates the relationship between income and PA.

• H2c: PS mediates the relationship between income and NA.

The moderating role of PS

Need theory posits that human beings have universal needs, and the degree of need satisfaction is associated with different levels of SWB [57]. Deci and Ryan [33] emphasized the role of basic psychological needs from the perspective of internal and external motivation, stating that inconsistent results in the impact of pursuing and achieving life goals on well-being may be that basic psychological needs are met to different degrees. Basic psychological needs are affected by the environment of individuals, and the relationship between external environment and individual emotions and behaviors is always affected [58]. Financial insecurity may be associated with unmet basic psychological needs, which in turn is related to lower levels of individual happiness [59]. Maslow [37] stated that higher needs become an individual's primary needs only after the individual's lower needs are satisfied, but it is not the case that higher needs emerge only after the lower needs are fully satisfied. Most needs are related to an economic base. Thus, income and PS needs may act simultaneously on SWB. It has been shown that job insecurity and economic insecurity positively affect depression and anxiety It has been shown that job insecurity and economic insecurity positively affect depression and anxiety [60, 61]. Financial well-being, which can be interpreted as a subjective feeling about current and future personal finances, is positively correlated with SWB [62]. We speculate that individuals with high PS have their basic psychological needs met. Higher income is likely to be associated with increased perceived security of financial situations, affect SWB of people whose basic needs are met [63], and further satisfy individuals' higher needs. Positive psychological experiences will tend to perceive their goals as more attainable [64, 65], and goal attainment is positively correlated with LS [66]. Individuals with low PS may be more likely to feel anxious or depressed due to social class differences [67]. While higher individual income is associated with improved basic material living conditions, it may not be sufficient to address the individual's need for socioeconomic status or other expectations for a better life. These unmet needs may be related to lower levels of SWB. Thus, we believe that the link between income and SWB may be moderated by PS, and the following hypotheses are proposed.

• H3a: PS moderates the relationship between income and LS, and the higher the PS, the stronger the relationship between them.

• H3b: PS moderates the relationship between income and PA, and the higher the PS, the stronger the relationship between them.

• H3c: PS moderates the relationship between income and NA, and the higher the PS, the weaker the relationship between them.

To summarize the above, the conceptual model of this article is as follows (Fig. 1). The mediating and moderating effects were tested respectively.

Fig. 1.

The conceptual model

Methods

Participants

This study utilized a cluster sampling method to collect data from urban residents across multiple Chinese provinces and municipalities (Jiangsu, Guangdong, Hunan, Shaanxi, Henan, Hebei, and Beijing) through the online survey platform "Questionnaire Star." Electronic questionnaires were distributed to target regional users via the platform's built-in random distribution function. The research strictly adhered to the principles of voluntary participation and prior informed consent. All participants were required to read an informed consent statement on the questionnaire's front page detailing the research purpose, data usage, and privacy protection measures. A confirmation step where "submission constitutes consent to participate" was implemented to ensure ethical compliance. To ensure data quality, researchers rigorously examined each questionnaire for authenticity and completeness, including checks for omitted items and logical contradictions. Questionnaires exhibiting significant logical inconsistencies, excessively short response times, or consecutive repeated answers were deemed invalid and excluded. After removing invalid responses, 6,953 valid entries were retained for analysis. Among them, 50.1% were male and 49.9% were female. 50.1% were 35 years old and below, and 49.9% were 36 years old and above. 28% were junior college or below, 57.7% were Bachelor degrees, and 14.3% were Graduate degree or above.

This research has been approved by the ethics committee of the institution where the first author works, and was developed by our research group in the National Natural Science Foundation project “The mechanism and intervention strategies of group psychological security in the formation of social risks under the new normal” (Approval No. 71673256) to measure the PS and SWB level of urban residents.

Measures

Income is the independent variable. Taking into account the residents’ family environment, the study adopts per capita monthly household income as an indicator. In the questionnaire, per capita monthly household income is a categorical variable, including five groups of less than 2000, 2001–5000, 5001–10,000, 10,000–20,001, and more than 20,000, which are filled in according to the actual situation.

