The association between financial strain and volatility in daily relationship uncertainty: A dyadic investigation

Abstract

Prior research suggests that financial strain negatively impacts relational outcomes and that fluctuations (i.e., volatility) in daily reports of relationship aspects may be detrimental for relational outcomes. Daily relational uncertainty may also be associated with financial stressors; however, little is known about the association between financial strain and levels of daily relational uncertainty, as well as the volatility in day-to-day relational uncertainty. The current study includes both members of 100 adult different-sex couples (relationship length M = 7.0 years, SD = 7.1) who completed 14 days of daily diaries. We examined whether greater baseline financial strain is associated with higher levels of daily relational uncertainty and greater day-to-day volatility in relational uncertainty among actors and partners. Individuals who reported greater financial strain also reported higher overall levels of daily relationship uncertainty, as well as greater volatility in daily relationship uncertainty. The association between actor financial strain and volatility in daily relationship uncertainty was moderated by gender and marital status, such that financial strain was only associated with greater volatility in daily relationship uncertainty for men (but not for women) and for unmarried (but not married) individuals. Evidence for partner effects were also found, where partners’ higher levels of financial strain was associated with less volatility in actors’ daily relational uncertainty; however, this relationship was moderated by income, gender, and marital status. Individuals with lower income levels (versus high income levels) reported less volatility in their daily relationship uncertainty when their partners reported higher financial strain. Males rather than women reported lower volatility in daily relational uncertainty when their partners reported greater financial strain. In addition, unmarried rather than married individuals reported greater volatility in daily relationship uncertainty when their partners reported higher financial strain. Implications for future research and practice are discussed.

Keywords

Daily diary dyads financial strain relational uncertainty volatility

Introduction

Financial strain (i.e., attitudes of worry, concern, and stress surrounding perceived financial problems) is associated with myriad negative outcomes for individuals and interpersonal relationships (Bask et al., 2020; Conger et al., 1990; Gudmunson et al., 2007; Vinokur et al., 1996; Williams & Cheadle, 2016). For instance, financial stress is associated with depression, anxiety, and poor physical health (Ahnquist et al., 2012; Butterworth et al., 2009; Tran et al., 2018), as well as reports of decreased relationship satisfaction for certain individuals (Totenhagen et al., 2018). Married couples’ reports of financial dissatisfaction and worries have also been linked to increased emotional distress (for both husbands and wives) and contribute to an increased frequency of disagreements between couples (Gudmunson et al., 2007). In addition, other evidence suggests that when individuals experience financial strain, their partners are likely to report some financial strain as well (Falconier & Epstein, 2010).

Although a robust literature has demonstrated the detrimental associations between financial strain and relationship outcomes cross-sectionally (e.g., Conger et al., 1990) and longitudinally (e.g., Masarik et al., 2016; Vinokur et al., 1996), the majority of this literature focuses on understanding partners’ overall evaluations of relationship quality and stability at the trait level (i.e., individuals’ average levels, or between-person differences, in relationship outcomes). Yet, there is compelling evidence that volatility, or within-person variability (i.e., state levels), in reported relational aspects may be uniquely detrimental for relational outcomes. For example, fluctuations (i.e., volatility) in reports of relationship quality is associated with poorer individual outcomes (e.g., depression and psychological distress; Whitton et al., 2014; Whitton & Whisman, 2010) as well as negative relational outcomes (e.g., break-up; Arriaga, 2001; Arriaga et al., 2006). Importantly, these effects of volatility in perceptions of the relationship on relational outcomes remain even after controlling for overall average levels of relationship quality (Arriaga, 2001; Campbell et al., 2010). Thus, it is important for researchers to understand what predicts both levels of and volatility in relationship evaluations. In the present study, we focus on understanding how financial strain is associated with levels of relational uncertainty, as well as day-to-day volatility in relational uncertainty over a period of 14 days.

Relational uncertainty encapsulates sources of ambiguity related to self, partner, and relational topics (Knobloch & Solomon, 1999; Knobloch & Theiss, 2011) and has been found to be associated with lower reports of relationship satisfaction, cohesion, and affection, as well as increased relational turmoil (e.g., the reporting of intense irritations and perceived hurtful interactions between partners; Knobloch & Knobloch-Fedders, 2010; Theiss & Knobloch, 2009; Theiss et al., 2009). Relational uncertainty is a fundamental component of relational well-being (Knobloch & Solomon, 2002; Weigel et al., 2011) and people with higher uncertainty are also more prone to breakup (Weigel et al., 2011). Indeed, people who are highly uncertain about their relationship are also more likely to avoid conflict-inducing topics that may threaten the relationship, such as politics or religion (Knobloch & Carpenter-Theune, 2004). The discussion of finances may also highlight dissimilarities or conflict between couples, whereby previous research has found financial disagreements to be associated with a greater likelihood of divorce (Dew et al., 2012). In sum, we examine relational uncertainty as our outcome of interest given: (a) the association between relational uncertainty and other negative relational outcomes, and (b) relational uncertainty may be a particularly salient outcome to examine relative to the presence of financial strain.

The association between financial strain and relational uncertainty

Prior evidence suggests that financial strain may impact relational outcomes due to shared or common financial stressors experienced by both partners (e.g., reduced household income) and/or the contagion of financial strain from one partner to the other (Falconier & Epstein, 2011). Support of these hypotheses are in line with previous research, which suggests that an individual’s experience of financial strain may lead to spillover into other life domains, particularly in interpersonal relationships (Wheeler & Kerpelman, 2016). However, much of this research has focused on evaluations of relational satisfaction and stability instead of relational uncertainty. For example, evidence suggests that financial strain may be indirectly and negatively associated with indicators of relational quality—such as relationship satisfaction, stability, and commitment—due to the impact of financial strain on partners’ behaviors and moods, which likely spill over into partner interactions (Conger et al., 1994; Falconier & Epstein, 2011; Vinokur et al., 1996). These findings may be understood through an interdependence theory framework (Kelley, 1979; Kelley & Thibaut, 1978), which posits that interpersonal interactions are constituted by both actor and partner effects (e.g., outcomes are influenced by individuals’ own behaviors and moods, as well as by their partners’ behaviors and moods). Thus, these exchanges between partners will motivate individuals to seek to maximize perceived rewards and minimize perceived costs associated with the relationship. Enduring financial strain may, in fact, be considered a cost individuals experience that reduces the likelihood of experiencing high quality and stable relationships (i.e., low relationship uncertainty). Thus, we expect greater financial strain will be associated with greater relational uncertainty for both individuals and their partners.

