How Does Financial Life Shape Emerging Adulthood? Short-Term Longitudinal Associations Between Perceived Features of Emerging Adulthood,Financial Behaviors,and Financial Well-Being

Abstract

Becoming independent from parental financial support and developing financial capabilities are important life tasks in emerging adulthood (EA). However, research on how the accomplishment of these tasks contributes to perceptions of EA features is rare. This study investigates how functioning in the financial domain shapes perceptions of EA features during the early years of EA. Participants in this short-term longitudinal study were 533 emerging adults (57.2% women; M_age = 18.94, SD_age = .73, range 18–21 years) freshly enrolled into a set of programs at three higher education institutions. Results show that (a) financial well-being promotes more desirable (perceived) EA features, while financial difficulties tend to be related to more negative ones; (b) change in economic dependence is primarily driven by financial well-being; (c) (perceived) features of EA also contribute to how one functions in financial life; and (d) parental socioeconomic status plays at least some role in these matters.

Keywords

longitudinal transitions to adulthood well-being emerging adulthood financial well-being economic independence financial behaviors

The path to adulthood involves the transition from parental dependence to self-reliance, which is a complex and multifaceted developmental task (Eccles et al., 2003). This transition starts to take place in the period between 18 and 25/29 years, termed as “emerging adulthood” (EA; Arnett, 2014). EA is also characterized by extensive changes in all aspects of life, developmental disturbances in psychological functioning, and increasing heterogeneity in life paths (Schulenberg & Zarrett, 2006). Among the most prominent developmental tasks of this period are the formation of personal identity (Arnett, 2014; Erikson, 1968) and the acquisition of skills needed for self-sufficient living, productive occupation, and family life (Eccles et al., 2003; Havighurst, 1966).

The processes of addressing the central tasks of EA have been studied extensively in the last two decades. Social and economic conditions, parental relationships, and peer influences were studied the most extensively as the predictors of the process of becoming an adult and prominent experiences in this period (Schwartz, 2016). However, the process of becoming capable of handling personal finances and reaching independence from parental financial support (e.g., Butterbaugh et al., 2019) was investigated less. An especially understudied matter is how this transition relates to the accomplishment of other life tasks and more general EA experiences. In particular, emerging adults’ financial life issues include family financial socialization, development of financial capabilities, economic independence, and financial well-being (Serido et al., 2010; Shim et al., 2012). However, these issues have rarely been linked to outcomes in other life domains discussed in EA theory, such as identity formation in the occupational domain or perception of possibilities.

Moreover, most studies on emerging adults’ financial life issues used a cross-sectional approach and were conducted in the United States (see Gudmunson & Danes, 2011). Some large studies did use longitudinal designs (e.g., Serido et al., 2010; Shim et al., 2012), and some longitudinal studies were conducted outside of the United States (e.g., in Finland; Ranta & Salmela-Aro, 2018). However, the transition to financial self-sufficiency in contexts characterized by different economic systems is still understudied especially with longitudinal designs. Given that typical experiences of EA vary even between developed contexts (Arnett, 2011, 2016b; Arnett & Galambos, 2003; Buhl & Lanz, 2007), this presents a considerable gap of knowledge.

The current study addresses these issues by focusing on how aspects of financial life shape the main features of EA conceptualized in EA theory (Arnett, 2014). First, we distinguish objective aspects of financial well-being, in particular, income (amount of money received on a regular basis) and economic dependency (level of financial assistance from parents). Second, we assess the subjective aspects of financial well-being of financial strain (insufficient funds to cover living expenses) and financial satisfaction (satisfaction with one’s current financial situation). Third, we focus on positive financial behaviors. In particular, we focus on money management behaviors that promote financial well-being and are an important aspect of financial capabilities (Serido et al., 2013). Furthermore, we will employ a short-term longitudinal approach to study emerging adults living in Lithuania, which shares many characteristics with a broader and less represented context in the EA literature: Eastern Europe.