Considering regional disparities, we utilized the "average wage levels of urban employed persons by province" from the China Statistical Yearbooks 2019–2021 (National Bureau of Statistics, http://www.stats.gov.cn/) published by the National Bureau of Statistics of China as the basis for income group classification. We converted the annual "average wage levels of urban employed persons" into monthly indicators and established relative low-income and high-income thresholds using the 33% standard. These thresholds were then compared with the "per capita monthly household income" reported in the questionnaires. Using 2020 data as an example (see Table 1), participants whose income exceeded the high-income threshold were classified into the relatively high-income group, while those below the low-income threshold were assigned to the relatively low-income group. The remaining participants were categorized as middle-income. Through this methodological approach, we obtained three distinct income groups: 2,364 individuals in the low-income group, 2,571 in the middle-income group, and 2,018 in the high-income group.

Table 1.

The coding of income

Province	Average Annual Wage	monthly average wage	High-income standard	Low-income standard	High-income	Low-income
Guangdong	108,045	9003.75	11,974.99	6032.51	above 10,000	below 5000
Beijing	178,178	14,848.17	19,748.06	9948.27	above 20,000	below 10,000
Jiangsu	103,621	8635.08	11,484.66	5785.51	above 10,000	below 5000
Henan	70,239	5853.25	7784.82	3921.68	above 10,000	below 5000
Hunan	79,122	6593.50	8769.36	4417.65	above 10,000	below 5000
Shanxi	83,520	6960.00	9256.80	4663.20	above 10,000	below 5000
Hubei	85,052	7087.67	9426.60	4748.74	above 10,000	below 5000
Shandong	87,749	7312.42	9725.51	4899.32	above 10,000	below 5000

SWB The explanatory variable of SWB was measured by both the Life Satisfaction Scale [68] and the Happiness Scale [69]. The Life Satisfaction Scale consists of five items, with higher values corresponding to greater LS. The Happiness Scale includes both PA and NA, with six entries for PA and eight entries for NA, with trends ranging from none to some and from weak to strong. Both the scale use 7-point Likert scoring, and the participants chose the option that corresponded to their feelings according to the actual situation. The Cronbach's alpha values for subscales were 0.84, 0.86, and 0.85, respectively.

PS was measured by the Psychological Security Questionnaire for Urban Residents developed by the research group [70, 71]. The PSS is composed of seven dimensions: General Sense of Safety, Certainty, Normal Physiological Function, Calmness, Trust, Relaxation, and Excitation. There are a total of 36 items, using a 5-point Likert scoring, ranging from “1 = completely disagree” to “5 = very agree”, calculated as the average of the total questionnaire scores. Yang et al. [71] explained the specific items of the PSS. The reliability and construct validity of the PSS have been tested, χ2/df = 4.19, NFI = 0.96, RFI = 0.95, IFI = 0.97, CFI = 0.97, RMSEA = 0.07 [70, 71]. In this study, the Cronbach’s alpha coefficients of the total scale was 0.934. The psychological security scale had good cross-group measurement invariance (Supplementary Table S1).

Analysis

We used SPSS Statistics 25.0 for data analysis. First, correlation analysis was performed for the main variables. Second, hypothesis testing was performed using SPSS macro (PROCESS version3.3 is written by Andrew F. Hayes, http://www.afhayes.com). Finally, heterogeneity testing was performed using grouped regression.

Common method bias test

Common method biases are systematic errors caused by the data source, measurement environment, and item characteristics [72], which can be tested by Harman’s single factor test. Less than 40% was found in the first unrotated principal component (26.245%), suggesting no serious common method bias problem.

Results

Descriptive statistics and variable intercorrelations

The results are shown in Table 2. Income was positively correlated with LS and PA, and less correlated with NA. PS was positively correlated with LS and PA and negatively correlated with NA. This result lays the foundation for subsequent analysis.

Table 2.