Given the prior work examining spillover of financial stressors onto relational outcomes, as well as the emphasis on interdependence between partners, it may be prudent to test whether actor and partner reports of financial strain are associated with feelings of relational uncertainty. Couples who exhibit uncertainty or doubt about the ability to meet financial obligations may report uncertainty about the romantic relationship as well. Prior evidence suggests that the presence of relational uncertainty may hinder partners’ discussions of difficult topics (e.g., topics that may threaten the relationship; Knobloch & Carpenter-Theune, 2004) and financial strain could certainly be categorized as a difficult topic for some couples to discuss (Falconier & Epstein, 2011). Nonetheless, limited prior research has examined the association between actor and partner financial strain and relational uncertainty, as well as whether reports of relational uncertainty vary from day-to-day among couples with financial strain. Testing for within-person variability (i.e., volatility) in daily relational uncertainty and whether financial strain predicts such volatility is an important extension of family finance-related research. Recent evidence suggests that myriad relational outcomes—such as satisfaction, commitment, closeness, conflict, ambivalence, maintenance, and love—fluctuate from day-to-day among couples (Totenhagen et al., 2016), and that financial satisfaction and stress also fluctuate from day-to-day (Totenhagen et al., 2018). Thus, we build on the prior literature to examine whether financial strain predicts both levels of daily relationship uncertainty as well as within-person volatility in daily relationship uncertainty. This evidence has important practical implications, as it may help to direct practitioners’ attention to the ways financial strain may increase not only levels of relationship uncertainty, but fluctuations in evaluations of the relationship as well.

The roles of income, gender, and marital status

As previously indicated, we frame our examination of the associations between financial strain and relationship uncertainty through an interdependence theory lens, which emphasizes the importance of the behaviors, cognitions, and experiences of each partner as influencing both their own and their partner’s outcomes (Kelley, 1979; Kelley & Thibaut, 1978). There is compelling evidence, however, that the spillover of stressful experiences such as financial strain, as well as cross-over of this stress from one partner to another, may be stronger for some individuals than others. For example, the vulnerability stress adaptation model (VSA; Karney & Bradbury, 1995) suggests that some individuals enter relationships with vulnerabilities that leave them less well-equipped to adapt to stressors and thus more likely to experience lower quality and more unstable relationships.

Although individuals at all income levels may experience financial strain, it may be particularly difficult for those with low-income status to successfully cope with their financial strain and associated physical and psychosocial stressors (Evans & Kim, 2013; Wadsworth, 2012). Indeed, previous research has shown that the correlates of chronic poverty—i.e., physical stressors (e.g., chaotic environments and housing instability) and psychosocial stressors (e.g., relationship turmoil)—contribute to dysregulated stress responses among individuals over time (Evans & Kim, 2007, 2013). Thus, individuals with histories of low-income status may be unable to effectively manage environmental and internal stressors due to disrupted self-regulatory systems (Evans & Kim, 2013). Moreover, low-income individuals may be more negatively impacted by their financial strain than individuals in higher income brackets because low-income individuals may lack the resources to effectively manage financial stressors. Previous research has found that low-income individuals with high physiological arousal may be unable to successfully engage in coping strategies (e.g., problem solving skills, emotion regulation, and active acceptance) for managing poverty-related stressors (Santiago et al., 2012; Wadsworth, 2012). In the context of the current study, we test whether household income moderates the associations between financial strain and levels of or volatility in daily relational uncertainty for both individuals and their partners.

Further, gender may also moderate the associations between financial strain and levels of and volatility in relational uncertainty. Falconier and Epstein (2010) examined gender differences in the influence of economic strain on relational outcomes (i.e., relationship satisfaction) among a sample of different-sex couples. Their findings suggest that although women reported significantly more economic strain than did men, it was men’s reports of economic strain that influenced both his own (actor effect) and his partner’s (partner effect) relational behaviors (e.g., displaying more psychological aggression toward each other). Moreover, women were less likely to display positive behaviors toward their partners and report lower relationship satisfaction when their male partners reported greater levels of economic strain (Falconier & Epstein, 2010). To our knowledge, no prior studies have examined whether the relationship between financial strain and levels/volatility in daily relational uncertainty is moderated by gender. Thus, we will test whether there are gender differences in the relationships between financial strain and levels and/or volatility in daily relational uncertainty reported by individuals and their partners.

Finally, marital status may also be important to consider within the context of the current study. Previous research suggests that married individuals report lower levels of economic/money-related stress than do individuals who identify as “single” across several categories (e.g., never married/not dating, never married/casually dating, separated, and divorced; Ta et al., 2017). Therefore, marriage may serve as a protective factor against the negative influences of financial strain on both individual-level and relationship outcomes. Experiences with relational uncertainty, moreover, may differ between married and unmarried/dating couples (Knobloch, 2008). For example, whereas uncertainty about dating relationships may originate from internal sources (e.g., doubts about the compatibility of partners and the mutuality of commitment levels), uncertainty within marital relationships could stem from external factors that may change the status of the relationship, such as decisions to have children, careers, and financial status (Knobloch, 2008). Thus, we examine marital status (in addition to income and gender) as a potential moderator of the associations between actor and partner financial strain and levels of and volatility in daily relational uncertainty.

Current study

We were interested in understanding the associations between actor and partner reports of baseline financial strain, as well as the levels of and volatility in daily relational uncertainty. Relatively little is known about the associations between financial strain and daily relational uncertainty among couples due to the reliance on cross-sectional and/or traditional longitudinal designs. These traditional methodologies may be more prone to recall bias, whereas intensive longitudinal designs such as daily diaries can capture these within-person, day-level changes as they occur. Thus, we used a dyadic daily diary design to examine the link between baseline financial strain and levels of daily relational uncertainty (fixed effects), as well as volatility in daily relational uncertainty (volatility effects), among a sample of different-sex couples. Given the interdependence between partners (Kelley & Thibaut, 1978), we also examined partner effects. Moreover, we examined the potential for household income, gender, and marital status to moderate the relationship between financial strain and daily relational uncertainty. Given the literature reviewed above, we pose one hypothesis and two research questions:

H1: Greater baseline financial strain is associated with higher levels of daily relational uncertainty for both individuals and partners.

RQ1: Is greater baseline financial strain associated with greater volatility in daily relational uncertainty for both individuals and partners?

RQ2: Do (a) household income, (b) gender, and (c) marital status moderate the above associations between financial strain and levels of or volatility in daily relational uncertainty for individuals or partners?

Methods

Procedure and participants

Participants of the current study included both members of adult romantic couples. Couples were recruited through flyers distributed both online and in hard-copy through university and community venues in a geographical region surrounding a large university in the Southeastern United States. To be eligible for the study, both partners had to agree to participate in the study, had to be in a relationship together for at least 6 weeks, be of adult age (i.e., age 19 or older), and have their own individual email addresses. Interested individuals contacted the research team via email to determine whether they and their partners were eligible for the study. Partners in eligible couples were enrolled in the study and contacted separately to provide each participant with their own identification number which corresponded to their surveys to preserve confidentiality.