Financial Self-Sufficiency and the Path to Adulthood

Among the many financial issues that are prominent in EA, gaining independence from parental financial support (economic independence) could be considered central. Among the individualistic markers that emerging adults consider to be necessary for reaching adulthood, it is the most tangible (Arnett, 1998, 2003) and is highly valued across a wide range of cultural contexts (see Nelson & Luster, 2016, for an in-depth review of adulthood criteria research). The significance of economic independence for reaching adulthood may be attributed to prolonged reliance on parental financial assistance, which often lasts well into their 30s for recent generations (Padilla-Walker et al., 2012; Swartz et al., 2017). Notably, the achievement of this marker carries more than personally significant value for emerging adults; it is also associated with other markers of adulthood. In particular, cross-sectional and longitudinal studies show that the achievement of economic independence predicts increases of self-perceived adulthood (Benson & Furstenberg, 2006; Shanahan et al., 2005), decreases in drinking alcohol, an increased sense of occupational identity (Padilla-Walker et al., 2012), and decreased delinquent behaviors (Hill et al., 2017). Economically independent emerging adults are also more likely to be recognized as full adults by their parents (Padilla-Walker et al., 2012).

Clearly, reaching economic independence requires stable employment. However, effective management of personal finances also contributes to the earlier achievement of this marker (Butterbaugh et al., 2019; Lee & Mortimer, 2009; Xiao et al., 2014). Self-sufficient functioning in the domain of personal finance requires adequate financial knowledge, self-belief, skills, and positive financial behaviors, which altogether constitute financial capabilities (Lee & Mortimer, 2009; Serido et al., 2013). Development of these capabilities is conceptualized as another major life challenge for emerging adults (Serido et al., 2013).

While the formation of financial capabilities starts much earlier (Gudmunson & Danes, 2011), it is in the EA years when financial capabilities become important for individuals’ functioning. Specifically, it is in EA that complex issues in the domain of personal finance and making independent decisions related to these issues and personal money management become apparent. Indeed, sufficient financial capabilities promote emerging adults’ well-being in the domain of personal finances. Results of a large cross-sectional study showed that budgeting and saving behaviors among students were linked to higher levels of financial well-being, while risky credit card behaviors and compulsive buying were linked to lower levels of financial well-being (Gutter & Copur, 2011). These effects are present even when various demographics, such as socioeconomic status (SES), and other financial characteristics (e.g., loans) were taken into account. Longitudinal studies also support these links and indicate that positive financial behaviors are linked to financial well-being, subjective well-being, and lower overall psychological distress over time (Ranta & Salmela-Aro, 2018; Serido et al., 2010, 2013; Shim et al., 2012). Increases in financial capabilities also contribute to a faster transition to adulthood (Serido et al., 2019).

The Role of Family Factors in the Formation of Financial Capabilities

Research on the determinants of youths’ financial capabilities emphasizes the role of parental financial socialization and family characteristics. The integrative family financial socialization (FFS) model (Gudmunson & Danes, 2011), built upon an extensive review of existing research, suggests that FFS processes (e.g., financial role-modeling, financial communication, or financial expectations; Serido et al., 2019) play a crucial role in the formation of young people’s financial capabilities. The model further suggests that family characteristics, such as SES, shape these socialization processes. This statement is supported by recent empirical research. For example, a qualitative investigation found that, compared to working-class parents, middle-class parents are more proactive in teaching their children about financial matters. Working-class parents often felt unequipped to do that, which consequently seemed to lead to their adolescent children having difficulties in dealing with issues of financial life (Luhr, 2018). These findings are consistent with those from quantitative studies indicating that higher family income and parental educational levels are linked to emerging adults’ higher economic self-efficacy (Lee & Mortimer, 2009), less risky credit card behaviors (Xiao et al., 2011), higher preventive budgeting (Serido et al., 2010), and healthier attitudes toward personal finances (Shim et al., 2012). Those findings also are consistent with studies showing that these effects can be attributed to different parental financial behaviors (Damian et al., 2019; Jorgensen & Savla, 2010; Kagotho et al., 2017; Kim & Torquati, 2018). However, some of the effects of parental SES and financial capabilities are less investigated and consequently less understood.

