Previous research attributes vulnerability to financial hardship either to structural inequities or to poor financial behavior. Less attention has been paid to the role of social psychological factors or to the relative contribution of demographics, behavior, and social psychology in understanding an individual's vulnerability to financial hardship. While studies have examined psychosocial factors in financial outcomes, we argue that these factors represent a missing perspective in the construction of interventions to lessen vulnerability. We further argue that a holistic perspective considering all three factors is needed to address vulnerability to financial hardship. Capitalizing on the richness of the CFPB National Financial Well-Being Survey data (n = 6394), we examine the unique contribution of psychosocial factors in explaining an individual's financial vulnerability over and above demographics and behaviors. Using four different measures of financial hardship, we find that all three types of factors play important roles in understanding vulnerability to financial hardship. Our findings suggest that more holistic measures and interventions are needed to enhance consumer financial well-being.

1 INTRODUCTION

Vulnerability to financial hardship is part of the lived experience of many American households (Morduch & Schneider, 2017). Evidence of vulnerability was clear during the COVID-19 pandemic as over 30% of households had difficulty covering usual expenses even with government subsidies (Center on Budget and Policy Priorities, 2021). Yet vulnerability to financial hardship is not a new phenomenon that occurred solely during the pandemic. In years prior, most Americans (55%) could not replace even one month of their income through savings (Pew Research Center, 2015), more than half rated their financial situation as either “poor” or “fair” (Saad, 2015), and one in three Americans had been contacted by a creditor or debt collector in a single year (Consumer Financial Protection Bureau [CFPB], 2017). It is important to recognize that these conditions are not necessarily due to low income, as only one in five households experiencing financial hardship earn less than $40,000 per year (Malito, 2018).

Financial hardship is defined as “a state of distress in which an individual is unable to maintain a standard of living” (O'Connor et al., 2019, p. 422). Most people who fall into financial hardship indicate they never saw it coming (Morduch & Schneider, 2017; Ulrich, 2020). From their perspective, they wake up one day to find, for one reason or another, that they can no longer meet their needs or obligations with the resources available to them. Perhaps they were not saving sufficiently, their resources did not continue or grow as they expected, or what was required to maintain a standard of living changed without a commensurate change in resources. In many of these cases, they failed to realize what was happening, missing the warning signs that might motivate preventive action to protect their financial health. The experience of financial hardship has been associated with poor mental health, substance abuse, and difficulty avoiding future hardship experiences (Brown, 2002). Financial vulnerability refers to the risk or probability of financial hardship rather than the presence of such hardship (O'Connor et al., 2019). Once hardship occurs, there is no remaining vulnerability or risk as the experience of hardship is certain.

Many studies of financial vulnerability, largely grounded in economic and sociological theory, tend to focus on the structural reasons for vulnerability (Baker, 2009). This vulnerable people perspective identifies positions in the social structure that place individuals at a disadvantage due to the lack of available resources and/or unique challenges. Structural inequities, largely defined by demographics, make avoiding hardship difficult for some groups of people (Chiteji, 2010; Yearby, 2018). Vulnerability often becomes a classification challenge (Baker, 2009; Commuri & Ekici, 2008) wherein all members of a given class (e.g., sex, race or ethnicity, age, level of education) are identified as being vulnerable by virtue of their position in the social structure (Wisner, 2004). The vast majority of financial vulnerability research has examined the relationship between financial vulnerability and demographics such as age (Thorne et al., 2009), income (Anderloni et al., 2012), sex (Lusardi & Mitchell, 2008), race (Chiteji, 2010; Yearby, 2018), and geography (Wang & Tian, 2014). Such studies tend to focus on how those demographics put individuals at a disadvantage due to the lack of available resources and unique challenges. The primary focus of such studies is policy or program initiatives designed to reduce vulnerability and hardship (Osgood et al., 2005).

However, other studies, largely grounded in personal finance and consumer behavior, take a more agentic perspective and consider the ways in which financial behaviors, reflecting individual choices or tradeoffs, render an individual more (or less) vulnerable to financial hardship (e.g., Anderloni et al., 2012). Financial behaviors studied include money and credit management (Dew & Xiao, 2011; Hilgert et al., 2003), risky financial behavior (Maddux, 2002), savings (Gjertson, 2016; Lusardi, 2011) and retirement planning (Lusardi & Mitchell, 2007). These studies equate financial vulnerability with poor choices relative to resources (e.g., over-borrowing in relation to current or future income or a failure to take protective action such as having adequate insurance or savings [Anderloni et al., 2012]). The source of financial vulnerability is assumed to be the individual and their actions without consideration of the structural inequities that constrain or enable such actions. In addition, these studies tend to describe financial vulnerability as the presence of one or more financial outcomes rather than the risk of such outcomes (Baker et al., 2005).

Research suggests that neither demographics nor behaviors are likely sufficient explanations for financial vulnerability. Some studies suggest that the structural perspective relies too heavily on demographics that endow the social structure with too much permanence and power (Jones & Karsten, 2008) while failing to consider the role of purposive action (Baker, 2009; Stones, 2005). Conversely, the more agentic perspective of financial vulnerability cannot speak to the effects of “constraint, power, and large-scale social organization” on an individual's financial situation (Giddens, 1993, p. 4). Missing in the literature are studies that consider a more complete or holistic set of structural and agentic factors in determining vulnerability to financial hardship. Such a holistic perspective would allow us to identify “which conditions of action will maximize the capacity of knowledgeable actors to make a difference in this society when they are differentially socially endowed with access to resources” (Kilminster, 1991, p. 92).

We explore this more holistic perspective and argue that psychosocial factors represent an additional category to be considered. Less attention has been paid to the role of social psychological factors (i.e., thoughts, feelings, beliefs and other aspects of an individual's mindset) in vulnerability to financial hardship even though studies have demonstrated their association with related constructs such as consumer debt (Lea et al., 1995; Sweet et al., 2013), savings behavior (Copur & Gutter, 2019), and financial behavior (Bapat, 2020). Attitudes toward money, for example, have been shown to have an independent effect on financial outcomes controlling for socioeconomic characteristics and financial capabilities (von Stumm et al., 2013). In addition, many social psychological indicators that relate to forward-looking behavior and future-oriented goals (Gollwitzer & Sheeran, 2006) could be instrumental in determining consumer success in such goal attainment (Bandura, 1997) and reducing potential vulnerability to financial hardship. Such psychosocial factors, however, tend to be absent in studies of financial vulnerability.

Using data from the CFPB National Financial Well-Being Survey (n = 6394), we examine the contribution of social psychological factors in conjunction with demographic and behavioral factors to our ability to explain who experiences financial hardship. The challenge in improving consumer welfare and quality of life, the focus of transformative consumer research (TCR), is in identifying effective strategies driven by clear and complete understanding of antecedents. In line with the TCR objective to improve consumer well-being (Ozanne et al., 2015), this study offers a more holistic understanding of vulnerability to financial hardship and its antecedents by focusing on the individual's mindset or psychographic characteristics that frame the individual's view of their position and opportunities. As such, social psychological antecedents of financial hardship provide an important additional component in attempt to help a person reduce their vulnerability to financial hardship.

The remainder of this paper is structured as follows. First, we present the theoretical model to be examined. Next, we conduct analyses to understand the contribution of demographics, behaviors, and social psychological factors to one's vulnerability to financial hardship. Finally, we discuss the results in terms of a more holistic approach to addressing vulnerability to financial hardship.

2 THEORETICAL MODEL

2.1 Financial hardship

Financial hardship is the ultimate outcome in our theoretical model. The distinction between being financially vulnerable and experiencing financial hardship is critical in determining appropriate interventions for each individual and motivate them to act before a serious hardship develops. The Oxford English Dictionary defines vulnerability as “susceptibility to harm.” To examine financial vulnerability, then, we must first specify the outcome or harm to which an individual may be susceptible.

We argue that financial hardship is the harm. Financial hardship indicates that a person has experienced a significant negative event related to his or her ability to maintain a minimum standard of living (Whelan et al., 2001). Regardless of the specific nature of hardship experienced, the experience of financial hardship is psychologically damaging, often leaving the individual in a persistent state of worry and susceptibility to continued hardship in other forms (Mani et al., 2013). In many studies, the outcome or condition to which one is vulnerable tends to be rather narrowly defined. For example, the vulnerability-to-poverty literature examines the likelihood of a household falling below versus staying above the poverty line (Kamanou & Morduch, 2002; Morduch, 1994).