Participants were sent an initial survey link to complete a baseline survey that inquired about demographics (e.g., gender, income, and ethnicity) and other individual and relationship constructs not expected to vary from day to day (e.g., religiosity and attachment). Upon completion of the baseline survey, participants received an email link to the daily diary surveys and an invitation with instructions to opt-in to daily text reminders to complete the daily surveys. Text message reminders were sent through Survey Signal (Hofmann & Patel, 2015), which sends SMS messages to remind/notify participants to complete surveys and contains the link to the online survey in Qualtrics. Participants could also directly access the daily survey link through the email sent daily. Undergraduate and graduate research assistants monitored daily survey submissions and contacted participants who missed two or more days in a row by email to remind them to complete surveys. Participants were asked to complete 14 consecutive days of daily surveys each night, between 7 p.m. and midnight. Participants were each compensated up to $38 in the form of Amazon gift cards. At the end of their participation, they received $5 for completing the initial survey, $2 for each daily diary survey completed (up to 14 days), and a $5 bonus if all 14 days were completed consecutively. All study procedures were approved by the Institutional Review Board at the University of Alabama (Protocol 13-OR-391 for the study titled, “Sacrifices, Hassles and Relationship Experiences” [SHARE]. Data were collected in 2014-2015.

The total sample included 132 couples. Participants were dropped from analyses for the present study if only one partner completed the initial survey (n = 5). Given the small number of participants who were not in different-sex couples, an additional six couples were removed from analyses (i.e., 3 female couples, 1 male couple, and 2 couples in which one partner did not identify as male or female [i.e., agender or nonbinary]). Finally, because we focus on within-person variability as well as actor and partner effects, participants were dropped from analyses if both they and their partner did not complete at least three matching days of daily data. The final sample was 100 different-sex couples (200 individuals). Participant compliance with the daily diaries for these participants was generally good; 2956 observations were read for our model with 2250 valid responses (76%) and 706 missed/skipped days (24%). On average participants in the analytic sample completed 11.81 days of data (SD = 3.09). Number of days completed was not significantly associated with relationship length, household income, or daily relational uncertainty. Number of days completed was, however, negatively correlated with financial strain (r = −0.18, p = 0.01), such that participants with higher financial strain completed fewer days of data.

Among the participants who identified as different-sex couples and were included in the analyses, 94% of women and 97% of men identified as heterosexual. Overall, participants were largely Caucasian, completed at least some college or more and were approximately 30 years of age (M_age 30 years, SD = 3.09, range 19-65). Approximately 67% of participants reported cohabiting with their partner, and about half of the sample was married (51% were not married); of those who were not married, the majority (92% of women and 96% of men) reported seriously dating or being in lifelong commitments. 87% of women and 79% of men reported residing in Alabama, with approximately 85-90% of all participants residing in the southeastern U.S. Detailed demographic information can be found in Table 1.

Table 1.

Participant demographics.

	Women	Men
Race/ethnicity
Caucasian or White	90	92
Hispanic or Latin American origin	4	1
African-American or Black	3	2
Asian or Pacific Islander	2	0
American Indian or Alaska Native	0	1
Mixed race/ethnicity	1	4
Household income
Below $20,000	23	16
$20,000–$39,999	17	22
$40,000–$59,999	21	20
$60,000–$79,999	15	16
$80,000–$99,999	10	11
$100,000–$119,999	6	8
$120,000–$139,999	4	4
$140,000–$159,999	3	0
Above $160,000	1	3
Education level
Less than high school	0	0
Some high school	0	0
Graduated high school/GED	0	5
Some college	22	25
Associate’s or technical degree	7	3
Bachelor’s degree	21	21
Some graduate school	16	13
Master’s degree	30	20
Degree such as M.D. or Ph.D.	4	13
Currently a student
Yes	46	36
No	54	63
Missing	0	1
Sexual orientation
Heterosexual	94	97
Gay/lesbian	0	1
Bisexual	5	1
Queer	1	0
Missing	0	1
Marital status^a
Married	49	47
Not married	51	51
Missing	0	2
Relationship involvement for unmarried participants (N=101)
Casually dating	2	0
Seriously dating	28	22
Lifelong commitment	18	27
Other	2	2
Cohabitation status
Cohabiting	68	66
Not cohabiting	32	33
Missing	0	1
Have children living at home
Yes	33	34
No	67	66
State of residence
Alabama	87	79
Florida	1	2
Georgia	2	2
Tennessee	1	1
West Virginia	1	1
Other	8	15
Age (M, SD)	29.22 (8.37)	30.69 (8.68)

Note: Numbers are provided for each category and correspond to percentages since our sample is comprised of 100 men and 100 women; a = 2 men did not provide information for marital status.

Measures

Daily relationship uncertainty

Relational uncertainty was measured with four items indicating how certain an individual was of their relationship based on previous research (Knobloch & Theiss, 2011). The items were adapted for daily use and asked in reference to the statement, “Today, how certain did you feel about…”, including (1) the current status of your relationship, (2) how you can or cannot behave around your partner, (3) the definition of your relationship, and (4) the future of your relationship. Response options to all items were (1) completely certain, (2) very certain, (3) somewhat certain, (4) somewhat uncertain, (5) very uncertain, and (6) completely uncertain. The mean of the four items were used to create a measure of daily relationship uncertainty, where higher scores indicated greater relational uncertainty. We tested Cronbach’s alpha for each day (1−14) and found that the scale performed well on a daily basis (α ranged 0.82−0.97 for women; 0.90−0.96 for men).

Baseline financial strain

Financial strain was assessed during the baseline survey with an item that asked, “During the past year, how much difficulty have you had with paying your bills? Would you say you have had…”, with response categories of (4) a great deal of difficulty, (3) quite a bit of difficulty, (2) some difficulty, (1) a little difficulty, and (0) no difficulty at all.

Moderators and covariates

Household income, gender, and marital status were assessed at baseline. Gender and marital status were effect coded as the following: female = −0.5 and male = 0.5; unmarried = −0.5 and married = 0.5. Household income was coded with nine ordinal categories (i.e., 1 = “Below $20,000” to 9 = “Above $160,000”). Relationship length was also included as a covariate in all analyses (Women: Mean years = 6.76; Men: Mean years = 7.12).