First, less is known about the role of parental SES and financial well-being in the attainment of economic independence. Several studies indicate that financial capabilities, such as financial self-efficacy and healthy money management behaviors, predicted earlier achievement of economic independence (Lee & Mortimer, 2009; Xiao et al., 2014). However, it is unclear whether parental SES, which is known to contribute to higher financial capabilities and financial well-being can also indirectly promote the achievement of economic independence.

A second less investigated question is how financial capabilities and financial well-being contribute to more general experiences of EA. Most studies on the development of financial capabilities and its outcomes address only very general aspects of psychological functioning (e.g., subjective well-being, life satisfaction, or physical health; e.g., Curran et al., 2018; Ranta & Salmela-Aro, 2018; Serido et al., 2010, 2013; Shim et al., 2012). These aspects are relevant for people of different ages. Very few studies focus on outcomes and experiences more specific to EA theory. Considering that reaching economic independence is linked to other aspects of maturation, it is likely that financial capabilities could also shape more general experiences of EA.

Examining the abovementioned links in one study could also shed more light on the question of how family SES affects EA experiences. In particular, EA theory has often been criticized for paying insufficient attention to the impact of family SES (Bynner, 2005; Furstenberg, 2016; Silva, 2016). However, the findings on this matter are inconclusive. Some studies find no direct associations between key EA features and SES (Arnett, 2016a), some find weak associations (e.g., Hill et al., 2015), while others find substantial ones (e.g., Landberg et al., 2019). The impact of family SES on EA experiences was primarily seen via enrollment into postsecondary education (Schwartz, 2016). However, SES may also be related to developmental outcomes via issues related to functioning in the financial domain, which was not substantially studied. As such, further investigation of these links would illuminate the often debated association between SES and experiences and outcomes in EA (Arnett et al., 2011). Moreover, the investigation of SES effects on EA experiences in cultural contexts that are underrepresented in the EA literature could also shed more light on the possibility that SES effects may be more context-specific.

A final issue in the study of emerging adults’ financial behaviors is the large variety of positive (also referred to as responsible or healthy) financial behavior definitions and measures (see van Raaij, 2016; Xiao, 2015). Among the most common financial behaviors addressed in studies with emerging adults are those related to spending, saving, credit card use, and budgeting (e.g., Shim et al., 2010). However, the chosen behaviors are often considered to represent a single underlying dimension and the frequency of engagement in these behaviors is usually averaged for analyses. This may be problematic, as the adoption of some financial behaviors may actually promote the adoption of others (see Xiao, 2015, for a more extended discussion of these issues), which suggests multidimensionality among financial behaviors (also see Serido et al., 2010).

The issue of multidimensionality can be addressed by adopting the self-regulation approach to financial behaviors. This approach indicates that successful self-regulation in any domain (including the financial one) has three key dimensions. These are (1) goal and standard setting, (2) engaging in actions that lead to obtaining the goal or actions that help to keep up with the standards, and (3) monitoring the progress of goal achievement or keeping up with the desirable standards (Faber & Vohs, 2011; Vohs et al., 2008). From this perspective, positive financial behaviors represent an individual’s efforts to self-regulate in the domain of personal finances (Faber & Vohs, 2011; Vohs et al., 2008). Most positive financial behaviors can be mapped on to these three self-regulation dimensions. For example, financial planning represents setting financial goals and standards. Cash-flow monitoring (tracking monthly expenses) represents monitoring the progress toward financial goals and/or keeping up with established standards. As a final example, saving, responsible spending and credit card use, and investing represent actions to achieve certain financial goals or meet spending standards.

Grounded in the previously discussed issues, we formulated the main questions of this study: (1) what is the role of parental SES in the attainment of economic independence? (2) how do financial well-being and financial capabilities shape perceptions of EA? (3) is there an indirect association between parental SES and EA perceptions, via the financial well-being and financial capabilities? and (4) how do financial capabilities relate to financial well-being? The five EA features are discussed below.