This study evaluates an individual's susceptibility to a range of financial hardships that may manifest in different experiences for different people (e.g., lack of food, housing or medical care, debt in collections). Specifically, we examine four types of financial hardship: material hardship, difficulty in making ends meet, having debt in collections, and being unable to absorb a financial shock (Beverly, 2001; Iceland & Bauman, 2004). These events represent financial hardship as their presence indicates that the individual has failed to maintain a certain standard of living. Material Hardship reflects an inability to meet basic needs. This occurs when an individual's needs or expenditures exceed their income (Mayer & Jencks, 1989), a condition shown to result in significant stress, mental health issues, and negative life events (Gershoff et al., 2007). Research has considered material hardship as an alternative or complement to traditional income-based poverty measures (Beverly, 2001, 2003). An individual does not need to be living in poverty to experience material hardship (Gershoff et al., 2007). Difficulty in Making Ends Meet refers to a financial hardship experience in which debt obligations exceed an individual's ability to pay. This experience might occur for individuals with lower incomes or for individuals who have higher income but also higher debt levels (Short, 2005). Debt in Collections indicates an extended inability to repay debts and is often a precursor to bankruptcy. Debt in collections can be a very traumatic experience, especially the first time it is experienced (CFPB, 2017). Debt collectors are adept at leveraging psychological tactics to collect debt that can have serious ramifications on an individual's mental health (Sweet et al., 2013). The final factor of financial hardship we consider, Ability to Handle an Unexpected Expense, refers to an individual's confidence in their ability to manage unexpected expenses and has been shown to be associated with positive psychological and relationship outcomes (Dew, 2007; Shanks, 2007).

2.2 Holistic view of vulnerability to financial hardship

There is a need to aggregate and balance knowledge from research involving both the structural and agentic perspectives of financial vulnerability (Baker, 2009; Stones, 2005). To this end, we hope to increase knowledge of vulnerability to financial hardship by including demographics (i.e., age, sex, race/ethnicity, and income), financial behaviors (i.e., paying bills on time, paying credit cards on time, checking statements for errors, and following a budget), and psychosocial factors (i.e., self-control, conscientiousness, goal confidence, and future orientation) in a hierarchical model. Of particular interest, this research considers whether psychosocial factors offer incremental explanation for the experience of financial hardship over demographics and behaviors. Figure 1 presents the framework.

Details are in the caption following the image — **FIGURE 1**
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Conceptual model.

2.2.1 Demographics

There has been a plethora of research examining the relationship between financial hardship and a wide range of demographic variables. Recent studies, for example, consider financial hardship in relation to age (Thorne et al., 2009); sex (Lusardi & Mitchell, 2008); income (Anderloni et al., 2012); and race (Plath & Stevenson, 2005).

Age—An individual's age has long been associated with their financial outcomes (Finke et al., 2017). Although young individuals often struggle with debt (e.g., student loans), older Americans are often most at risk for hardship (e.g., bankruptcy filings among Americans aged fifty-five or older are much higher [Thorne et al., 2009]). Older individuals face greater out-of-pocket medical expenses (Hwang et al., 2001) and food insecurity (Leung & Wolfson, 2022). Given these findings, we expect age to be negatively associated with the experience of financial hardship.

Sex – Females have been shown to be at greater risk for financial problems than males (Weir & Willis, 2000). Females have been found to have lower financial literacy (Bucher-Koenen et al., 2017) and to engage in more costly credit card behaviors (e.g., incurring late and over-the-limit fees [Allgood & Walstad, 2011]) than their male counterparts. Thus, we expect males to be less likely to be vulnerable to financial hardship.

Race/ethnicity—Notwithstanding policy and practice changes, studies show that historical and structural circumstances perpetuate racial disparities in finances, often in subtle ways (Chiteji, 2010; Yearby, 2018). Financial hardship has been shown to be more prevalent among Black households, with one study showing the rate of hardship to be three times higher for Black households compared to White households (Alice National Report, 2020). According to the Pew Research Center (2021), 62% of Latinos have experienced at least one form of hardship since the pandemic, with trouble paying bills being the most common problem. Based on the above, we expect Non-Hispanic Black and Hispanic individuals to be more vulnerable to financial hardship.

Income—Individuals' income (Gjertson, 2016) as well as their desire for income security (Chatterjee et al., 2019) have been associated with their financial state. Low income has been shown to increase the likelihood of economic hardship, such as food insecurity and disconnected services (Gjertson, 2016), while also being associated with myopic and shortsighted financial decisions (Jachimowicz et al., 2017). Based on extant research, we expect individuals with lower income to be more vulnerable to financial hardship.

2.2.2 Financial behaviors

Behavioral indicators are perhaps the most proximate predictors of the likelihood that someone will become vulnerable to financial hardship. We believe that the consideration of financial behaviors should cover several domains, including cash and credit management and one's attention to finances (Dew & Xiao, 2011; Hilgert et al., 2003).

Paying bills on time—Paying bills on time helps individuals avoid late fees, higher interest rates and other negative consequences (Dew & Xiao, 2011). Therefore, we expect that paying bills on time is likely to be associated with financial hardship and include this behavior in the model.

Paying credit cards in full—How an individual manages credit cards is not only another important indicator of positive financial behavior (Hilgert et al., 2003; Dew & Xiao, 2011), it is interconnected to the entire chain of financial behaviors. Paying credit cards in full helps individuals avoid interest and debt, while maintaining a good payment history allows the individual to maintain a good credit score. Credit card interest and fees are associated with bankruptcy filings (Pottow, 2012). By extension, we suggest that paying credit cards in full will be associated with lower vulnerability to financial hardship.

Checking statements for errors—While checking financial statements for errors helps address financial mistakes, this behavior provides an opportunity for the individual to pay attention to their finances (Chowdhry & Dholakia, 2020). In line with this premise, checking statements for errors is likely to be negatively associated with financial hardship.

Following a budget or spending plan—Having a budget or a spending plan refers to money management as well as saving and investing for the future (Hilgert et al., 2003). A budget or spending plan has been associated with positive financial behavior (Heath & Soll, 1996). Therefore, we expect that following a budget will be negatively associated with financial hardship.

2.2.3 Social-psychological factors

Many psychosocial factors influence the choices and tradeoffs people make in the present as well as their willingness to work toward their financial future.

Self-control—Self-control is defined as the ability to control one's behavior and maintain self-discipline while avoiding temptation (Baumeister, 2002; Kivetz & Simonson, 2002). A central tenet of this construct is the ability to refrain from acting upon undesirable impulses. Self-control is positively associated with an individual's perceived financial well-being (Ponchio et al., 2019) while lack of self-control has been associated with over-indebtedness (Gathergood, 2012). Thus, we expect that self-control will be negatively associated with the experience of financial hardship.

Conscientiousness—A conscientious individual strives to do what is right and to fulfill their duties. Research shows that conscientiousness correlates with an individuals' ability to be mindful of their financial behaviors to avoid financial hardships (Haws et al., 2012). This personality trait is a consistent predictor of asset accumulation (Letkiewicz & Fox, 2014) while being negatively associated with financial distress (Xu et al., 2015). As such, we expect conscientiousness to be negatively associated with the experience of financial hardship.

Goal confidence—Although extant research has shown that holding goal intention does not necessarily guarantee goal attainment (due primarily to self-regulatory problems in goal pursuits [Gollwitzer & Sheeran, 2006]), an individual's belief in their ability to achieve their financial goals (i.e., goal confidence) has been found to be instrumental to their success (Bandura, 2007). Hence, we expect that goal confidence will be associated with lower levels of vulnerability to financial hardship.

Future orientation—Studies have shown that individuals with longer planning horizons tend to engage in more positive financial behaviors such as saving, planning for retirement, and responsible credit use (Fisher & Montalto, 2010; Kim & DeVaney, 2001; Rutherford & DeVaney, 2009). These findings are consistent with other research indicating that those who are able to visualize their future self positively (Hershfield et al., 2011; Nenkov et al., 2008) and plan for an abundant future (Lynch Jr et al., 2009) will experience more favorable financial outcomes. Individuals with a saver mindset have a higher likelihood of accomplishing their financial goals, regardless of income levels (Newmeyer et al., 2021). People who are more future oriented tend to defer immediate pleasure, accept later monetary rewards, and allocate more resources to tomorrow's needs (Hershfield et al., 2011). Thus, we expect that future orientation assessed as planning horizon will be associated with lower levels of vulnerability to financial hardship.

3 METHODOLOGY

To examine our framework, we explored the ability of established demographic, behavioral, and psychosocial constructs to explain whether a given individual had experienced financial hardship.

3.1 Data

Data for this study came from the CFPB's National Financial Well-Being Survey (2017), a large scale, publicly available, government data source collected by the CFPB¹ through a national online survey of 6394 U.S. adults conducted between October and December 2016. The CFPB sample was drawn from the GfK Knowledge Panel, a recruited Internet panel that is nationally representative of U.S. households. Responses were weighted on several demographic and geographic characteristics to reflect the U.S. population. Missing data for the variables included in the study accounted for less than 2% of all cases, well below the recommended threshold of 5% or less of total cases for missing data (Tabachnick et al., 2007). The full sample is used in our analysis.