Statistical analysis

Multilevel modeling (MLM) was employed in SAS PROC MIXED to account for nonindependence in the data (i.e., days nested within persons and persons nested within dyads; Kenny et al., 2006). The data in the current study are characterized by a three-level structure, where day is Level 1, persons are Level 2, and dyads are Level 3. However, there is no variability in the 3-level dyadic data MLM because there are only two members in each dyad. Dummy coded variables for men and women were added to the random line in the model to account for the structure and interdependences in the data, as well as the error structure (Bolger & Laurenceau, 2013; Curran et al., 2015). Finally, this model specification estimates interindividual differences in men’s and women’s intercepts on relational uncertainty. Therefore, the model is best characterized as a 2-level model, where day (Level 1) and men and women are specified within couples (Level 2).

Specifically, we used modeling appropriate for longitudinal data with distinguishable dyads (i.e., men paired with women) using a single intercept for men and women and testing gender differences via moderation similar to the analytic technique employed by Campbell et al. (2005). Gender and marital status were effect coded so that conditional main effects could be interpreted as pooled across gender and marital status. Household income and actor and partner financial strain were grand-mean centered prior to analyses.

To test hypotheses and research questions, we ran a single model with daily relational uncertainty specified as the dependent variable; predictors of levels of daily relational uncertainty were entered on the model line, and predictors of volatility in daily relational uncertainty were exponentiated and entered on the repeated line, as further described below. Specific to levels of daily relational uncertainty, we entered on the model line the covariate (relationship length), the demographics (gender, marital status, and household income), actor and partner financial strain, and interactions between the demographics and actor and partner financial strain. To test the research questions specific to volatility (i.e., within-person variability) in daily relational uncertainty, we employed Hoffman’s (2007) methods to predict residual variability from the demographics (gender, marital status, and household income), actor and partner financial strain, and interactions between the demographics and actor and partner financial strain. Specifically, these variables and interactions were entered into the repeated line and exponentiated to allow for linear prediction of the variance component by gender, marital status, income, actor and partner financial strain, and their interactions. As explained by Curran et al. (2015), “Estimates of the effects of predictors of volatility must be exponentiated before being added to the residual variance to get the estimate of the residual variance at a certain level of the predictor” (p. 166). Equations from a study employing similar methodology can be found in Curran et al. (2015), and the syntax used in the present study is in an Online Supplementary File).

Results

Descriptive statistics and correlations of the main study variables for men and women in the sample can be found in Table 2. Specific to the actor-partner multilevel models, full model results can be found in Table 3. Here, we discuss results specific to our hypotheses and research questions. Significant interactions involving household income were probed at one standard deviation above and below the mean for high and low values.

Table 2.

Descriptive information and correlations.

	1	2	3	4	5	Men, M (SD)	Women, M (SD)	Range
1. Relationship length	0.99***	0.29**	0.42***	0.08	-0.11	7.12 (7.44)	6.76 (6.76)	0.17 to 39.33
2. Education level	0.20*	0.59***	0.08	0.03	−0.05	6.24 (1.88)	6.73 (1.60)	3 to 9
3. Household income	0.42***	0.15	0.74***	-0.11	-0.12	3.42 (1.94)	3.27 (1.97)	1 to 9
4. Financial strain	0.19	-0.07	-0.02	0.55***	0.06	1.10 (1.19)	1.10 (1.17)	0 to 4
5. Daily relationship uncertainty	-0.29**	-0.12	-0.25*	0.09	0.33***	1.59 (0.66)	1.62 (0.76)	1 to 4.86

Note: Correlations for men are above the diagonal, correlations for women are below the diagonal. Correlations between men and women are in bold on the diagonal.

Here, daily relationship uncertainty represents the means of individual averages across the 14 days and all correlations are between-person correlations.

Range is the range of scores reported by participants. Financial Strain is a 5-point scale coded 0–4, daily relationship uncertainty is a 6-point scale coded 1–6, and education level and household income are 9-point scales coded 1–9.

A score of 3 on household income corresponds to $40,000–$59,999 per year. A score of 6 for education level corresponds to a Bachelor's degree.

*p < 0.05, ** p < 0.01, ***p < 0.001.

Table 3.

Multilevel models of daily relational uncertainty and financial strain.

	b	SE
Fixed/regression effects (H1, RQ2)
Intercept	1.63***	0.06
Covariates
Relationship length	−0.00	0.00
Conditional main effects
Gender	0.02	0.07
Marital status	−0.45**	0.14
Income	−0.02	0.03
Actor financial strain	0.12**	0.05
Partner financial strain	0.05	0.04
Interactions
Gender × Actor financial strain	0.00	0.11
Gender × Partner financial strain	0.00	0.10
Marital status × Actor financial strain	−0.13	0.11
Marital status × Partner financial strain	−0.12	0.10
Income × Actor financial strain	0.03	0.03
Income × Partner financial strain	−0.01	0.03
Predictors of residual variance—volatility effects (RQ1, RQ2)
Conditional main effects
Gender	−0.13*	0.07
Marital status	−0.48***	0.07
Income	−0.00	0.02
Actor financial strain	0.14**	0.04
Partner financial strain	−0.08*	0.04
Interactions
Gender × Actor financial strain	0.52***	0.07
Gender × Partner financial strain	−0.17*	0.07
Marital status × Actor financial strain	−0.16*	0.08
Marital status × Partner financial strain	−0.42***	0.08
Income × Actor financial strain	0.05	0.03
Income × Partner financial strain	0.07**	0.03

Note: Significant results are in bold.

*p < 0.05, **p < 0.01, ***p < 0.001.

H1: Levels of relational uncertainty

We found partial support for H1 that higher levels of financial strain would be associated with higher levels of daily relational uncertainty for both actors and partners. The actor effect (b = 0.12, p < 0.01), but not the partner effect (b = 0.05, p = 0.24), was significant, suggesting that individuals who reported higher financial strain also reported higher overall levels of daily relationship uncertainty. Specific to our moderation research questions (RQ2a-c), none of the demographic characteristics were significant moderators of the association between financial strain and relational uncertainty, although married individuals reported lower levels of daily relational uncertainty than unmarried individuals (b = −0.45, p < 0.01).

RQ1: Volatility in relational uncertainty

In contrast with the findings for H1, there were several significant effects and interactions for RQ1 regarding within-person volatility in relational uncertainty as predicted by financial strain. First, the conditional main effects of both actor and partner financial strain were significant. When individuals reported higher baseline financial strain, they reported greater volatility in daily relationship uncertainty (b = 0.14, p < 0.01) whereas their partners reported lower volatility in daily relationship uncertainty (b = −0.08, p < 0.05). These conditional main effects, however, were qualified by significant higher-level interactions (RQ2). For example, significant interactions of actor financial strain by gender (RQ2b) (b = 0.52, p < 0.001) and actor financial strain by marital status (RQ2c) (b = −0.16, p < 0.05) emerged. In decomposing the interaction between actor financial strain and gender, we found that financial strain was only associated with higher within-person volatility in daily relationship uncertainty for men (b = 0.39, p < 0.001), but not for women (b = −0.12, p = 0.02). The decomposition of the interaction between actor financial strain and marital status suggests that when unmarried individuals report higher baseline financial strain, they also report higher within-person volatility in daily relational uncertainty (b = 0.22, p < 0.001), whereas actor financial strain was not significantly associated with volatility in daily relational uncertainty for married individuals (b = 0.05, p = 0.37).