Five Features of EA

The theory of EA (Arnett, 2000, 2014) was partly inspired by observations that young people are characterized by a distinct pattern of typical life experiences and characteristics. The five most prominent features of this period are thought to be prolonged identity exploration, instability, self-focus, feeling “in-between,” and perceived possibilities (Arnett, 2014). The centrality of identity exploration, which characterizes the attempts to form one’s identity, is grounded in the assumption that identity formation is the most crucial psychosocial task in late adolescence and EA (Arnett, 2014; Erikson, 1968). Instability relates directly to the first and characterizes the constant change of life direction. Objectively, it is seen as the recurrent change of residence, romantic partners, and jobs. Subjectively, it manifests and is often addressed through negative feelings of uncertainty, anxiety, and stress (Arnett, 2014; Lanctot & Poulin, 2018; Luyckx et al., 2011). A strong self-focus reflects attempts to become self-sufficient, independent, and responsible (Arnett, 1998, 2014). As it takes much longer and it is harder to reach adulthood, many feel “in-between” (i.e., neither adolescents nor adults). Lastly, as emerging adults can be rather uncertain about their life direction as many life paths remain open. Therefore, EA is the age of possibilities (Arnett, 2014).

There are large individual differences in the extent to which these features are applicable to emerging adults (Lanctot & Poulin, 2018; Tagliabue et al., 2016). This suggests that EA can be experienced very differently (Schwartz, 2016). Importantly, individual differences in the five features are also linked to a range of adjustment issues prominent in this life period. Features such as identity exploration, self-focus, and perceived possibilities are typically considered as more positive, as the prominence of these perceptions is linked to more positive outcomes. For example, identity exploration was linked to lower levels of substance use (Allem et al., 2017) although other studies link it to higher levels of internalizing symptoms (Baggio et al., 2017; Lanctot & Poulin, 2018). Self-focus and perceived possibilities were also linked to higher satisfaction with life (Negru, 2012) and other positive outcomes (Nelson et al., 2015). Conversely, instability and feeling “in-between” were linked to more substance use (Smith et al., 2014), poorer mental health (Baggio et al., 2017), and other less positive outcomes (Lanctot & Poulin, 2018). However, less is known about how functioning in the financial domain shapes these characteristics and experiences.

The Current Study

The main goal of this study was to investigate how functioning in the financial domain predicts perceived features of EA during the first year after high school completion. For the financial domain, we focus on the more objective markers of parental SES, income (i.e., total sum from all income sources), economic dependency (i.e., the proportion of income that comes from parental financial support; see Lee & Mortimer, 2009, for a similar approach), and positive financial behaviors as a proxy for financial capabilities. We also assessed subjective financial well-being, subdivided in financial strain (i.e., the experience of insufficient income) and financial satisfaction (i.e., general satisfaction with current financial situation). In line with the discussed self-regulation approach on financial behaviors, we first investigated financial planning (propensity to plan for and set short- and long-term financial goals; Lynch et al., 2010), which targets the self-regulation dimension of goal and standard setting. Second, we focused on saving (saving money to achieve certain financial goals or to buffer for unforeseen expenditure). Third, we assessed controlled spending operationalized as exercising control in spending decisions (i.e., regulating one’s spending-related thoughts and decisions by self-imposed standards; Haws et al., 2012). Controlled spending combined with saving targets engagement in pursuing the goal or keeping up with the standards. Fourth, we examined cash-flow monitoring (i.e., tracking and monitoring monthly purchases and expenses), which targets the monitoring dimension of self-regulation.