Descriptive statistics and correlation analysis

Variables	1	2	3	4	5	6	7	8
1 Income	1
2 LS	0.188***	1
3 PA	0.176***	0.637***	1
4 NA	−0.040***	−0.178***	−0.170***	1
5 PS	0.127***	0.516***	0.627***	−0.273***	1
6 Gender	−0.029*	0.017	0.004	−0.037***	0.021	1
7 Age	0.081***	0.083***	0.037***	−0.062***	0.022	−0.023	1
8 Education	0.242***	0.098***	0.098***	−0.006	0.071***	0.012	−0.065***	1
Mean ± SD	1.95 ± 0.79	3.87 ± 1.32	4.19 ± 1.11	2.73 ± 0.88	3.48 ± 0.60	0.500 ± 0.500	0.499 ± 0.500	1.863 ± 0.636

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security

Gender coding: 0 = male, 1 = female; Age coding: 0 = under 35, 1 = over 35; Education coding: Junior college or below = 1 Bachelor degree = 2 Graduate degree or above = 3

N = 6953

^*p < 0.05

^**p < 0.01

^***p < 0.001

Differences in SWB of urban residents at different income levels

The one-way ANOVA results demonstrated that income level differed significantly in LS (F (2,6950) = 126.862, p < 0.001), PA (F (2,6950) = 111.171, p < 0.001), and NA (F (2,6950) = 5.591, p < 0.01). The results are shown in Table 3.

Table 3.

Differences in SWB of urban residents

Dependent variable	Independent Variable	N	Mean ± SD	F
LS	Low income	2364	3.581 ± 1.323	126.862***
	Middle income	2571	3.873 ± 1.301
	High income	2018	4.208 ± 1.269
PA	Low income	2364	3.962 ± 1.133	111.171***
	Middle income	2571	4.200 ± 1.072
	High income	2018	4.455 ± 1.067
NA	Low income	2364	2.770 ± 0.901	5.591**
	Middle income	2571	2.737 ± 0.869
	High income	2018	2.681 ± 0.881

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security

^**p < 0.01

^***p < 0.001

The results of multiple comparisons after ANOVA showed that the LS at the high-income level (4.208 ± 1.269) was significantly higher than that at the middle-income level (3.873 ± 1.301) and the low-income level (3.581 ± 1.323) (p < 0.001; p < 0.001), and that the middle-income was higher than the low-income (p < 0.001). The PA of residents at the high-income level (4.455 ± 1.067) was significantly higher than that at the middle-income level (4.200 ± 1.072) and the low-income level (3.962 ± 1.133) (p < 0.001; p < 0.001), and the middle-income was higher than the low-income (p < 0.001). The NA of residents at the high-income level (2.681 ± 0.881) were significantly lower than that at the low-income level (2.770 ± 0.901) (p < 0.01). Thus, hypotheses 1a–1c were supported.

Testing mediating effects

The SPSS macro (PROCESS version3.3 is written by Andrew F. Hayes, http://www.afhayes.com) and Bootstrap method was conducted to examine the mediating effect of PS while controlling gender, age, and education. The judgment coefficients mainly include Bootstrap confidence intervals, relative direct effects, and relative mediation effect values [73]. The results are shown in Table 4.

Table 4.

Results of the mediating role of PS

Mediating effect path (High-income group as a reference)		Effect	S.E	95%CI
LS	Low income → PS → LS	−0.191a	0.021	[−0.232, −0.151]
	Low income → LS	−0.368	0.036	[−0.440, −0.297]
	Middle income → PS → LS	−0.078a	0.019	[−0.115, −0.042]
	Middle income → LS	−0.217	0.033	[−0.283, −0.153]
PA	Low income → PS → PA	−0.197a	0.021	[−0.238, −0.156]
	Low income → PA	−0.249	0.028	[−0.302, −0.196]
	Middle income → PS → PA	−0.081a	0.019	[−0.118, −0.043]
	Middle income → PA	−0.149	0.025	[−0.200, −0.100]
NA	Low income → PS → NA	0.070a	0.008	[0.054,0.086]
	Low income → NA	0.011	0.027	[−0.043,0.064]
	Middle income → PS → NA	0.028a	0.007	[0.016,0.042]
	Middle income → NA	0.019	0.025	[−0.030,0.068]

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security

^aindicates significant relative mediating effect, CI Confidence interval

As the income variable is a categorical variable, this paper refers to hayes (2014) uses indicator coding for income to form dummy variables [73], namely, low income (vs high) (Inc-L) and middle income (vs high) (Inc-M). Inc-L represents low-income as a reference to high-income, and Inc-M represents middle-income as a reference to high-income.