3.2 Measures

3.2.1 Financial hardship—the outcome measure

Financial hardship was indicated by the experience of material hardship, difficulty in making ends meet, having debt in collections, or being unable to absorb a financial shock. The initial model was estimated using material hardship. The other three types of financial hardship were used in robustness checks of our model.

Material hardship—Participants responded to six questions regarding their experience of material hardship related to food (worried whether food would run out before getting money to buy more, food did not last and did not have money to get more), housing (could not afford a place to live; utilities shut off due to non-payment), and medical services (any household member could not afford to see doctor or go to hospital; any household member stopped taking medication or took less due to costs) on a three-point scale of “often,” “sometimes,” and “never” during the past 12 months. Financial hardship was indicated as the experience of at least one material hardship that occurred “often” with 1 representing that the experience had occurred and 0 representing that it had not.
Difficulty in making ends meet—A single item asked how difficult it was for the respondent to cover their expenses and pay all their bills in a typical month with the response options of “very difficult,” “somewhat difficult,” and “not at all difficult.” Financial hardship was indicated by respondent finding it “very difficult” to make ends meet with 1 representing that the experience had occurred and 0 representing that it had not.
Debt in collection—A single item asked whether the respondents had been contacted by a debt collector in the past 12 months for a debt that they owe. Financial hardship was indicated if a respondent had been contacted by a debt collector with 1 representing that the experience had occurred and 0 representing that it had not.
Inability to absorb a financial shock—A single item asked the respondent how certain they were that they could come up with $2000 in 30 days to cover an unexpected expense. Response options ranged from being “certain they could” to “certain they could not” on a four-point scale. Financial hardship was indicated by whether the respondent said they were “certain they could not” cover a financial shock with 1 representing that the experience had occurred and 0 representing that it had not.

3.2.2 Indicators of vulnerability to financial hardship

Demographics

Age—Age in years was provided as a panel variable (i.e., a variable known by the panel company from their pre-screen) and used as a continuous variable in the analysis.
Sex—Sex was provided as a panel variable with the categories of male and female. A binary indicator was used to represent male (1 = Male; 0 = Female).
Race/ethnicity—Race/ethnicity was assessed through two questions. The first question asked whether the individual was Hispanic/Latino (1 = Yes; 0 = No). The second question asked for the individual's race (coded as White, Black, and Other by the CFPB). These two variables were used to construct binary indicators for Hispanic (Hispanic/Latino origin = Yes), Non-Hispanic White (Hispanic/Latino origin = No and Race = White), Non-Hispanic Black (Hispanic/Latino origin = No and Race = Black), and Non-Hispanic Other Race (Hispanic/Latino origin = No and Race = Other Race). Non-Hispanic White was used as the comparison category.
Income—Income was measured using 21 ranges with the lowest range being < $5000 and the highest range being $250,000 or more. For our analysis, we constructed a pseudo-continuous variable using the midpoints of these ranges with <$5000 being coded as $2500 and $250,000 or more being coded as $279,999.

Financial Behaviors—Three of the four measures for financial behavior were taken from the Money Management Scale (Dew & Xiao, 2011). Participants were asked to indicate how often they had engaged in each behavior in the past six months. Responses were assessed using a five-point scale with 1 representing “Never” and 5 representing “Always”.

Paying bills on time—Paid all your bills on time (part of the cash management subscale).
Paying credit cards in full each month—Paid off credit card balance in full each month (part of the credit management subscale).
Following a budget or spending plan—Stayed within your budget or spending plan (part of the cash management subscale).

One additional item included in the CFPB survey asked about a behavior related to recordkeeping.

Checking statements for errors—Checked your statements, bills and receipts to make sure there were no errors.

Social Psychological Factors

Self-control—Three items were used to assess self-control (I often act without thinking through all the alternatives, I am good at resisting temptation, and I am able to work diligently toward long-term goals). Each item used a four-point scale representing how well the statement described the participant where 1 = Not at all, 2 = Not very well, 3 = Very well, and 4 = Completely. The average of these three items was used in our analysis.
Conscientiousness—The CFPB data contains two items assessing conscientiousness as expected follow-through on commitments to others (i.e., I follow-through on my financial commitments to others) and to oneself (i.e., I follow-through on financial goals I set for myself). For our analysis, we selected the follow-through on commitments to others given the similarity between the personal conscientiousness item and the measure used for goal confidence. This item was asked on a five-point scale, anchored by Not at all (1) and Completely (5) assessing the degree to which the statement described the participant.
Goal confidence—A single item was used to assess the individual's confidence in their own ability to achieve financial goals. This item used a five-point scale anchored by Not at all confident (1) and Very confident (5).
Future orientation—Financial planning horizon was used to indicate whether the individual was future oriented. The response options were: The next few months (1), The next year (2), The next few years (3), The next 5 to 10 years (4), and Longer than 10 years (5). A binary indicator was constructed with 1 representing five or more years and 0 indicating less than 5 years.

3.3 Analytical strategy

The sample was weighted using the final weight supplied by the CFPB. Descriptive statistics were calculated for the sample and the variables of interest. Correlations between the variables of interest were calculated using Pearson correlations or Eta, depending on the nature of the variable. Given the binary nature of our outcome variables, we used logistic regression to estimate our models. A hierarchical approach was used to assess the incremental model fit as each block of predictors (demographic, behavioral, and social psychological) were added. In the first model, financial hardship was regressed on only the demographic constructs. The second model added the behavioral constructs. The third model added the social psychological constructs. We used the likelihood ratio test (Smith & McKenna, 2013) to examine the additional variance explained by each model by comparing the demographic only model (model 1) to the demographic and behavioral model (model 2), and the demographic, behavioral and social psychological model (model 3). All analyses were conducted in SPSS Version 28.

4 RESULTS

4.1 Sample characteristics

Table 1 contains the characteristics of the sample. The average age was 47.5 years and the average income was $82,236. The sample was roughly half-male and half female. In terms of race/ethnicity, Non-Hispanic Whites represented 64.3% of the sample while Hispanics represented 15.8% and Non-Hispanic Blacks represented 11.9%. Just under 40% of participants were financially responsible for at least one child, almost 31% had a Bachelor's degree or higher education, and 55% were married.

TABLE 1. Sample characteristics.

	Mean/incidence	Std. deviation/count
Age in years	47.49	17.81
Income	$82,236	$64,923
Sex
Male	48.4%	3095
Female	51.6%	3299
Race/ethnicity
White	64.3%	4144
Black	11.9%	758
Hispanic	15.8%	1008
Other race	8.0%	514
Presence of children
Children	39.8%	2547
No children	60.2%	3847
Education
Bachelor's degree or higher	30.7%	1960
Marital status
Married	55.3%	3533
Living with partner	6.7%	426
Divorced/separated	10.9%	694
Widowed	4.8%	307
Single, never married	22.4%	1434

4.2 Variables of interest

Table 2 contains the means and standard deviations for the variables of interest as well as the correlations between them. In terms of financial hardship, not being able to absorb a financial shock was the most frequently occurring hardship at 15.6%, followed by being contacted by a debt collector at 14.8% and material hardship at 11.0%. Overall, 70.9% of the sample did not experience any of these hardships, 16.3% experienced just one, 6.4% experienced two, 4.5% experienced three, and 1.9% experienced all four. The correlations did not indicate multicollinearity.

TABLE 2. Descriptive statistics and correlations between variables of interest.