We use data from an ongoing short-term longitudinal study conducted in Lithuania, a context characterized by seemingly high opportunities to attain high education and get a job, yet low economic well-being. In terms of patterns of transitioning to adult roles, Lithuanian emerging adults follow a rather diverse set of paths that are compatible with those found in other Western contexts (Vosylis, 2018). However, paths characterized by earlier entry into adult roles and those characterized by late home leaving are more common than in Western Europe countries (Schwanitz, 2017). The number of emerging adults living with parents, however, has decreased after the 2008 financial crisis. The proportion of 20- to 24-year-olds living with parents in 2018 was 69.8%, compared to 84.6% in 2009, and is now lower than the European Union (EU) average (Eurostat, 2019c). During the last decade, employment rates among Lithuanian emerging adults have also steadily increased. In 2018, 54.1% of 20- to 24-year-olds were employed, which is very close to EU average of 54.0% (Eurostat, 2019d). Notably, low cost of higher education provides Lithuanians with good opportunities for obtaining a tertiary degree. These opportunities are widely used, as in 2017, the percentage of those entering tertiary education was 70% (Statistics Lithuania, 2019). In addition, during the last several years, compared to other EU countries, Lithuania had the largest proportion of the population aged 30–34 with a tertiary education degree (around 58%, compared to around 39% of EU average; Eurostat, 2019b). While low employment and high education levels may seem to be positive features, economic well-being in Lithuanian emerging adults is low when compared to emerging adults from other European countries. This may be due to studying and working often being combined. That is, despite higher levels of employment, Lithuanian emerging adults have the smallest mean disposable income (the amount of money left to be spent or saved as one wishes, after income taxes have been deduced; as defined and indicated by the Organisation for Economic Co-operation and Development [OECD], 2019) compared to other Eurozone countries. The proportion of emerging adults at risk of poverty is also among the largest compared to other EU countries (18- to 25-year-olds; Eurostat, 2019a). This results in a highly prolonged dependence on parental financial assistance (Kraniauskienė, 2013).

In general, we use an exploratory approach to address the reciprocal longitudinal associations between study variables. However, research and theoretical models discussed in the previous sections were consulted to guide the inclusion of certain aspects of financial life in the study and build general predictions. Based on the previously discussed studies, we expected that (Hypothesis 1) higher parental SES would be positively associated with financial well-being (higher income, lower financial strain, higher subjective financial well-being), and (Hypothesis 2) with a higher level of engagement in positive financial behaviors. Also, based on the predictions of the FFS model (Gudmunson & Danes, 2011) and previous research (Serido et al., 2013), we expected that (Hypothesis 3) positive financial behaviors would promote financial well-being. Guided by previous research (Serido et al., 2013), we also expected that (Hypothesis 4) financial well-being indicators would be associated with perceived EA features. Grounded in previous research on the effects of economic dependence, we expected that (Hypothesis 5) higher levels of financial dependency would also be associated with perceived features of EA.

Also in line with this approach, we expected that (Hypothesis 6) financial planning and cash-flow monitoring would promote actions (saving and exercising control in spending decisions) taken to achieve financial goals. Many studies (primarily those conducted in the United States) also address behaviors related to credit card use, investment behaviors, or insurance. We do not address these issues in this study, as most Lithuanian (and many other European) emerging adults rely on debit cards instead of credit cards. This is because Lithuanian banks (like most European ones) have stricter requirements for credit card applicants (e.g., requirement to have regular and stable levels of income for a prolonged period of time) compared to U.S. banks. Also, the very low levels of income among emerging adults limit opportunities for investment or insurance.

Method

Participants and Procedures

Participants in this ongoing short-term longitudinal study were 533 emerging adults (57.2% women; M_age = 18.94, SD_age = .73, range 18–21 years) freshly enrolled into a diverse set of programs at three higher education institutions in Lithuania, that is, two universities (35% of participants) and one large vocationally oriented nonuniversity higher education institution (college; 65% of participants). Although all participants were students, the sample was diverse in terms of living situations. During the initial assessment, 49% of participants were living with their parents, while the other 51% were living in a dormitory, rented flat, or another type of temporary household. Of the participants, 78% were unemployed, while the rest indicated that they were employed full time or part time. Only 4% were married and only one (0.2%) indicated having a child.

Over 87% indicated that their monthly income was between 100 and 500 euros, and only 6% indicated income levels of ≥700 euro. Most (87%) received at least some financial support from parents and 36% indicated that most of their income (80–100%) came from parental support. Among alternative sources of income were salary (25%), stipends (2%), loans (2%), and an unspecified “other” (17%). Only 7% of participants indicated that they borrowed money to fund their studies. Although no comparative data are available for the first-year college and university students sampled in this study, samples from previous studies involving the same institutions with students from different bachelor and master years were highly comparable to the general population of Lithuanian students (Vosylis & Erentaitė, 2019).