First, the mediating effect was tested using PROCESS marco Model 4 with LS as the dependent variable. The omnibus total effect was significant, F (2,6947) = 94.519, p < 0.001. And the omnibus direct effect was significant, F (2,6946) = 54.571, p < 0.001. This suggests that at least one relative total effect or relative direct effect was not equal to 0. Therefore, the relative mediating effect was further analyzed. According to the test results, taking high income as the reference, the relative mediation effect of low income on LS was significant (β = −0.191, 95% CI = [−0.232, −0.151]), and the relative direct effect was significant (β = −0.368, 95% CI = [−0.440, −0.297]). Similarly, using high income as a reference, the relative mediation effect and relative direct effect of middle income were significant. Thus, PS plays a partially mediating role. Hypothesis 2a was tested.

Second, we took PA as the dependent variable. According to the test results, using high income as a reference, the relative mediation effect and relative direct effect of low income were significant. Similarly, using high income as a reference, middle income had a significant relative mediation effect and relative direct effect. Thus, PS plays a partially mediating role in the effect of income on PA. Hypothesis 2b was tested.

Finally, the mediating role of PS between income and NA was tested. Taking high income as a reference, the relative mediation effect of low income on NA was significant (β = 0.070, 95% CI = [0.054,0.086]), and the relative mediation effect of middle income on NA was significant (β = 0.028, 95% CI = [0.016,0.042]). Thus, PS plays a mediating role in the effect of income on NA. Hypothesis 2c was tested.

Testing moderating effects

SPSS macro (PROCESS version3.3) was used to perform moderation tests for multi-category independent variables [74]. The independent variables were virtualized to form low-income (vs. high) (Inc-L) and middle-income (vs. high) (Inc-M), with high income as the reference group. If moderating effects hold, further simple slope tests are conducted. The results are shown in Table 5.

Table 5.

Results of the moderating role of PS

Variables	Model 1	Model 2	Model 3
	LS	PA	NA
Inc-L	−0.363***	−0.248***	0.009
	(−10.238)	(−9.108)	(0.318)
Inc-M	−0.205***	−0.145***	0.013
	(−6.058)	(−5.587)	(0.523)
PS	1.231***	1.177***	−0.456***
	(27.023)	(33.670)	(−13.151)
Inc-L*PS	−0.212***	−0.071	0.086*
	(−3.684)	(−1.600)	(1.968)
Inc-M*PS	−0.134*	−0.041	0.058
	(−2.257)	(−0.906)	(1.293)
Constant	3.606***	4.179***	2.929***
	(45.165)	(68.242)	(48.259)
Gender	0.027	−0.014	−0.057**
	(1.024)	(−0.673)	(−2.805)
Age	0.174***	0.041	−0.098***
	(6.446)	(1.957)	(−4.776)
Education	0.084***	0.059***	0.016
	(3.846)	(3.517)	(0.973)

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security, Inc-L Low-income as a reference to high-income, Inc-M Middle-income as a reference to high-income

Gender coding: 0 = male, 1 = female; Age coding: 0 = under 35, 1 = over 35; Education coding: Junior college or below = 1 Bachelor degree = 2 Graduate degree or above = 3

t statistics in parentheses

^*p < 0.05

^**p < 0.01

^***p < 0.001

The moderation of Model 1 was significant, R² = 0.288, F(2, 6944) = 6.785, p < 0.01. Specifically, the interaction term between Inc-L and PS significantly negatively predicted LS, β = −0.212, p < 0.001. And the interaction term between Inc-M and PS significantly negatively predicted LS, β = −0.134, p < 0.05. Thus, the moderating effect of PS between income and LS was significant. Hypothesis 3a was validated. The moderation of Model 2 was not significant, △R² = 0.0002, F(2, 6944) = 1.283, p > 0.05. The moderating effect of Model 3 was not significant, △R² = 0.0005, F(2, 6944) = 1.945, p > 0.05. Therefore, the moderating effect of PS between income and PA or NA was not significant, and hypotheses 3b and 3c were not tested.