Specific to partner effects, the conditional main effect of partner financial strain was significant. This finding suggests that individuals with partners who report higher baseline financial strain will report lower volatility in their own daily relational uncertainty (b = −0.08, p < 0.05). We also found three significant interactions: household income (RQ2a) by partner financial strain (b = 0.07, p < 0.01), gender (RQ2b) by partner financial strain (b = −0.17, p < 0.05), and marital status (RQ2c) by partner financial strain (b = −0.42, p < 0.001). In decomposing the interaction for household income (RQ2a) by partner financial strain, we found a pattern suggesting that for people with lower (-1SD) household income, when their partners reported higher financial strain, they reported lower volatility in daily relational uncertainty (b = −0.22, p < 0.001); the effect for individuals with higher (+1SD) household income was not significant (b = 0.05, p = 0.52). Next, in decomposing the interaction for gender (RQ2b) by partner financial strain, we found this relationship to be significant and negative for men (b = −0.17, p < 0.01), such that males report lower within-person volatility in daily relational uncertainty when their female partners report higher baseline financial strain. This relationship was nonsignificant for females (b = 0.00, p = 0.96). Finally, in decomposing the interaction for marital status by partner financial strain (RQ2c), we found that unmarried individuals reported greater volatility in daily relationship uncertainty when their partners reported higher financial strain (b = 0.13, p = 0.02). Moreover, this association was significant and negative for married individuals, such that married individuals report significantly lower volatility in daily relational uncertainty when their partners report higher financial strain (b = −0.29, p < 0.001).

Discussion

The current study examined the associations between actor and partner reports of baseline financial strain and the levels of and volatility in daily relational uncertainty. The findings revealed that individuals with higher baseline financial strain are more likely to report higher levels (H1) of daily relational uncertainty. In addition, married individuals reported lower levels of daily relational uncertainty than did unmarried individuals. None of the examined demographic factors (i.e., income, gender, and marital status) moderated the associations between baseline financial strain and daily levels of relational uncertainty, and no partner effects were significant. These findings suggest that individuals (but not their partners) who have greater financial strain report higher levels of relational uncertainty, regardless of income, gender, or marital status.

For the analyses predicting volatility (RQ2), or within-person fluctuations in daily relationship uncertainty, the results suggested that individuals with higher baseline financial strain reported greater volatility in daily relationship uncertainty and that this association was moderated by gender and marital status. Specifically, we found that actors’ reports of financial strain were significantly associated with greater volatility in daily relationship uncertainty for men, but not for women. Actors’ reports of financial strain were also significantly associated with greater volatility in daily relationship uncertainty for unmarried individuals, but not in those who are married.

Moreover, the current study found evidence to support that partner financial strain was significantly associated with actors’ volatility in daily relational uncertainty, suggesting that individuals with partners who report higher baseline financial strain also report lower volatility in their own daily relational uncertainty. However, these associations were found to be moderated by household income (RQ2a), gender (RQ2b), and marital status (RQ2c). Specific to household income, when their partners reported higher financial strain, individuals with lower income levels experienced less volatility in their daily relationship uncertainty than did individuals with higher income levels. Although we did not pose hypotheses specific to volatility, these findings were somewhat surprising given prior research suggesting that certain stressors (i.e., financial strain) may lead to spillover, as well as cross-over between partners, to negatively impact relational outcomes (Falconier & Epstein, 2010; Totenhagen et al., 2018). Additionally, prior research suggests that low-income individuals may be more susceptible to the negative impacts of environmental stressors (Evans & Kim, 2013)—for example, financial strain—than individuals with higher income levels. Our findings suggest that lower income individuals may display increased stability (i.e., lower volatility) in their certainty about their relationship status despite their partners’ reports of financial instability. Importantly, although lower income participants were more stable (i.e., less volatile) in their levels of uncertainty when their partners reporter greater financial strain, the results did not suggest that lower income participants had higher levels of uncertainty than did higher income participants. This pattern may be reflective of individuals choosing partners who report similar financial statuses/hardships—indeed, partners’ financial strain was significantly and positively correlated with actors’ financial strain (r = 0.55; p < 0.001). Thus, lower income individuals may feel more stable in their relationships when their partners report financial strain.

Additionally, a significant association emerged between gender and partner financial strain, such that males reported lower within-person volatility in their daily relational uncertainty when their female partners reported higher baseline financial strain. This finding is in contrast to the moderation of gender on the relationship between actors’ financial strain and reports of volatility in daily relational uncertainty (i.e., males with greater levels of baseline financial strain reported greater volatility in their own daily relationship uncertainty). Interdependence theory (Kelley, 1979; Kelley & Thibaut, 1978) may help aid in the interpretation of this finding. For example, males might feel a need to take on more of a steady and supportive role (both relationally and financially) when their female partners report greater financial strain. Indeed, previous research suggests that men report more confidence in engaging in financial management practices than do women (Alsemgeest & Grobbelaar, 2015). Importantly, these findings emerged while also controlling for relationship length; therefore, these gender roles may be stable to length of time in the relationship.

Finally, marital status significantly moderated the association between partner financial strain and volatility in daily relationship uncertainty. Also relevant to the interdependence between partners, when one partner reports high financial strain, the other partner may feel less certain about the status of their relationship and these feelings may vary day-to-day. Importantly, these associations were moderated by marital status, suggesting that unmarried (but not married) cohabiting individuals report greater volatility in their daily relational uncertainty when their partners report higher financial strain. Moreover, we also found evidence to suggest that married individuals reported significantly lower volatility in daily relational uncertainty when their partners reported higher financial strain.