				Correlations
		Mean	SD	1	2	3	4
1	Material hardship experience	11.0%		–
2	Contacted by debt collector	14.8%		0.274**	–
3	Very difficult to make ends meet	8.8%		0.401**	0.301**	–
4	Cannot absorb shock	15.6%		0.359**	0.328**	0.390**	–
5	Age in years	47.49	17.81	−0.157**	−0.087**	−0.058**	−0.111**
6	Male	48.4%		−0.013	−0.021	0.008	−0.028*
7	White	64.3%		−0.107**	−0.126**	−0.071**	−0.113**
8	Black	11.9%		0.080**	0.164**	0.048**	0.042**
9	Hispanic	15.8%		0.061**	0.031*	0.044**	0.104**
10	Other race	8.0%		0.01	−0.014	0.01	0.011
11	Income	$82,236	$64,923	−0.207**	−0.167**	−0.189**	−0.263**
12	Pay bills on time	4.45	0.97	−0.309**	−0.335**	−0.312**	−0.339**
13	Pay credit cards in full	3.37	1.60	−0.239**	−0.289**	−0.258**	−0.369**
14	Follow budget or spending plan	3.74	1.09	−0.205**	−0.236**	−0.226**	−0.264**
15	Check statements for errors	4.13	1.09	−0.137**	−0.125**	−0.107**	−0.144**
16	Self-control	2.58	0.55	−0.027*	−0.062**	−0.038**	−0.105**
17	Conscientious with obligations to others	4.16	0.88	−0.203**	−0.216**	−0.232**	−0.274**
18	Future oriented (planning horizon 5+ years)	38.7%		−0.122**	−0.125**	−0.098**	−0.146**
19	Goal confidence	3.17	0.75	−0.252**	−0.224**	−0.280**	−0.350**

		5	6	7	8	9	10
1	Material hardship experience
2	Contacted by debt collector
3	Very difficult to make ends meet
4	Cannot absorb shock
5	Age in years	–
6	Male	−0.041**	–
7	White	0.173**	0.006	–
8	Black	−0.049**	−0.049**	−0.493**	–
9	Hispanic	−0.137**	0.029*	−0.581**	−0.159**	–
10	Other race	−0.063**	0.008	−0.397**	−0.108**	−0.128**	–
11	Income	−0.088**	−0.012	0.162**	−0.108**	−0.167**	0.067**
12	Pay bills on time	0.244**	−0.030*	0.159**	−0.164**	−0.065**	0.001
13	Pay credit cards in full	0.181**	0.005	0.136**	−0.157**	−0.045**	0.008
14	Follow budget or spending plan	0.170**	−0.006	0.050**	−0.102**	0.02	0.007
15	Check statements for errors	0.260**	−0.049**	0.064**	−0.058**	−0.030*	−0.004
16	Self-control	0.001	0.026*	0.026*	−0.005	0.004	−0.044**
17	Conscientious with obligations to others	0.119**	−0.063**	0.190**	−0.106**	−0.118**	−0.049**
18	Future oriented (planning horizon 5+ years)	0.072**	0.025*	0.165**	−0.093**	−0.106**	−0.038**
19	Goal confidence	0.042**	0.014	0.086**	0.003	−0.082**	−0.046**

		11	12	13	14	15	16	17	19
1	Material hardship experience
2	Contacted by debt collector
3	Very difficult to make ends meet
4	Cannot absorb shock
5	Age in years
6	Male
7	White
8	Black
9	Hispanic
10	Other race
11	Income	–
12	Pay bills on time	0.174**	–
13	Pay credit cards in full	0.278**	0.450**	–
14	Follow budget or spending plan	0.095**	0.471**	0.430**	–
15	Check statements for errors	0.044**	0.451**	0.325**	0.433**	–
16	Self-control	0.035**	0.084**	0.142**	0.195**	0.097**	–
17	Conscientious with obligations to others	0.225**	0.460**	0.326**	0.382**	0.293**	0.148**	–
18	Future oriented (planning horizon 5+ years)	0.232**	0.136**	0.198**	0.095**	0.069**	0.080**	0.144**	–
19	Goal confidence	0.249**	0.367**	0.411**	0.453**	0.267**	0.227**	0.411**	0.166**

Note: *p < 0.05 (two-tailed); **p < 0.01 (two-tailed).

4.3 Model results

4.3.1 Material hardship model results

Table 3 contains the results of the hierarchical model for material hardship. Model 1, the demographics-only model, explained 18.0% of the variance in material hardship. Age, being Non-Hispanic Black or Non-Hispanic Other Race, and income were significant variables in the model. Model 2, the demographic and behavior model, explained 26.9% of the variance in material hardship, a significant increase over model 1 (χ² = 313.409, p < 0.001). All four behavioral variables were significantly associated with the experience of material hardship. Age, being Non-Hispanic Other Race, and income remained significant. Being Non-Hispanic Black was no longer significant, but sex (male) became significant once the behavioral variables were added to the model. Model 3, the demographic, behavior, and psychosocial model, explained 29.1% of the variance in material hardship, a significant increase over model 2 (χ² = 83.841, p < 0.001). Self-control, future orientation (as planning horizon), and goal confidence were significantly associated with material hardship. Conscientiousness was not significant in this model. All four behavioral variables, age, sex, and income remained significant. Two variables, checking statements for errors and self-control, were positively (rather than negatively, as expected) associated with the experience of material hardship. Further analysis suggested that this surprising finding was likely a suppression effect produced by the associations between these two variables and the other three money management variables (i.e., paying bills on time, paying credit cards in full, and following a budget) rather than an indication that checking statements for errors and/or self-control were associated with a higher probability of material hardship.

TABLE 3. Material hardship hierarchical logistic regression.

Independent variables	B	Model 1			B	Model 2			B	Model 3
Independent variables	B	SE	Exp(B)	p	B	SE	Exp(B)	p	B	SE	Exp(B)	p
Demographics
Age in years	−0.034	0.003	0.966	***	−0.026	0.003	0.974	***	−0.028	0.003	0.973	***
Male	−0.147	0.086	0.863		−0.207	0.090	0.813	*	−0.181	0.091	0.834	*
Non-Hispanic Black	0.323	0.120	1.381	**	0.058	0.126	1.059		0.176	0.129	1.192
Hispanic	0.059	0.111	1.060		0.158	0.116	1.171		0.082	0.119	1.085
Non-Hispanic other	0.331	0.155	1.392	*	0.323	0.163	1.381	*	0.254	0.166	1.289
Income	−1.132	0.071	0.322	***	−0.981	0.073	0.375	***	−0.893	0.074	0.409	***
Constant	−0.960	0.136	0.383	***
Behaviors
Pay bills on time					−0.406	0.044	0.666	***	−0.356	0.046	0.700	***
Pay credit cards in full					−0.208	0.033	0.812	***	−0.169	0.034	0.844	***
Follow budget					−0.206	0.047	0.814	***	−0.113	0.051	0.893	*
Check statements					0.115	0.044	1.122	***	0.120	0.045	1.127	**
Constant					1.294	0.207	3.646	***
Social-psychological factors
Self-control									0.330	0.092	1.392	***
Conscientiousness: others									−0.020	0.055	0.980
5+ year planning horizon									−0.346	0.107	0.708	***
Goal confidence									−0.528	0.069	0.590	***
Constant									1.604	0.327	4.971	***
Model statistics
Nagelkerke R²	0.180				0.269				0.291
−2 log likelihood	3762.754				3449.345				3365.504
χ² of model	593.670				907.078				990.919
df	6				10				14
p value	<0.001				<0.001				<0.001
χ² of model step					313.409				83.841
df					4				4
p value					<0.001				<0.001

Note: *p < 0.05; **p < 0.01; ***p < 0.001.

4.3.2 Debt in collections model results

Table 4 contains the results of the hierarchical model for having debt in collections. Model 1, the demographics-only model, explained 9.7% of the variance in having debt in collections. Age, being Non-Hispanic Black, and income were significant variables in the model. Model 2, the demographic and behavior model, explained 23.8% of the variance in having debt in collections, a significant increase over model 1 (χ² = 556.925, p < 0.001). All four behavioral variables were significantly associated with having debt in collections. Checking statements for errors was positively associated with debt in collections. Being Non-Hispanic Black and income remained significant. Age was no longer significant but being Hispanic became significant once the behavioral variables were added to the model. Model 3, the demographic, behavior, and psychosocial model, explained 24.8% of the variance in having debt in collections, a significant increase over model 2 (χ² = 38.299, p < 0.001). Future orientation (as planning horizon) and goal confidence were significantly associated with having debt in collections. Self-control and conscientiousness were not significant in this model. In addition, all four behavioral variables, being Non-Hispanic Black, and income remained significant in this model.

TABLE 4. Contacted by debt collector hierarchical logistic regression.

Independent variables	B	Model 1			B	Model 2			B	Model 3
Independent variables	B	SE	Exp(B)	p	B	SE	Exp(B)	p	B	SE	Exp(B)	p
Demographics
Age in years	−0.014	0.002	0.986	***	−0.002	0.002	0.998		−0.003	0.002	0.997
Male	−0.114	0.074	0.892		−0.147	0.079	0.863		−0.139	0.080	0.870
Non-Hispanic Black	1.010	0.097	2.746	***	0.739	0.104	2.095	***	0.760	0.106	2.137	***
Hispanic	0.140	0.102	1.151		0.222	0.108	1.248	*	0.150	0.109	1.161
Non-Hispanic Other	0.118	0.143	1.125		0.052	0.153	1.053		−0.024	0.155	0.977
Income	−0.532	0.047	0.587	***	−0.311	0.049	0.732	***	−0.247	0.050	0.781	***
Constant	−1.328	0.119	0.265	***
Behaviors
Pay bills on time					−0.463	0.041	0.630	***	−0.423	0.042	0.655	***
Pay credit cards in full					−0.310	0.029	0.733	***	−0.280	0.030	0.755	***
Follow budget					−0.231	0.041	0.794	***	−0.176	0.045	0.839	***
Check statements					0.185	0.040	1.204	***	0.190	0.041	1.210	**
Constant					1.119	0.185	3.063	***
Social-psychological factors
Self-control									0.096	0.081	1.100
Conscientiousness: others									−0.085	0.050	0.918
5+ year planning horizon									−0.366	0.091	0.693	***
Goal confidence									−0.223	0.061	0.800	***
Constant									1.581	0.290	4.859	***
Model statistics
Nagelkerke R²	0.097				0.238				0.248
−2 log likelihood	4894.083				4331.942				4298.858
𝜒² of model	354.606				911.532				949.831
df	6				10				14
p value	<0.001				<0.001				<0.001
𝜒² of model step					556.925				38.299
df					4				4
p value					<0.001				<0.001

Note: *p < 0.05; **p < 0.01; ***p < 0.001.