In order to reveal more clearly the moderating role of PS, we use pick-a-point approach for simple slopes test. As shown in Fig. 2, when PS was low (mean-SD = 2.88), the overall test results indicated that the impact of income on LS varied significantly, F (2,6944) = 11.252, p < 0.001. The paired test was continued. The results of the paired test show that the simple slope of the Inc-L is −0.236 (t = −4.682, p < 0.001), which indicates that the low income in comparison with high income significantly reduces urban residents’ LS. The simple slope of the Inc-M is −0.124 (t = −2.401, p < 0.05), indicating that compared to those with high incomes, urban residents with middle incomes have significantly less LS. When PS was high (mean + SD = 4.08), the overall test results were significant, F (2,6944) = 51.422, p < 0.001. The paired test was continued. The results show that the simple slope of the Inc-L is −0.490 (t = −10.091, p < 0.001), which indicates that the low income in comparison with high income significantly reduces LS. The simple slope of the Inc-M is −0.285(t = −6.155, p < 0.001), indicating that compared to those with high incomes, urban residents with middle incomes have significantly less LS.

Fig. 2.

Interaction between income and PS on LS

According to Hayes (2017) and Fang & Wen (2022) [74, 75], Johnson-Neyman (J-N) method was used to conduct a simple slope test, as shown in Fig. 3. The analysis reveals that when PS scores range between [2.43, 5], the 95% confidence intervals for the simple slopes do not include zero, indicating significant simple slopes. This suggests that, compared to high-income levels, urban residents' LS significantly decreases at low-income levels within this range of PS. Similarly, for PS scores between [2.78, 5], the 95% confidence intervals for the simple slopes also exclude zero, signifying significant simple slopes. This indicates that, relative to high-income levels, urban residents' LS is significantly lower at medium-income levels within this PS range.

Fig. 3.

The simple slope graph derived from the Johnson-Neyman (J-N) analysis

Heterogeneity test

The above results have shown that income and PS affect the SWB of Chinese urban residents. However, different groups tend to have large differences in a variety of aspects, such as background, resources, and opportunities [76], which in turn may differ in the degree of influence on the relationship between income and SWB. It has been shown that the U-shaped curve of age and SWB is mostly evident in the middle-income group, whereas for the higher-income group, happiness does not change much with age [9], and there is a correlation between education and SWB [77, 78]. Thus, the study's additional focus is on group heterogeneity in terms of gender, age and education.

Analyze the difference of income and PS on SWB of urban residents of different genders. As shown in Table 6, lower income significantly reduces men's and women's life satisfaction, and women's life satisfaction is more affected by income. The lower income level significantly reduces the PA of both men and women, and the PA of men is more affected by income. PS significantly affects residents' LS and PA. However, the difference test of surtest regression coefficient is not significant. These findings may reflect gendered societal pressures and normative constraints. For example, obstacles to women's career development have made women more sensitive to work and income. Becchetti& Conzo [79] found that women's satisfaction with life is more easily affected by possible life events. When the female workforce is hit, lower incomes may not be able to meet women's basic needs, which has an impact on life satisfaction. When facing economic pressure, men may adopt avoidance or suppression strategies due to social norms, which may exacerbate emotional problems [80].

Table 6.

Heterogeneity analysis based on gender

Variables	LS		PA		NA
	Male	Female	Male	Female	Male	Female
Inc-L	−0.397***	−0.438***	−0.300***	−0.252***	−0.012	0.041
	(−7.936)	(−9.313)	(−7.853)	(−7.010)	(−0.312)	(1.167)
Inc-M	−0.222***	−0.268***	−0.183***	−0.142***	−0.002	0.046
	(−4.691)	(−5.681)	(−5.058)	(−3.940)	(−0.044)	(1.289)
PS	1.105***	1.102***	1.147***	1.127***	−0.371***	−0.430***
	(33.716)	(35.285)	(45.735)	(47.233)	(−14.766)	(−18.355)
Constant	0.231	0.297*	0.381***	0.404***	4.053***	4.169***
	(1.895)	(2.530)	(4.075)	(4.510)	(43.342)	(47.421)
N	3480	3473	3480	3473	3480	3473
adj. R2	0.272	0.291	0.397	0.407	0.059	0.090