The majority of the participants in the current study also reported cohabiting with their romantic partner; therefore, it is important to acknowledge the findings regarding the moderation of marital status on financial strain (in both actors and partners) and volatility in daily relationship uncertainty within the context of cohabitation. The decision of a couple to marry may not be driven only by relational factors (e.g., love), but also by economic considerations (e.g., being able to make ends meet; Cancian & Meyer, 2014). Unmarried cohabiting couples may more often characterize their relationships as vulnerable and fragile, as they do not receive the same legal and financial benefits that married couples receive (Waggoner, 2015). For example, when partners marry, spouses gain the right to claim financial resources gained during the marriage (Mahoney, 1983). In the event of relationship dissolution, unmarried couples do not have a legal right for shared property or finances as married couples do (Melton, 1990); instead, unmarried cohabiting couples themselves must decide how to split any joint finances. Unmarried cohabiting couples are also not legally viewed as a couple and “as far as the law is concerned, the partners are complete strangers to one another” (Waggoner, 2015, p. 64). Ultimately, this means that individuals may or may not be held liable for debts brought on during their relationship (Waggoner, 2015). Further, if finances had been combined during the relationship, dissolution may be even more costly for unmarried couples (Avellar & Smock, 2005; Rhoades et al., 2012). Moreover, prior research suggests that when cohabiting couples combine their consumer debt, this is associated with a greater risk of relationship dissolution (Britt-Lutter et al., 2018). For these reasons, unmarried (rather than married) individuals may display greater volatility in day-to-day relational uncertainty when they themselves, as well as their partners, report greater financial strain.

Implications

Prior research finds support for the detrimental effect of volatility on relational outcomes (e.g., Arriaga, 2001), suggesting that successful relationships are determined not only by levels of relationship constructs, but by stability in those constructs as well. Our findings suggest that men may be particularly vulnerable to volatility in their feelings of relational uncertainty in light of their own financial strain, but show greater stability in their daily relational uncertainty when their female partners report higher financial strain. Thus, men with financial strain may be an important intervention group to target in order to improve relational outcomes. In addition, our results suggest that financial strain may influence relational uncertainty more so for unmarried couples than for married couples. Cohabiting couples have a higher risk of uncertainty and instability as compared to married couples (Lamidi et al., 2019). In our study, unmarried cohabiting couples reported higher levels of daily relationship uncertainty, and they were more volatile in these perceptions of uncertainty when their partners (as well as themselves) reported high financial strain. In the U.S., marriage is not only a highly symbolic union, but it is also a legally protected union (Cherlin, 2004). There are many characteristics of marriage, like legal rights and responsibilities to joint financial resources that may influence relationship uncertainty, especially when compared with cohabiting couples. Marriage may provide a protective factor for uncertainty as it is an institutionalized union, through legal structures encourage couples to combine all aspects of their lives including finances (Cherlin, 2004), whereas cohabiting couples don’t have the legal and institutional structure encouraging combination and often they remain individualized from one another (Brines & Joyner, 1999). Focusing prevention and intervention efforts on relational outcomes among unmarried couples with financial strain may yield significant improvements in these couples’ wellbeing.

Limitations

The current study has several limitations that should be noted. First, although our measure of financial strain was chosen to capture difficulty in paying bills across various income levels in the current study, the use of a single item to assess financial strain is unlikely to capture all of the variation associated with experiencing financial stressors. Prior work has identified that varying types of financial stressors in relationships may impact the financial and relational outcomes couples experience (e.g., Eickmeyer, 2019). For example, among young adults, struggling to make ends meet increases the likelihood that a couple pools their income, however financial stressors related to jobs and personal debts don’t have the same effect within couples (Eickmeyer, 2019). Future work could benefit from measures of specific financial stressors such as fixed and current expenses (e.g., monthly mortgage or rent payments, utilities, and food), variable expenses (e.g., clothing and entertainment), and different types of debt (e.g., consumer debt like personal loans, auto loans, or student loans). Second, future research may wish to tease apart income-specific experiences with financial strain in high-versus low-income households. We do not wish to imply that individuals from low-income households are more likely to experience greater financial strain by default than are higher-income households. In reality, both types of households may experience financial strain, but these experiences may substantively differ (e.g., couples with higher incomes levels may report greater financial debt than do lower income couples, and their reports of financial strain could be reflective of these types of strain). Third, although the current study examined the associations between financial strain and daily relational uncertainty in different-sex couples within a 14-day study period, future work may wish to integrate this dyadic question with other longitudinal surveys that extend over longer periods of time. Indeed, our study provides a “snapshot” of the associations between financial strain and daily relational uncertainty in daily life, but future research is warranted to examine changes in these relationships over time, as well as the possibility of reciprocal effects (relational uncertainty may increase one’s concern about their finances). Finally, the generalizability of our findings to couples more broadly is unknown, as participants were different-sex couples primarily recruited from the southeastern U.S. Future research would benefit from examining the relationships between financial strain and daily relational uncertainty among same-sex couples and/or among couples living in different geographical regions. Moreover, the current study did not collect information on participant disability status. Future work may wish to examine the role of additional demographics (e.g., disability status and other sexual orientations) when studying relationship uncertainty.

Conclusions

The present study provides evidence that financial strain is associated with daily levels, as well as volatility (i.e., within-person fluctuations) in daily relational uncertainty in our sample of different-sex couples. Collecting daily diary data among dyads also allowed for us to examine whether actors’ and partners’ financial strain was associated their own and their partners’ levels and volatility in daily relational uncertainty. Further, our findings suggest that men and unmarried individuals who perceive greater financial stressors may be especially susceptible to greater volatility in daily relationship uncertainty. Evidence for the moderation of income level, gender, and marital status on the association between partners’ financial strain and volatility in daily relational uncertainty was also supported. These findings add to the body of evidence that has examined the role of two important factors implicated in predicting long-term relational outcomes (i.e., financial strain and relational uncertainty) with traditional methodologies, such as cross-sectional and longitudinal designs. To our knowledge, this is one of the first studies to examine the association between financial strain and daily relational uncertainty with daily dyadic data, which enabled us to examine the influence of financial strain on within-person changes in daily relational uncertainty among different-sex couples.

Supplemental Material

sj-pdf-1-spr-10.1177_02654075211056896 – Supplemental Material for The association between financial strain and volatility in daily relationship uncertainty: A dyadic investigation

Supplemental Material, sj-pdf-1-spr-10.1177_02654075211056896 for The association between financial strain and volatility in daily relationship uncertainty: A dyadic investigation by Jamie M. Gajos, Casey J. Totenhagen and Melissa J. Wilmarth in Journal of Social and Personal Relationships

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by internal funding from the College of Human Environmental Sciences at the University of Alabama.

Open research 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 are available. The data can be obtained via e-mail from the second author (Dr. Casey Totenhagen). The materials used in the research are available. The materials can be obtained via e-mail from the second author (ctotenhagen@ches.ua.edu).

ORCID iD

Jamie M. Gajos

Supplemental material

Supplemental material for this article is available online.