Participant retention rates were 84% (n = 448) and 77% (n = 411) at the second and third assessments, respectively. Little’s MCAR (Missing Completely At Random) test suggested that data were missing completely at random (χ² = 835.80, df = 843, p = .563). Therefore, full information maximum likelihood estimation was used to deal with missing data (Enders, 2010).

The study’s first assessment took place in October 2018 with consecutive assessments in February and May 2019. It took around 25 days to collect the data at each assessment and the time span between assessments (from the moment when an ongoing electronic survey was closed to the moment when a subsequent one was opened) was 13 weeks. Participants were informed about the purpose and length of the study, the content of the survey, and the intended use of the data. Potential participants were asked to indicate whether they wanted to participate and, if so, to sign an informed consent. Questionnaires were then administered to those who agreed to participate (approximately 95%).

During the initial assessment, questionnaires were administered in classrooms during class hours via tablet computers using the SurveyMonkey.com internet survey platform. During the second assessment, most (∼80%) questionnaires were administered in classrooms during class hours via tablet computers. Participants who were not present were contacted by email. These emails contained a link to the survey that these participants filled out at their own convenience. By the third assessment, most participants were no longer on campus, either because the semester was over or because they were doing a practical internship. Therefore, all participants were contacted by email. Participants were rewarded with small (worth of ∼1 euro) prizes for participation in the second assessment and with larger prizes (worth 5–6 euro) for participation in the third assessment.

Measures

During the initial assessment, participants received a battery of instruments targeting various aspects of financial life and EA-specific developmental outcomes. Although other financial behaviors (impulsive spending, compulsive buying) and aspects of financial well-being (financial anxiety) were also presented, we did not address these in this study. Items on parental SES were presented only in the initial assessment. All other items were presented in each assessment and before responding to the items, participants were asked to think about the last several months. All items belonging to financial behavior scales, subjective financial well-being, and scales targeting features of EA were scored on a scale ranging from 1 (completely disagree) to 5 (completely agree). All items within these scales were also randomized for each participant to avoid any item-order effects. Scale reliability was assessed with a composite reliability (ρ) index (Raykov, 1997), using the unstandardized parameter estimates from longitudinal measurement models with strict invariance (see Results section).

Family SES

Participants were asked to indicate their parents’ education. Possible alternatives were secondary or lower (scored as 1), higher or highest nonuniversity (2), or highest university (3). They further reported on employment status, with the alternatives works at a permanent job (scored 1); works at temporary jobs (2); unemployed, looking for a job (3); unemployed, retired (4); and unemployed, ‘other reasons’ (5). Finally, the following possible income categories were distinguished: less than 500 euro per month (scored 1), 500–850 euro per month (2), 850–1,700 euro per month (3), and more than 1,700 euro per month (4). The three categories for different types of unemployment were merged, as some responses were very rare. The cutoffs for the income categories were selected based on national income data, where 500 euro was close to the national minimum monthly wage, 850 euro was close to the average monthly wage, and 1,700 euro was twice the average at the time of the initial assessment. These items were used to create SES groups. Specifically, responses to these questions were subjected to latent class analysis (see Supplementary Material, for more detailed results), which suggested a three-class solution. A class membership variable was saved creating three SES groups: “lower,” “middle,” and “higher.”

Income

Income was measured by providing a single item “Please indicate your total recent monthly income that includes all sources of your income, such as parental support, salary, stipend, and so on” and were provided a scale: up to 100 euro (scored as 1), 101–200 euro (2), 201–300 euro (3), 301–500 euro (4), 501–700 euro (5), 700–1,000 euro (6), 1,000–1,500 euro (7), and more than 1,500 euro (8).

Economic dependency

Economic dependency was measured by an item asking participants to indicate “How much of your current income comes from parental support?” on the given scale: 0% (scored as 1), around 25% (2), around 50% (3), around 75% (4), and 100% (5).

Financial strain

Financial strain was measured by asking participants to think about the last couple of months and indicate how often they ran out of money before the next typical time that they received their monthly money, on a scale never (scored as 1), once in a couple of months (2), once a month (3), couple of times during last month (4), and always (5).