4.3.3 Difficulty making ends meet model results

Table 5 contains the results of the hierarchical model for having difficulty making ends meet. Model 1, the demographics-only model, explained 12.8% of the variance in having difficulty making ends meet. Age and income were significant variables in the model. Model 2, the demographic and behavior model, explained 28.0% of the variance in having difficulty making ends meet, a significant increase over model 1 (χ² = 472.912, p < 0.001). All four behavioral variables were significantly associated with having difficulty making ends meet. Income remained significant. Age was no longer significant once the behavioral variables were added to the model. Model 3, the demographic, behavior, and psychosocial model, explained 30.8% of the variance in having difficulty making ends meet, a significant increase over model 2 (χ² = 91.368, p < 0.001). All four social psychological variables (i.e., self-control, conscientiousness, future orientation, and goal confidence) were significantly associated with having difficulty making ends meet. All four behavioral variables and income remained significant in this model. Self-control and checking statements for errors were positively associated with difficulty making ends meet.

TABLE 5. Very difficult to make ends meet hierarchical logistic regression.

Independent variables	B	Model 1			B	Model 2			B	Model 3
Independent variables	B	SE	Exp(B)	p	B	SE	Exp(B)	p	B	SE	Exp(B)	p
Demographics
Age in years	−0.015	0.003	0.985	***	−0.002	0.003	0.998		−0.004	0.003	0.996
Male	0.025	0.092	1.026		−0.013	0.099	0.987		−0.011	0.101	0.989
Non-Hispanic Black	0.150	0.133	1.162		−0.254	0.143	0.776		−0.129	0.146	0.879
Hispanic	0.002	0.122	1.002		0.152	0.131	1.164		0.025	0.134	1.025
Non-Hispanic other	0.265	0.167	1.304		0.243	0.180	1.275		0.169	0.185	1.184
Income	−1.179	0.080	0.308	***	−0.958	0.084	0.384	***	−0.845	0.085	0.430	***
Constant	−2.143	0.152	0.117	***
Behaviors
Pay bills on time					−0.497	0.048	0.608	***	−0.429	0.051	0.651	***
Pay credit cards in full					−0.336	0.038	0.714	***	−0.288	0.040	0.750	***
Follow budget					−0.329	0.051	0.720	***	−0.190	0.056	0.827	*
Check statements					0.256	0.049	1.292	***	0.282	0.051	1.325	**
Constant					0.434	0.221	1.544	***
Social-psychological factors
Self-control									0.354	0.102	1.425	***
Conscientiousness: others									−0.164	0.059	0.849	**
5+ year planning horizon									−0.238	0.118	0.788	*
Goal confidence									−0.581	0.074	0.559	***
Constant									1.082	0.358	2.951	***
Model statistics
Nagelkerke R²	0.128				0.280				0.308
−2 log likelihood	3365.841				2892.929				2801.561
𝜒² of model	371.423				844.335				935.703
df	6				10				14
p value	<0.001				<0.001				<0.001
𝜒² of model step					472.912				91.368
df					4				4
p value					<0.001				<0.001

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

4.3.4 Inability to absorb financial shock model results

Table 6 contains the results of the hierarchical model for inability to absorb financial shock. Model 1, the demographics-only model, explained 18.1% of the variance in inability to absorb shock. Age, sex, being Hispanic or Non-Hispanic Other Race, and income were significant variables in the model. Model 2, the demographic and behavior model, explained 36.1% of the variance in inability to absorb financial shock, a significant increase over model 1 (χ² = 777.389, p < 0.001). All four behavioral variables were significantly associated with inability to absorb financial shock. Checking statements for errors was positively associated with inability to absorb a financial shock. Age, sex, being Hispanic, and income remained significant. Being Non-Hispanic Other Race was no longer significant once the behavioral variables were added to the model. Model 3, the demographic, behavior, and psychosocial model, explained 39.2% of the variance in inability absorb shock, a significant increase over model 2 (χ² = 145.529, p < 0.001). Conscientiousness, future orientation (as planning horizon) and goal confidence were significantly associated with inability to absorb financial shock. Self-control was not significant in this model. All four behavioral variables, age, sex, being Non-Hispanic Black or Hispanic, and income remained significant in this model.

TABLE 6. Unable to absorb shock hierarchical logistic regression.

Independent variables	B	Model 1			B	Model 2			B	Model 3
Independent variables	B	SE	Exp(B)	p	B	SE	Exp(B)	p	B	SE	Exp(B)	p
Demographics
Age in years	−0.022	0.002	0.978	***	−0.010	0.003	0.990	***	−0.014	0.003	0.986	***
Male	−0.226	0.074	0.798	**	−0.287	0.082	0.751	***	−0.282	0.084	0.755	***
Non-Hispanic Black	0.042	0.112	1.043		−0.378	0.122	0.685	**	−0.258	0.125	0.772	*
Hispanic	0.273	0.095	1.313	**	0.493	0.104	1.637	***	0.391	0.107	1.478	***
Non-Hispanic other	0.324	0.136	1.382	*	0.278	0.150	1.321		0.176	0.154	1.192
Income	−1.172	0.061	0.310	***	−0.993	0.066	0.371	***	−0.869	0.067	0.419	***
Constant	−1.033	0.119	0.356	***
Behaviors
Pay bills on time					−0.368	0.041	0.692	***	−0.292	0.044	0.747	***
Pay credit cards in full					−0.495	0.030	0.609	***	−0.439	0.031	0.644	***
Follow budget					−0.305	0.043	0.737	***	−0.139	0.047	0.870	**
Check statements					0.191	0.041	1.210	***	0.207	0.042	1.230	***
Constant					1.740	0.196	5.695	***
Social-psychological factors
Self-control									0.005	0.083	1.005
Conscientiousness: others									−0.145	0.051	0.865	**
5+ year planning horizon									−0.326	0.097	0.722	***
Goal confidence									−0.633	0.063	0.531	***
Constant									3.346	0.310	28.388	***
Model Statistics
Nagelkerke R²	0.181				0.361				0.392
−2 log likelihood	4753.147				3975.759				3830.230
𝜒² of model	698.418				1475.806				1621.335
df	6				10				14
p value	<0.001				<0.001				<0.001
𝜒² of model step					777.389				145.529
df					4				4
p value					<0.001				<0.001

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

5 DISCUSSION

By examining the role of demographics, financial behaviors, and social psychological factors in financial difficulties taken from the economics, sociological, psychological, consumer behavior, and marketing literatures, the current study provides an extensive view on how these variables jointly contribute to vulnerability to financial hardship. The CFPB's National Financial Well-Being Survey offered a unique opportunity to consider this broad set of factors in a single study. While other large national data sources tend to focus on one or two categories of variables, the CFPB data covers all three. With this data source, we were able to demonstrate the impact of social psychological characteristics in addition to demographics and financial behaviors. An individual's dispositional characteristics and attitudes influence how they assess the financial decision that leads to their behavior. Some characteristics and attitudes promote better decisions and outcomes while others do not. Compared to what is currently available in the financial vulnerability and financial decision-making literatures, this research offers a more comprehensive, actionable, and accurate perspective on an individual's vulnerability to financial hardship and the interventions that might lessen that vulnerability.

One surprising finding from the study was the positive associations between the financial hardships and behavior of checking statements for errors and self-control. While the zero-order correlations were negative as expected, the odds ratios [Exp(B)] in the logistic regression analyses were positive. This finding was true for all four types of hardship and the behavior of checking statements for errors but only significant for two of the four types of hardships and self-control (i.e., material hardship and difficulty making ends meet). We suspected that these findings were due to suppression effects. In post-hoc analyses, we discovered that these effects appear to be driven by the positive relationships between these two hardship variables and the three other behavioral predictors being considered. There also appeared to be a suppression effect due to the relationship between self-control and income. Leaving out checking statements for error and self-control in our post-hoc analysis produced similar results (Model 2—Material Hardship with Demographics and Behaviors: 𝜒²₍₃₎ = 289.982, p < 0.001; Model 3—Material Hardship with Demographics, Behaviors, and Social-Psychological Factors: 𝜒²₍₃₎ = 69.580, p < 0.001). While these associations are curious, they are not uncommon (MacKinnon et al., 2000). These results should not be interpreted conceptually (Maassen & Bakker, 2001).