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security, Inc-L Low-income as a reference to high-income, Inc-M Middle-income as a reference to high-income

t statistics in parentheses

^*p < 0.05

^**p < 0.01

^***p < 0.001

The sample was divided into young and middle-aged to analyze the differences between the income and PS of urban residents of different ages on SWB. As shown in Table 7, the results indicate that lower income significantly reduces LS and PA for both youth and middle-aged, increases NA for youth residents, and decreases NA for middle-aged residents. PS significantly improves SWB. The test of difference in regression coefficients using seemingly unrelated estimation found that there was a difference in the effect of income on the LS of residents of different ages, with youth having a significantly higher impact than middle aged. PS significantly affected LS and PA, and had a significantly higher effect on middle aged than on youth. This may be due to the fact that young residents face greater competitive pressure and need to face the phenomenon of difficult employment under high demand [81]. And negative labor market shocks affect the youth employment group [82]. Bleak career prospects and workplace competition lead to lower LS. While middle-aged residents are in a more stable state of income at work compared to young people, they may have a mid-life crisis, which leads to a bottleneck stage in career development with the growth of age and the deterioration of physical functions, thus leading to career anxiety as the needs of personal development cannot be met [83].

Table 7.

Heterogeneity analysis based on age

Variables	LS		PA		NA
	Youth	Middle age	Youth	Middle age	Youth	Middle age
Inc-L	−0.443***	−0.367***	−0.279***	−0.266***	0.095*	−0.084*
	(−8.999)	(−7.658)	(−7.284)	(−7.374)	(2.514)	(−2.321)
Inc-M	−0.322***	−0.157***	−0.172***	−0.150***	0.091*	−0.045
	(−6.570)	(−3.450)	(−4.509)	(−4.377)	(2.434)	(−1.315)
PS	1.075***	1.128***	1.101***	1.171***	−0.396***	−0.406***
	(33.428)	(35.670)	(44.004)	(49.050)	(−16.090)	(−17.054)
Constant	0.319**	0.212	0.504***	0.283**	4.089***	4.139***
	(2.649)	(1.795)	(5.369)	(3.181)	(44.335)	(46.530)
N	3485	3468	3485	3468	3485	3468
adj. R2	0.269	0.293	0.374	0.429	0.073	0.077

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security, Inc-L Low-income as a reference to high-income, Inc-M middle-income as a reference to high-income

t statistics in parentheses

^*p < 0.05

^**p < 0.01

^***p < 0.001

Education level is an important indicator of quality of life and social development. According to Table 8, income significantly impacted the LS at different levels of education. Inc-L on LS was stronger with a high education level, while the effect of Inc-M was higher with a low education level, but the coefficients are not significantly different. The impact of income on PA was higher among less educated residents, and the results of seemingly unrelated estimations found a significant difference between the two groups. PS significantly impacts SWB of residents with different levels of education, with a greater effect on residents with higher levels of education. Relatively speaking, residents with higher education levels are better at communicating and are able to deal with conflicts in their interactions flexibly, giving individuals stable emotional support [84]. They are also able to find better jobs, but they are more likely to focus on falling short of income expectations [85]. Individuals with higher education and income are inclined to maintain or lower their evaluation of their economic status [86], which has an impact on well-being.

Table 8.

Heterogeneity analysis based on educational attainment

Variables	LS		PA		NA
	Low	High	Low	High	Low	High
Inc-L	−0.339***	−0.395***	−0.375***	−0.213***	−0.023	0.022
	(−4.329)	(−9.865)	(−6.465)	(−6.870)	(−0.398)	(0.726)
Inc-M	−0.248**	−0.218***	−0.250***	−0.135***	0.019	0.019
	(−3.010)	(−5.982)	(−4.100)	(−4.748)	(0.318)	(0.678)
PS	0.987***	1.145***	1.066***	1.162***	−0.366***	−0.416***
	(22.859)	(43.223)	(33.346)	(56.537)	(−11.456)	(−20.395)
Constant	0.520**	0.140	0.649***	0.301***	4.024***	4.161***
	(3.115)	(1.424)	(5.257)	(3.936)	(32.549)	(54.915)
N	1947	5006	1947	5006	1947	5006
adj. R2	0.224	0.295	0.382	0.403	0.062	0.078