Financial satisfaction

Financial satisfaction was measured using a General Subjective Financial Well-Being Scale from the Multidimensional Subjective Financial Well-Being Scale for Emerging Adults (Sorgente & Lanz, 2019). This scale consists of 10 items (e.g., “I’m satisfied with my present financial situation”; ρ = .95).

Financial planning

Financial planning was assessed using a Generalizable Scale of Propensity to Plan in Money domain (Lynch et al., 2010). This scale consists of two subscales targeting short-term (6 items, e.g., “I set financial goals for the next few days for what I want to achieve with my money”) and long-term (6 items, e.g., “I set financial goals for the next 1–2 months for what I want to achieve with my money”) financial planning. As the subscales were strongly correlated, we combined them into a 12-item single indicator (ρ = .93).

Cash-flow monitoring

Cash-flow monitoring was assessed with the Cash-flow Monitoring subscale from a Brief Money Management Scale (Ksendzova et al., 2017), which consists of 4 items (e.g., “I review and evaluate spending on a regular basis”; ρ = .81).

Saving

Saving was assessed with the Saving subscale from a Brief Money Management Scale (Ksendzova et al., 2017), which consists of 4 items (e.g., “I regularly set aside money for saving”; ρ = .83).

Controlled spending

Controlled spending was assessed with the Consumer Spending Self-Control Scale (Haws et al., 2012), which consists of 10 items (e.g., “I carefully consider my needs before making purchases”; ρ = .94).

Features of EA

Features of EA were assessed using the short version (Crocetti et al., 2015) of the Inventory of Dimensions of Emerging Adulthood (Reifman et al., 2007). This measure has five 3-item scales that target perceived features of EA: identity explorations (e.g., “Is this period of your life a time of defining yourself?”; ρ = .85), instability (e.g., “Is this period of your life a time of many worries?”; ρ = .90), self-focus (e.g., “Is this period of your life a time of independence?”; ρ = .87), feeling in-between (e.g., “Is this period of your life a time of feeling adult in some ways but not others?”; ρ = .82), and perceived possibilities (e.g., “Is this period of your life a time of many possibilities?”; ρ = .91).

Data Analysis Strategy

Descriptive statistics and correlations were estimated in SPSS Version 21. All other analyses were conducted using Mplus Version 7.4 with maximum likelihood robust estimation. To investigate the bidirectional effects of study variables, we used latent difference score (LDS) models (McArdle, 2009; Newsom, 2015). Compared to standard cross-lagged panel models (CLPMs), LDS models focus on within-person change and predictors thereof rather than on between-person change (McArdle, 2009). The specific LDS model that we used was the change regression model (McArdle, 2009), which is a transformation of the CLPM in which the between-individual differences (including those for the same variable) at the previous occasions (T1 and T2) predict the within-individual change at the subsequent occasions (change from T1 to T2 and T2 to T3, respectively) rather than the relative change at the subsequent occasions, as in CLPM.

In general, we used latent variables in our models (multiple indicator models), as this approach corrects parameter estimates for attenuation due to measurement error. However, income, economic dependency, and financial strain were treated as observed variables since these variables were assessed with single-item measures. Items were used as latent variable indicators for perceived EA features, saving, and cash-flow monitoring. Item parcels were used for financial satisfaction, financial planning, and spending self-control to avoid models from getting overly complex. Three parcels were formed by using the item-to-construct balancing approach based on corrected item-total score correlations (Little, 2013). To investigate the possibility that the item parceling process may have camouflaged poor model-to-data fit, we ran a separate set of confirmatory factor analyses. Specifically, we tested longitudinal measurement invariance separately for financial satisfaction, financial planning, and spending self-control scales, using items as indicators of these latent constructs. Our results indicated good or adequate model data fit for all three constructs with no signs of longitudinal measurement noninvariance. This suggests that item parceling did not mask model misspecification. These results are reported in the Supplementary Material.

LDS models with multiple indicators require longitudinal equality constraints on factors loadings and intercepts (strong or scalar measurement invariance; Newsom, 2015). Therefore, we first evaluated whether measurement model parameters for the latent variables we used were invariant over time. We also tested for strict invariance (equivalence of residual errors) to see whether a more parsimonious model would fit the data. Latent variables were scaled using effects coding method (Little et al., 2006), as this is a general recommendation for multiple indicator LDS models (Newsom, 2015).