All three categories of variables (i.e., demographic, behavioral, and social psychological) provided significant explanatory power when added to the model. This finding was robust to the financial hardship considered. No single category of variables performed as well as the set. This finding suggests that while demographics matter in understanding financial vulnerability, they are insufficient on their own to explain the factors that lead an individual to be vulnerable. Demographics and positive financial behavior are associated with lower levels of vulnerability as is mindset (i.e., the collection of psychosocial factors considered). This highlights the need to take a more holistic view of vulnerability in research and program development. Historical and structural factors matter and should be addressed. However, behavioral and social psychological factors matter just as much and should also be addressed.

5.1 Implications

Financial hardship takes a negative toll on the physical, economic, and psychological health of the individuals experiencing it as well as the community and society in which they are situated (Case & Deaton, 2020). Reducing vulnerability to such hardships can mitigate these effects. Our findings suggest addressing financial hardship requires a multi-pronged approach including consideration of policy changes to address structural factors driving higher vulnerability to financial hardship, encouragement of positive financial behaviors that improve objective financial situations, and development of supportive attitudes to motivate such behaviors. This combination of considerations is consistent with the view that identifying those who are financially vulnerable to hardships should begin “by examining the nature of the actual situation and the reality of everyday lives” (Baker, 2009, p. 117). The vulnerability to financial hardship framework we present can be used to prioritize the most pressing needs for a given program audience, thereby allowing for programs to be designed around those needs. Ultimately, this new conceptualization of vulnerability to financial hardship allows us to consider who the individual is (i.e., demographic profile), as well as what they do (financial behaviors), and the motivations for their actions (socialpsychological factors).

Ideally, the vulnerability to financial hardship framework presented in this research will be available for an individual to use in tracking and understanding their financial vulnerability. For individuals who discover they have high vulnerability to financial hardship, next steps and resources would be identified and shared. These resources might include effective financial education programs that address the demographic, behavioral, and/or social-psychological indicators associated with a given individual's susceptibility to hardship. Such programs might provide targeted ways for individuals to identify and modify the indicators affecting their vulnerability to financial hardship.

Our holistic framework of vulnerability to financial hardship can also be used at a community or other group level to identify how well the group is doing overall. For example, governments might consider these comprehensive measures to identify areas where interventions might include consideration of circumstances, opportunity, behaviors or habits, and mindsets to prevent hardship (Hamad & Rehkopf, 2016). An understanding of the key contributors to a particular group's vulnerability to financial hardship would allow such programs to be designed around the most pressing need for a given program audience. Where structural barriers exist for particular demographic groups, the focus should be on removing those barriers and providing a more level playing field (Chiteji, 2010). When financial behavior patterns are leading individuals to be more vulnerable, education or training on more favorable patterns or habits might be the focus. Perhaps there is an opportunity for financial fitness classes in such instances. Where mindset represents a challenge to reducing vulnerability, programs might focus on the attitudes people hold toward money or finances that are influencing the actions they take (or fail to take). Our research suggests that programs and policies should consider ways in which to integrate several or all these factors.

An interesting question is how attitudes toward money relate to the individual's assessment of their financial well-being (CFPB, 2015; Brüggen et al., 2017). Financial well-being is an important driver of overall feelings of wellness (Netemeyer et al., 2018). Individuals with higher financial well-being tend to have lower levels of stress associated with money management and meeting future financial obligations (Netemeyer et al., 2018). This decrease in current stress may make it easier to plan for future needs (Mullainathan & Shafir, 2013; Trope & Liberman, 2010), creating a positive feedback cycle.

5.2 Limitations and future research

One limitation of our study is the use of cross-sectional data. Further research could examine causality using a longitudinal and/or experimental design. Further, while we have broadened our view of financial hardship to represent four types of hardship, there are possibly other forms of financial hardship that individuals experience. Future work should examine opportunities to expand and/or refine the hardships that are included in the outcome. Though we examine financial behaviors representing important domains identified in prior research, considerable debate remains as to which financial behaviors are most important. Further work could examine whether the holistic view of financial vulnerability can be improved with the addition of other financial behaviors. All data used in the analysis was taken from a survey and, therefore, self-reported. Future work could examine the model using a combination of self-reported and administrative data. Finally, this study does not examine the synergies between demographics, behaviors, and mindset in an individual's vulnerability to financial hardship. Future research might examine the ways in which two or more of these categories of variables operate together.

6 CONCLUSION

Socially endowed resources, individual choices/tradeoffs, and mindset all play significant roles in whether an individual experiences financial hardship. Efforts to mitigate financial vulnerability could benefit from a more holistic perspective that includes social psychological factors. Applying this perspective to research and participatory action will be essential in accelerating transformative consumer research and its practical application (Ozanne & Saatcioglu, 2008).

CONFLICT OF INTEREST

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

Endnote

¹ The data can be accessed through the CFPB website at https://www.consumerfinance.gov/data-research/financial-well-being-survey-data/.