Abbreviations: LS Life satisfaction, PA Positive affect, NA Negative affect, PS Psychological security, Inc-L Low-income as a reference to high-income, Inc-M Middle-income as a reference to high-income

t statistics in parentheses

*p <0.05

**p <0.01

***p <0.001

Discussion

This study focuses on the relationship between income and SWB among Chinese urban residents and proposes the mediating and moderating effect of PS. The findings support most hypotheses. There are significant differences in SWB across income levels, with high-income residents having significantly higher LS and PA and significantly lower NA than low-income residents. PS acts as a mediator in the link between income and LS, PA, or NA. And income can indirectly act on SWB by increasing residents' PS. PS positively moderates the link between income and LS. In addition, income and PS are stronger predictors of LS and PA, while they are weaker predictors of NA and differ by age and education level.

First, from these findings, we find that income has a significant effect on LS, both PA and NA. Higher income correlates with higher LS and PA and lower NA. This result affirms the important role of residents' income on SWB, consistent with various research [49, 87]. This result suggests that income-related need fulfillment remains the main source of happiness for urban residents in China.

Second, we deepen our understanding of the role of PS. Previous research considering intra-individual intrinsic psychological mechanisms in the relationship between income and SWB has mostly focused on relative income or income inequality based on social comparison theory and basic needs [10]. Differences between personal resources and needs, goals, or desires are associated with lower SWB [19, 88]. PS, as an individual's subjective feeling, reflects both psychological need to pursue security and the evaluation of income. Consequently, this study emphasizes the psychological variable of PS.

We propose and validate the mediating role of PS in the relationship between income and SWB, and that income increases SWB by increasing the PS of the population. This result supports previous theoretical perspectives and empirical findings. Low-income groups are prone to a state of psychological insecurity due to relative deprivation when comparing upward with a reference group, and an individual's PS state affects their ability to handle future risks and stress [89]. High or prolonged levels of stress can deplete an individual's resources and psychological energy to cope with stress and reduce SWB [90]. We examined the moderating effect of PS. PS plays a positive moderating role in the relationship between income and LS, and it is more obvious under high income, but there is no proof that PS can regulate the relationship between income and PA or NA. This result affirms the influence of PS on the relationship between income and SWB from a cognitive perspective. Compared to individuals with high PS, individuals with low PS, even though their income is boosted, do not meet their basic psychological needs, and money does not maximize the quality of life. In conclusion, the present study links PS as a mediator and moderator variable with income and LS, PA and NA, respectively, which enriches the study of the psychological mechanisms underlying the impact of income on SWB, and provides new perspectives for unraveling the mystery of happiness.

Furthermore, the effect strength of income on the emotional dimension is significantly weaker than on the cognitive dimension. This phenomenon may stem from the fact that the positive affect generated by income growth are easily eroded by hedonic adaptation [91], whereas life satisfaction, as a cognitive assessment, remains more stable [10]. Simply increasing income may fail to improve emotional well-being in the long term, necessitating complementary social policies such as reducing working hours to enhance opportunities for leisure and social interaction [92]. Moreover, the rapid development of internet applications has introduced diversified income sources and upward social comparisons for the public. While this may foster a sense of social deprivation and reduce psychological security, virtual communities simultaneously provide additional psychological support, and these dynamics collectively reshape the relationships among income, psychological security, and subjective well-being [93].

Finally, the study selected Chinese urban residents as the research object, focusing on the current situation of PS and SWB of Chinese urban residents from 2018 to 2020. Empirical research on the relationship between income and the three dimensions of SWB was supplemented in the context of Chinese culture.