References

Ahnquist

Wamala

S. P.

Lindstrom

(2012). Social determinants of health–a question of social or economic capital? Interaction effects of socioeconomic factors on health outcomes. Social Science & Medicine, 74(6), 930–939. https://doi.org/10.1016/j.socscimed.2011.11.026

Alsemgeest

Grobbelaar

(2015). Spouses’ views of gender roles: Financial management in marriage. Journal of Economic and Financial Sciences, 8(3), 843–860. https://hdl.handle.net/10520/EJC181219

Arriaga

X. B.

(2001). The ups and downs of dating: Fluctuations in satisfaction in newly formed romantic relationships. Journal of Personality and Social Psychology, 80(5), 754–765. https://doi.org/10.1037/0022-3514.80.5.754

Arriaga

X. B.

Reed

J. T.

Goodfriend

Agnew

C. R.

(2006). Relationship perceptions and persistence: Do fluctuations in perceived partner commitment undermine dating relationships? Journal of Personality and Social Psychology, 91(6), 1045–1065. https://doi.org/10.1037/0022-3514.91.6.1045

Avellar

Smock

P. J.

(2005). The economic consequences of the dissolution of cohabiting unions. Journal of Marriage and Family, 67(2), 315–327. https://doi.org/10.1111/j.0022-2445.2005.00118.x

Bask

Haapakorva

Gissler

Ristikari

(2020). Growing up in economic hardship: The relationship between childhood social assistance recipiency and early adulthood obstacles. International Journal of Social Welfare. https://doi.org/10.1111/ijsw.12444. 1-20.

Bolger

Laurenceau

J.-P.

(2013). Intensive longitudinal methods: An introduction to diary and experience sampling research. Guilford Press.

Brines

Joyner

(1999). The ties that bind: Principles of cohesion in cohabitation and marriage. American Sociological Review, 64(3), 333–355. https://doi.org/10.2307/2657490

Britt-Lutter

Dorius

Lawson

(2018). The financial implications of cohabitation among young adults. Journal of Financial Planning, 31(4), 38–45.

10.

Butterworth

Rodgers

Windsor

T. D.

(2009). Financial hardship, socio-economic position and depression: Results from the PATH through life survey. Social Science & Medicine, 69(2), 229–237. https://doi.org/10.1016/j.socscimed.2009.05.008

11.

Campbell

Simpson

J. A.

Boldry

Kashy

D. A.

(2005). Perceptions of conflict and support in romantic relationships: The role of attachment anxiety. Journal of Personality and Social Psychology, 88(3), 510–531. https://doi.org/10.1037/0022-3514.88.3.510

12.

Campbell

Simpson

J. A.

Boldry

J. G.

Rubin

(2010). Trust, variability in relationship evaluations, and relationship processes. Journal of Personality and Social Psychology, 99(1), 14–31. https://doi.org/10.1037/a0019714

13.

Cancian

Meyer

D. R.

(2014). Testing the economic independence hypothesis: The effect of an exogenous increase in child support on subsequent marriage and cohabitation. Demography, 51(3), 857–880. https://doi.org/10.1007/s13524-014-0295-8

14.

Cherlin

A. J.

(2004). The deinstitutionalization of American marriage. Journal of Marriage and Family, 66(4), 848–861. https://doi.org/10.1111/j.0022-2445.2004.00058.x

15.

Conger

R. D.

Elder

G. H.

Jr. Lorenz

F. O.

Conger

K. J.

Simons

R. L.

Whitbeck

L. B.

Huck

Melby

J. N.

(1990). Linking economic hardship to marital quality and instability. Journal of Marriage and the Family, 52(3), 643–656. https://doi.org/10.2307/352931

16.

Conger

R. D.

X.-J.

Lorenz

F. O.

(1994). Economic stress and marital relations. In Conger

R. D.

Elder

G. H.

(Eds.), Families in troubled times: Adapting to change in rural America (pp. 187–203). Aldine de Gruyter.

17.

Curran

M. A.

McDaniel

B. T.

Pollitt

A. M.

Totenhagen

C. J.

(2015). Gender, emotion work, and relationship quality: A daily diary study. Sex Roles, 73(3), 157–173. https://doi.org/10.1007/s11199-015-0495-8

18.

Dew

Britt

Huston

(2012). Examining the relationship between financial issues and divorce. Family Relations, 61(4), 615–628. https://doi.org/10.1111/j.1741-3729.2012.00715.x

19.

Eickmeyer

K. J.

(2019). The “common pot”: Income pooling in American couples and families. (Publication Number bgsu1563551133786962) [Doctoral Dissertation. Bowling Green State University, The OhioLINK Advantage.

20.

Evans

G. W.

Kim

(2007). Childhood poverty and health: Cumulative risk exposure and stress dysregulation. Psychological Science, 18(11), 953–957. https://doi.org/10.1111/j.1467-9280.2007.02008.x

21.

Evans

G. W.

Kim

(2013). Childhood poverty, chronic stress, self‐regulation, and coping. Child Development Perspectives, 7(1), 43–48. https://doi.org/10.1111/cdep.12013

22.

Falconier

M. K.

Epstein

N. B.

(2010). Relationship satisfaction in Argentinean couples under economic strain: Gender differences in a dyadic stress model. Journal of Social and Personal Relationships, 27(6), 781–799. https://doi.org/10.1177/0265407510373260

23.

Falconier

M. K.

Epstein

N. B.

(2011). Couples experiencing financial strain: What we know and what we can do. Family Relations, 60(3), 303–317. https://doi.org/10.1111/j.1741-3729.2011.00650.x

24.

Gudmunson

C. G.

Beutler

I. F.

Israelsen

C. L.

McCoy

J. K.

Hill

E. J.

(2007). Linking financial strain to marital instability: Examining the roles of emotional distress and marital interaction. Journal of Family and Economic Issues, 28(3), 357–376. https://doi.org/10.1007/s10834-007-9074-7

25.

Hoffman

(2007). Multilevel models for examining individual differences in within-person variation and covariation over time. Multivariate Behavioral Research, 42(4), 609–629. https://doi.org/10.1080/00273170701710072

26.

Hofmann

Patel

P. V.

(2015). SurveySignal: A convenient solution for experience sampling research using participants’ own smartphones. Social Science Computer Review, 33(2), 235–253. https://doi.org/10.1177/0894439314525117

27.

Karney

B. R.

Bradbury

T. N.

(1995). The longitudinal course of marital quality and stability: A review of theory, methods, and research. Psychological Bulletin, 118(1), 3–34. https://doi.org/10.1037/0033-2909.118.1.3

28.

Kelley

(1979). Personal relationships: Their structure and processes. Wiley.

29.

Kelley

Thibaut

(1978). Interpersonal relations: A theory of interdependence. Wiley.

30.