REFERENCES

Alice National Report. (2020) On uneven ground Alice and financial hardship in the U.S. united for Alice. Available from www.unitedforalice.org [Accessed 18th June 2022].
Google Scholar
Allgood, S., and Walstad, W. (2011) The effects of perceived and actual financial knowledge on credit card behavior. Working paper 2011-WP-15, networks Financial Institute at Indiana State University, Terre Haute, Indiana.
Google Scholar
Anderloni, L., Bacchiocchi, E. & Vandone, D. (2012) Household financial vulnerability: an empirical analysis. Research in Economics, 66(3), 284–296.
10.1016/j.rie.2012.03.001
Google Scholar
Baker, S.M. (2009) Vulnerability and resilience in natural disasters: a marketing and public policy perspective. Journal of Public Policy & Marketing, 28(1), 114–123.
10.1509/jppm.28.1.114
Web of Science® Google Scholar
Baker, S.M., Gentry, J.W. & Rittenburg, T.L. (2005) Building understanding of the domain of consumer vulnerability. Journal of Macromarketing, 25(2), 128–139.
10.1177/0276146705280622
Google Scholar
Baumeister, R.F. (2002) Yielding to temptation: self-control failure, impulsive purchasing, and consumer behavior. Journal of Consumer Research, 28(4), 670–676.
10.1086/338209
Web of Science® Google Scholar
Bandura, A. (1997). Behavior theory and the models of man (1974). In: Meeting of the American Psychological Association, August, 1974, New Orleans, LA, USA. Presidential Address presented at the aforementioned meeting. American Psychological Association.
Google Scholar
Bandura, A. (2007) Much ado over a faulty conception of perceived self-efficacy grounded in faulty experimentation. Journal of Social and Clinical Psychology, 26(6), 641–658.
10.1521/jscp.2007.26.6.641
Web of Science® Google Scholar
Bapat, D. (2020) Antecedents to responsible financial management behavior among young adults. International Journal of Bank Marketing, 38(5), 1177–1194.
10.1108/IJBM-10-2019-0356
Web of Science® Google Scholar
Beverly, S.G. (2001) Material hardship in the United States: evidence from the survey of income and program participation. Social Work Research, 25(3), 143–151.
10.1093/swr/25.3.143
Web of Science® Google Scholar
Beverly, S.G. (2003) Measures of material hardship: rationale and recommendations. Journal of Poverty, 5(1), 23–41.
10.1300/J134v05n01_02
Google Scholar
Brown, G.W. (2002) Social roles, context and evolution in the origins of depression. Journal of Health and Social Behavior, 43, 255–276.
10.2307/3090203
PubMed Web of Science® Google Scholar
Brüggen, E.C., Hogreve, J., Holmlund, M., Kabadayi, S. & Löfgren, M. (2017) Financial well-being: a conceptualization and research agenda. Journal of Business Research, 79, 228–237.
10.1016/j.jbusres.2017.03.013
Web of Science® Google Scholar
Bucher-Koenen, T., Lusardi, A., Alessie, R. & Van Rooij, M. (2017) How financially literate are women? An overview and new insights. Journal of Consumer Affairs, 51(2), 255–283.
10.1111/joca.12121
Web of Science® Google Scholar
Case, A. & Deaton, A. (2020) Deaths of despair and the future of capitalism. In: Deaths of despair and the future of capitalism. Princeton, New Jersey: Princeton University Press.
10.1515/9780691217062-006
Google Scholar
Center for Budget and Policy Priorities (CBPP). (2021) Hardship Fell after Relief Enacted but Persisted in 2021. Available from: https://www.cbpp.org/hardship-fell-after-relief-enacted-but-persisted-in-2021 [Accessed 18th April 2022].
Google Scholar
Chatterjee, D., Kumar, M. & Dayma, K.K. (2019) Income security, social comparisons and materialism: determinants of subjective financial well-being among Indian adults. International Journal of Bank Marketing, 37(4), 1059–1079.
10.1108/IJBM-04-2018-0096
Web of Science® Google Scholar
Chiteji, N. (2010) Time preference, noncognitive skills and well-being across the life course: do noncognitive skills encourage healthy behavior? American Economic Review, 100(2), 200–204.
10.1257/aer.100.2.200
PubMed Web of Science® Google Scholar
Chowdhry, N. & Dholakia, U.M. (2020) Know thyself financially: how financial self-awareness can benefit consumers and financial advisors. Financial Planning Review, 3(1), e1069.
10.1002/cfp2.1069
Google Scholar
Commuri, S. & Ekici, A. (2008) An enlargement of the notion of consumer vulnerability. Journal of Macromarketing, 28(2), 183–186.
10.1177/0276146708316049
Web of Science® Google Scholar
Consumer Financial Protection Bureau (CFPB). (2015) Financial well-being: the goal of financial education. Available from: http://www.consumerfinance.gov [Accessed 18th April 2022].
Google Scholar
Consumer Financial Protection Bureau (CFPB). 2017. CFPB survey finds over one-in-four consumers contacted by debt collectors feel threatened. Available from: http://www.consumerfinance.gov [Accessed 18th April 2022].
Google Scholar
Copur, Z. & Gutter, M.S. (2019) Economic, sociological, and psychological factors of the saving behavior: Turkey case. Journal of Family and Economic Issues, 40(2), 305–322.
10.1007/s10834-018-09606-y
Web of Science® Google Scholar
Dew, J. (2007) Two sides of the same coin? The differing roles of assets and consumer debt in marriage. Journal of Family and Economic Issues, 28(1), 89–104.
10.1007/s10834-006-9051-6
Google Scholar
Dew, J. & Xiao, J.J. (2011) The financial management behavior scale: development and validation. Journal of Financial Counseling and Planning, 22(1), 43–59.
Google Scholar
Finke, M.S., Howe, J.S. & Huston, S.J. (2017) Old age and the decline in financial literacy. Management Science, 63(1), 213–230.
10.1287/mnsc.2015.2293
Web of Science® Google Scholar
Fisher, P.J. & Montalto, C.P. (2010) Effect of saving motives and horizon on saving behaviors. Journal of Economic Psychology, 31(1), 92–105.
10.1016/j.joep.2009.11.002
Web of Science® Google Scholar
Gathergood, J. (2012) Self-control, financial literacy and consumer over-indebtedness. Journal of Economic Psychology, 33(3), 590–602.
10.1016/j.joep.2011.11.006
Web of Science® Google Scholar
Gershoff, E.T., Aber, L., Raver, C.C. & Lennon, M.C. (2007) Income is not enough: incorporating material hardship into models of income associations with parenting and child development. Child Development, 78(1), 70–95.
10.1111/j.1467-8624.2007.00986.x
PubMed Web of Science® Google Scholar
Giddens, A. (1993) New rules of sociological method: a positive critique of interpretative sociologies. Redwood City, California: Stanford University Press.
Google Scholar
Gjertson, L. (2016) Emergency saving and household hardship. Journal of Family and Economic Issues, 37(1), 1–17.
10.1007/s10834-014-9434-z
Web of Science® Google Scholar
Gollwitzer, P.M. & Sheeran, P. (2006) Implementation intentions and goal achievement: a meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69–119.
10.1016/S0065-2601(06)38002-1
Web of Science® Google Scholar
Hamad, R. & Rehkopf, D.H. (2016) Poverty and child development: a longitudinal study of the impact of the earned income tax credit. American Journal of Epidemiology, 183(9), 775–784.
10.1093/aje/kwv317
PubMed Web of Science® Google Scholar
Haws, K.L., Bearden, W.O. & Nenkov, G.Y. (2012) Consumer spending self-control effectiveness and outcome elaboration prompts. Journal of the Academy of Marketing Science, 40(5), 695–710.
10.1007/s11747-011-0249-2
Web of Science® Google Scholar
Heath, C. & Soll, J.B. (1996) Mental budgeting and consumer decisions. Journal of Consumer Research, 23, 40–52.
10.1086/209465
Web of Science® Google Scholar
Hershfield, H.E., Goldstein, D.G., Sharpe, W.F., Fox, J., Yeykelis, L., Carstensen, L.L. et al. (2011) Increasing saving behavior through age-progressed renderings of the future self. Journal of Marketing Research, 48, S23–S37.
10.1509/jmkr.48.SPL.S23
PubMed Web of Science® Google Scholar
Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003) Household financial management: the connection between knowledge and behavior. Federal Reserve Bulletin, 89, 309–322.
Google Scholar
Hwang, W., Weller, W., Ireys, H. & Anderson, G. (2001) Out-of-pocket medical spending for care of chronic conditions. Health Affairs, 20(6), 267–278.
10.1377/hlthaff.20.6.267
CAS PubMed Web of Science® Google Scholar
Iceland, J., and Bauman, K. (2004) Income poverty and capability deprivation how strong is the association. In: Paper presented at 4th International Conference on the Capability Approach (5–7), September, Pavia.
Google Scholar
Jachimowicz, J.M., Chafik, S., Munrat, S., Prabhu, J.C. & Weber, E.U. (2017) Community trust reduces myopic decisions of low-income individuals. Proceedings of the National Academy of Sciences, 114(21), 5401–5406.
10.1073/pnas.1617395114
CAS PubMed Web of Science® Google Scholar
Jones, M.R. & Karsten, H. (2008) Giddens's structuration theory and information systems research. MIS Quarterly, 32(1), 127–157.
10.2307/25148831
Web of Science® Google Scholar
Kamanou, G., and Morduch, J. (2002) Measuring vulnerability to poverty (no. 2002/58). WIDER Discussion papers//world Institute for Development Economics (UNU-WIDER).
Google Scholar
Kilminster, R. (1991) Evaluating Elias. Theory, Culture and Society, 8(2), 165–176.
10.1177/026327691008002010
Web of Science® Google Scholar
Kim, H. & DeVaney, S.A. (2001) The determinants of outstanding balances among credit card revolvers. Financial Counseling and Planning, 12(1), 67–77.
CAS PubMed Web of Science® Google Scholar
Kivetz, R. & Simonson, I. (2002) Self-control for the righteous: toward a theory of precommitment to indulgence. Journal of Consumer Research, 29(2), 199–217.
10.1086/341571
Web of Science® Google Scholar