Here, this paper emphasizes and proposes the following countermeasures to enhance SWB. First, efforts should be made to raise the income of low-income groups and expand the middle-income group. Raising the income of low-income groups can increase their sense of fulfillment of physiological needs and enable them to obtain better medical insurance or other objective benefits, so as to improve the public's sense of security and achieve a higher sense of well-being. China’s Fourteenth Five-Year Plan specifically emphasizes expanding the middle-income group. Our findings reinforce the significance of this goal the strategic importance of this response. Second, expanding the scope of unemployment insurance coverage. In the current social context, there will always be low-income groups, so strengthening the improvement of the unemployment insurance system, constantly improving the protection of the unemployed, and continuing to promote the re-employment of the unemployed will help to alleviate the burden on individuals or families, and to obtain relatively better material or spiritual pursuits, thereby enhancing the SWB. Third, attention should be paid to the environment and quality of employment for young people and to the medical security and psychological problems of middle-aged residents. Finally, it should publicize psychological assistance services and correctly guide the public through psychological adjustment. The government should focus on the PS of urban residents and effectively understand the psychological problems of the public. Especially in the Internet era of extremely rapid information dissemination and high content complexity, it should monitor network information in a timely manner to minimize the negative impact of public security incidents on individual psychology. It should also continue to strengthen the construction of a social psychological service system and carry out and correctly guide the public's psychological adjustment so as to enhance the public's SWB by improving their PS.

This study has some limitations. Firstly, we found different degrees of influence between income and the dimensions of SWB. Income was a strong predictor of LS and PA, but a weak predictor of NA. In contrast, previous research has found that income is a stronger predictor of life evaluations but a much weaker predictor of positive and negative affect [18]. Then, why there is a difference in the effect of income on PA needs to be further investigated. Diener & Lucas et al. [10] also mentioned the link between income and affect as an important direction for future research in their review. Secondly, the study only takes Chinese urban residents as the object of study, but there is an imbalance in development between urban and rural areas. The status of income, psychological security, and SWB in rural areas and differences in the relationship between them between rural and urban areas have yet to be examined. In addition, many scholars pay attention to the cross-country/regional research on income and subjective well-being [94, 95], however, this paper only examined psychological security in the context of Chinese culture, and whether it is applicable to countries with different cultural backgrounds remains to be verified. Therefore, future studies can broaden the regional sources of participants and investigate the relationship between income, psychological security and subjective well-being in different countries. Finally, the study data are cross-sectional and the findings should not be interpreted as evidence of causation. As Spector (2019) notes cross-sectional surveys are more appropriate for exploratory investigations where the anticipated patterns among variables remain theoretically uncleared [96, 97]. This methodological choice aligns with the study's objective to examine whether and how psychological security mediates the relationship between income and subjective well-being. While significant associations were identified among income, psychological security, and well-being outcomes, the non-longitudinal nature of the data constrains causal inference. To enhance ecological validity and establish temporal precedence, future research should prioritize longitudinal designs incorporating time-lagged measurements and counterfactual frameworks.

Conclusions

Based on social comparison theory and needs theory, this paper proposes and validates another psychological variable, psychological security, which affects the relationship between income and subjective well-being. The results show that to improve the subjective well-being of urban residents in China, we should not only consider the increase of income, but also pay attention to the construction of psychological security, and there are differences in the impact of income or psychological security of different age groups and educational groups on subjective well-being. Therefore, it is necessary to pay attention to the difference in needs of different groups to improve the subjective well-being of the public.

Abbreviations

SWB

Subjective well-being

LS

Life satisfaction

PA

Positive affect

NA

Negative affect

PS

Psychological security

Authors’ contributions

Thank all authors for their efforts in study conception and design. Material preparation, data collection and analysis were performed by HC, HL, and YG. The first draft of the manuscript was written by HC. HL and YG reviewed and revised the manuscript. HL and YG collected the data. HC analyzed and interpreted the data. All authors commented on previous versions of the manuscript.

Funding

This study received no specific funding from any funding agency.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ethics approval was obtained from the ethical committee of School of Economics and Management, China University of Geosciences (Beijing). The work process was performed in accordance with the Declaration of Helsinki. The purpose of the study, the choice of voluntary withdrawal from the study, and anonymity were explained at the beginning of the formal questionnaire, and informed consent was obtained from the participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.