Kenny

D. A.

Kashy

D. A.

Cook

W. L.

(2006). Dyadic data analysis. Guilford press.

31.

Knobloch

L. K.

(2008). The content of relational uncertainty within marriage. Journal of Social and Personal Relationships, 25(3), 467–495. https://doi.org/10.1177/0265407508090869

32.

Knobloch

L. K.

Carpenter-Theune

K. E.

(2004). Topic avoidance in developing romantic relationships: Associations with intimacy and relational uncertainty. Communication Research, 31(2), 173–205. https://doi.org/10.1177/0093650203261516

33.

Knobloch

L. K.

Knobloch-Fedders

L. M.

(2010). The role of relational uncertainty in depressive symptoms and relationship quality: An actor—partner interdependence model. Journal of Social and Personal Relationships, 27(1), 137–159. https://doi.org/10.1177/0265407509348809

34.

Knobloch

L. K.

Solomon

D. H.

(1999). Measuring the sources and content of relational uncertainty. Communication Studies, 50(4), 261–278. https://doi.org/10.1080/10510979909388499

35.

Knobloch

L. K.

Solomon

D. H.

(2002). Information seeking beyond initial interaction: Negotiating relational uncertainty within close relationships. Human Communication Research, 28(2), 243–257. https://doi.org/10.1111/j.1468-2958.2002.tb00806.x

36.

Knobloch

L. K.

Theiss

J. A.

(2011). Depressive symptoms and mechanisms of relational turbulence as predictors of relationship satisfaction among returning service members. Journal of Family Psychology, 25(4), 470–478. https://doi.org/10.1037/a0024063

37.

Lamidi

E. O.

Manning

W. D.

Brown

S. L.

(2019). Change in the stability of first premarital cohabitation among women in the United States, 1983–2013. Demography, 56(2), 427–450. https://doi.org/10.1007/s13524-019-00765-7

38.

Mahoney

M. M.

(1983). Economic sharing during marriage: Equal protection, spousal support and the doctrine of necessaries. Journal of Family Law, 22(2), 221–261.

39.

Masarik

A. S.

Martin

M. J.

Ferrer

Lorenz

F. O.

Conger

K. J.

Conger

R. D.

(2016). Couple resilience to economic pressure over time and across generations. Journal of Marriage and Family, 78(2), 326–345. https://doi.org/10.1111/jomf.12284

40.

Melton

R. L.

(1990). Legal rights of unmarried heterosexual and homosexual couples and evolving definitions of family. Journal of Family Law, 29(1), 497–517.

41.

Rhoades

G. K.

Stanley

S. M.

Markman

(2012). A longitudinal investigation of commitment dynamics in cohabiting relationships. Journal of Family Issues, 33(3), 369–390. https://doi.org/10.1177/0192513X11420940

42.

Santiago

C. D.

Etter

E. M.

Wadsworth

M. E.

Raviv

(2012). Predictors of responses to stress among families coping with poverty-related stress. Anxiety, Stress & Coping, 25(3), 239–258. https://doi.org/10.1080/10615806.2011.583347

43.

V. P.

Gesselman

A. N.

Perry

B. L.

Fisher

H. E.

Garcia

J. R.

(2017). Stress of singlehood: Marital status, domain-specific stress, and anxiety in a national US sample. Journal of Social and Clinical Psychology, 36(6), 461–485. https://doi.org/10.1521/jscp.2017.36.6.461

44.

Theiss

J. A.

Knobloch

L. K.

(2009). An actor-partner interdependence model of irritations in romantic relationships. Communication Research, 36(4), 510–537. https://doi.org/10.1177/0093650209333033

45.

Theiss

J. A.

Knobloch

L. K.

Checton

M. G.

Magsamen-Conrad

(2009). Relationship characteristics associated with the experience of hurt in romantic relationships: A test of the relational turbulence model. Human Communication Research, 35(4), 588–615. https://doi.org/10.1111/j.1468-2958.2009.01364.x

46.

Totenhagen

C. J.

Butler

E. A.

Curran

M. A.

Serido

(2016). The calm after the storm: Relationship length as associated with couples’ daily variability. Journal of Social and Personal Relationships, 33(6), 768–791. https://doi.org/10.1177/0265407515597562

47.

Totenhagen

C. J.

Wilmarth

M. J.

Serido

Betancourt

A. E.

(2018). Do day-to-day finances play a role in relationship satisfaction? A dyadic investigation. Journal of Family Psychology, 32(4), 528–537. https://doi.org/10.1037/fam0000406

48.

Tran

A. G. T. T.

Lam

C. K.

Legg

(2018). Financial stress, social supports, gender, and anxiety during college: A stress-buffering perspective. The Counseling Psychologist, 46(7), 846–869. https://doi.org/10.1177/0011000018806687

49.

Vinokur

A. D.

Price

R. H.

Caplan

R. D.

(1996). Hard times and hurtful partners: How financial strain affects depression and relationship satisfaction of unemployed persons and their spouses. Journal of Personality and Social Psychology, 71(1), 166–179. https://doi.org/10.1037/0022-3514.71.1.166

50.

Wadsworth

M. E.

(2012). Working with low-income families: Lessons learned from basic and applied research on coping with poverty-related stress. Journal of Contemporary Psychotherapy, 42(1), 17–25. https://doi.org/10.1007/s10879-011-9192-2

51.

Waggoner

L. W.

(2015). With marriage on the decline and cohabitation on the rise, what about marital rights for unmarried partners? ACTEC Law Journal, 41(1), 49–93.

52.

Weigel

D. J.

Brown

O’Riordan

C. K.

(2011). Everyday expressions of commitment and relational uncertainty as predictors of relationship quality and stability over time. Communication Reports, 24(1), 38–50. https://doi.org/10.1080/08934215.2010.511400

53.

Wheeler

Kerpelman

(2016). Change in disagreements about money, time, and sex and marital outcomes. Journal of Relationships Research, 7, Article e6. https://doi.org/10.1017/jrr.2016.8

54.

Whitton

S. W.

Rhoades

G. K.

Whisman

M. A.

(2014). Fluctuation in relationship quality over time and individual well-being: Main, mediated, and moderated effects. Personality and Social Psychology Bulletin, 40(7), 858–871. https://doi.org/10.1177/0146167214528988

55.

Whitton

S. W.

Whisman

M. A.

(2010). Relationship satisfaction instability and depression. Journal of Family Psychology, 24(6), 791–794. https://doi.org/10.1037/a0021734

56.

Williams

D. T.

Cheadle

J. E.

(2016). Economic hardship, parents’ depression, and relationship distress among couples with young children. Society and Mental Health, 6(2), 73–89. https://doi.org/10.1177/2156869315616258

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.14 MB