Lea, S.E.G., Webley, P. & Walker, C.M. (1995) Psychological factors in consumer debt: money management, economic socialization, and credit use. Journal of Economic Psychology, 16(4), 681–701.
10.1016/0167-4870(95)00013-4
Web of Science® Google Scholar
Letkiewicz, J.C. & Fox, J.J. (2014) Conscientiousness, financial literacy, and asset accumulation of young adults. Journal of Consumer Affairs, 48(2), 274–300.
10.1111/joca.12040
Web of Science® Google Scholar
Leung, C.W. & Wolfson, J.A. (2022) Food insecurity among older adults: 10-year national trends and associations with diet quality. Journal of the American Geriatrics Society, 69(4), 964–971.
10.1111/jgs.16971
Web of Science® Google Scholar
Lusardi, A. (2011) Americans' financial capability. No. w17103. National Bureau of Economic Research.
10.3386/w17103
Google Scholar
Lusardi, A. & Mitchell, O.S. (2007) Baby boomer retirement security: the roles of planning, financial literacy, and housing wealth. Journal of Monetary Economics, 54(1), 205–224.
10.1016/j.jmoneco.2006.12.001
Web of Science® Google Scholar
Lusardi, A. & Mitchell, O.S. (2008) Planning and financial literacy: how do women fare? American Economic Review, 98, 413–417.
10.1257/aer.98.2.413
Web of Science® Google Scholar
Lynch, J.G., Jr., Netemeyer, R.G., Spiller, S.A. & Zammit, A. (2009) A generalizable scale of propensity to plan: the long and short of planning for time and money. Journal of Consumer Research, 37(1), 108–128.
10.1086/649907
Web of Science® Google Scholar
Maassen, G.H. & Bakker, A.B. (2001) Suppressor variables in path models: definitions and interpretations. Sociological Methods & Research, 30(2), 241–270.
10.1177/0049124101030002004
Web of Science® Google Scholar
Maddux, E.M. (2002) A behavioral model to optimize financial quality of life. Social Indicators Research, 60, 155–177.
10.1023/A:1021261014699
Web of Science® Google Scholar
Malito, A. (2018) Here's what Americans earning less than $40,000 and over $100,000 worry about. Available from: https://www.marketwatch.com [Accessed 29th April 2022].
Google Scholar
Mani, A., Mullainathan, S., Shafir, E. & Zhao, J. (2013) Poverty impedes cognitive function. Science, 341, 976–980.
10.1126/science.1238041
CAS PubMed Web of Science® Google Scholar
Mayer, S.E. & Jencks, C. (1989) Poverty and the distribution of material hardship. Journal of Human Resources, 24(1), 88–114.
10.2307/145934
Web of Science® Google Scholar
MacKinnon, D.P., Krull, J.L. & Lockwood, C.M. (2000) Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1(4), 173–181.
10.1023/A:1026595011371
CAS PubMed Google Scholar
Morduch, J. (1994) Poverty and vulnerability. The American Economic Review, 84(2), 221–225.
Web of Science® Google Scholar
Morduch, J. & Schneider, R. (2017) The financial diaries: how American families cope in a world of uncertainty. Princeton, NJ: Princeton University Press.
10.1515/9781400884599
Google Scholar
Mullainathan, S. & Shafir, E. (2013) Scarcity: why having too little means so much. New York: Henry Holt and Company.
Google Scholar
Nenkov, G.Y., Inman, J.J. & Hulland, J. (2008) Considering the future: the conceptualization and measurement of elaboration on potential outcomes. Journal of Consumer Research, 35(1), 126–141.
10.1086/525504
Web of Science® Google Scholar
Netemeyer, R.G., Warmath, D., Fernandes, D. & Lynch, J.G., Jr. (2018) How am I doing? Perceived financial well-being, its potential antecedents, and its relation to overall well-being. Journal of Consumer Research, 45(1), 68–89.
10.1093/jcr/ucx109
Web of Science® Google Scholar
Newmeyer, C., Warmath, D., O'Connor, G. & Wong, N. (2021) Is savings automation helpful to liquid savings? It depends on whether you have a savings habit. Journal of Public Policy & Marketing, 40(2), 285–297.
10.1177/0743915620950216
Web of Science® Google Scholar
O'Connor, G.E., Newmeyer, C.E., Wong, N.Y.C., Bayuk, J.B., Cook, L.A., Komarova, Y. et al. (2019) Conceptualizing the multiple dimensions of consumer financial vulnerability. Journal of Business Research, 100, 421–430.
10.1016/j.jbusres.2018.12.033
Web of Science® Google Scholar
Osgood, D.W., Foster, E.M., Flanagan, C. & Ruth, G.R. (2005) On your own Without a net: the transition to adulthood for vulnerable populations. Chicago, Illinois: University of Chicago Press.
10.7208/chicago/9780226637853.001.0001
Google Scholar
Ozanne, J.L. & Saatcioglu, B. (2008) Participatory action research. Journal of Consumer Research, 35(3), 423–439.
10.1086/586911
Web of Science® Google Scholar
Ozanne, J.L., Mick, D.G., Pechmann, C. & Pettigrew, S. (2015) Transformative consumer research. Hoboken: Wiley Encyclopedia of Management. John Wiley & Sons, Ltd.
10.1002/9781118785317.weom090270
Google Scholar
Pew Research Center (2015). Pew finds American families ill-equipped for financial emergencies. Available from: https://www.pewtrusts.org [Accessed 22nd April 2022].
Google Scholar
Pew Research Center. (2021) For U.S. Latinos, COVID-19 has taken a personal and financial toll. Available from: https://www.pewtresearch.org [Accessed 22nd April 2022].
Google Scholar
Plath, D.A. & Stevenson, T.H. (2005) Financial services consumption behavior across Hispanic American consumers. Journal of Business Research, 58(8), 1089–1099.
10.1016/j.jbusres.2004.03.003
Web of Science® Google Scholar
Ponchio, M.C., Cordeiro, R.A. & Gonçalves, V.N. (2019) Personal factors as antecedents of perceived financial well-being: evidence from Brazil. International Journal of Bank Marketing, 37(4), 1004–1024.
10.1108/IJBM-03-2018-0077
Web of Science® Google Scholar
Pottow, J.A.E. (2012) The rise in elder bankruptcy filings and failure of U.S. bankruptcy law. Elder Law Journal, 19, 119–157.
Google Scholar
Rutherford, L. & DeVaney, S.A. (2009) Utilizing the theory of planned behavior to understand convenience use of credit cards. Journal of Financial Counseling and Planning, 20(2), 48–63.
Google Scholar
Saad, L. (2015) Americans' money worries unchanged from 2014. Gallup Economy. Available from: https://news.gallup.com/poll/182768/americans-money-worries-unchanged-2014.aspx [Accessed 29 April 2022].
Google Scholar
Shanks, T.R.W. (2007) The impacts of household wealth on child development. Journal of Poverty, 11(2), 93–116.
10.1300/J134v11n02_05
Google Scholar
Short, K.S. (2005) Material and financial hardship and income-based poverty measures in the USA. Journal of Social Policy, 34(1), 21–38.
10.1017/S0047279404008244
Web of Science® Google Scholar
Smith, T.J. & McKenna, C.M. (2013) A comparison of logistic regression pseudo R2 indices. Multiple Linear Regression Viewpoints, 39(2), 17–26.
Google Scholar
Stones, R. (2005) Structuration theory. Basingstoke, UK: Palgrave Macmillan.
10.1007/978-0-230-21364-7
Google Scholar
Sweet, E., Nandi, A., Adam, E.K. & McDade, T.W. (2013) The high price of debt: household financial debt and its impact on mental and physical health. Social Science & Medicine, 91, 94–100.
10.1016/j.socscimed.2013.05.009
PubMed Web of Science® Google Scholar
Tabachnick, B.G., Fidell, L.S. & Ullman, J.B. (2007) Using multivariate statistics, Vol. 5. Boston, MA: Pearson.
Google Scholar
Thorne, D., Warren, E. & Sullivan, T.A. (2009) The increasing vulnerability of older Americans: evidence from the bankruptcy court. Harvard Law and Policy Review, 3, 87.
Google Scholar
Trope, Y. & Liberman, N. (2010) Construal-level theory of psychological distance. Psychological Review, 117(2), 440–463.
10.1037/a0018963
PubMed Web of Science® Google Scholar
Ulrich, B. (2020). The real consumer debt issue. Federal News Network. Available from: https://federalnewsnetwork.com [Accessed 18th June 2022].
Google Scholar
von Stumm, S., O'Creevy, M.F. & Furnham, A. (2013) Financial capability, money attitudes and socioeconomic status: risks for experiencing adverse financial events. Personality and Individual Differences, 54(3), 344–349.
10.1016/j.paid.2012.09.019
Web of Science® Google Scholar
Wang, J.J. & Tian, Q. (2014) Consumer vulnerability and marketplace exclusion: a case of rural migrants and financial services in China. Journal of Macromarketing, 34(1), 45–56.
10.1177/0276146713508137
Web of Science® Google Scholar
Weir, D.R. & Willis, R.J. (2000) Prospects for widow poverty in the finances of married couples in the HRS. In: O. Mitchell, P.B. Hammond & A. Rappaport (Eds.) Forecasting retirement needs and retirement wealth. Philadelphia: University of Pennsylvania Press, pp. 208–234.
Google Scholar
Whelan, C.T., Layte, R., Maître, B. & Nolan, B. (2001) Income, deprivation, and economic strain. An analysis of the European community household panel. European Sociological Review, 17(4), 357–372.
10.1093/esr/17.4.357
Web of Science® Google Scholar
Wisner, B. (2004) Assessment of capability and vulnerability. In: G. Bankoff, G. Frerks & D. Hilhorst (Eds.) Mapping vulnerability: disasters, development and people. London: Earthscan Publication, pp. 183–193.
Google Scholar
Xu, Y., Beller, A.H., Roberts, B.W. & Brown, J.R. (2015) Personality and young adult financial distress. Journal of Economic Psychology, 51, 90–100.
10.1016/j.joep.2015.08.010
Web of Science® Google Scholar
Yearby, R. (2018) Racial disparities in health status and access to healthcare: the continuation of inequality in the United States due to structural racism. American Journal of Economics and Sociology, 77(3–4), 1113–1152.
10.1111/ajes.12230
Web of Science® Google Scholar

Citing Literature

Volume56, Issue3

Fall 2022

Pages 1148-1177

The role of social psychological factors in vulnerability to financial hardship

Abstract

1 INTRODUCTION