The comparative fit index (CFI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) were used to assess model fit. CFIs > .90 and RMSEAs and SRMRs < .08 indicated acceptable fit, and CFIs > .95 and RMSEAs and SRMRs < .05 indicated good fit (Little, 2013). Statistically significant differences between nested models were tested using scaled χ² difference test (Satorra & Bentler, 2001). Practically significant differences were assessed using model fit statistics: ΔCFI ≥ −.01 was considered a substantial decrease in model fit (Little, 2013). Since participants were nested within study programs, in all our analyses, we used the “type = complex” command in Mplus. Specifically, we used study program as a cluster variable. This procedure accounts for the effects that dependencies of observations due to clustering may have on the standard errors of parameters and provides more accurate estimates of statistical significance (Muthén & Satorra, 1995).

Results

Correlations Between Study Variables

Correlations between financial domain variables measured at the three different occasions are presented in Table 1. Correlations between financial domain variables and perceived features of EA measured at three different occasions are presented in Table 2. Small-to-medium-sized (Cohen, 1988) correlations were found between financial domain variables and features of EA.

Table 1.
Zero-Order Correlations Between Objective and Subjective Aspects of Financial Well-Being and Financial Behaviors Measured at Three Different Occasions.

Aspects of financial well-being and financial behavior 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1. Income (T1)

2. Income (T2) .58

3. Income (T3) .53 .63

4. Economic dependency (T1) −.30 −.18 −.19

5. Economic dependency (T2) −.23 −.36 −.30 .61

6. Economic dependency (T3) −.18 −.28 −.45 .55 .74

7. Financial strain (T1) .02 .06 .10 .02 −.07 −.12

8. Financial strain (T2) .02 −.05 −.03 −.01 −.04 −.10 .50

9. Financial strain (T3) −.06 −.02 −.07 −.01 −.08 −.03 .38 .50

10. Financial satisfaction (T1) .16 .07 .05 .11 .19 .20 −.49 −.39 −.34

11. Financial satisfaction (T2) .07 .09 .05 .20 .22 .21 −.33 −.49 −.35 .66

12. Financial satisfaction (T3) .09 .09 .10 .15 .18 .11 −.28 −.40 −.44 .62 .74

13. Financial planning (T1) −.03 .01 .05 −.04 −.02 −.08 .06 .04 .01 −.05 −.03 −.05

14. Financial planning (T2) .08 .11 .13 −.06 −.08 −.12 .05 .04 .03 −.05 −.02 −.01 .53

15. Financial planning (T3) .10 .12 .11 −.04 −.07 −.12 .08 .06 .04 −.09 −.08 −.01 .50 .58

16. Cash-flow monitoring (T1) .03 .01 .05 −.07 −.06 −.04 −.10 −.04 −.01 .10 .05 .04 .59 .39 .35

17. Cash-flow monitoring (T2) .01 .10 .07 −.07 −.06 −.10 −.20 −.19 −.18 .08 .16 .14 .38 .62 .37 .41

18. Cash-flow monitoring (T3) .10 .10 .14 −.04 −.02 −.07 .06 .01 −.04 −.01 .02 .10 .41 .50 .58 .44 .45

19. Saving (T1) .10 .01 .02 −.16 −.06 −.05 −.20 −.14 −.16 .10 .05 .03 .34 .30 .22 .43 .46 .31

20. Saving (T2) .06 .09 .07 −.14 −.14 −.16 −.14 −.18 −.19 .05 .12 .09 .33 .47 .26 .32 .75 .38 .58

21. Saving (T3) .08 .10 .14 −.07 −.09 −.17 −.07 −.09 −.19 .09 .16 .19 .26 .32 .37 .23 .46 .52 .40 .56

22. Controlled spending (T1) −.13 −.13 −.10 .04 .03 −.02 −.17 −.12 −.13 .09 .11 .09 .46 .27 .27 .49 .31 .33 .32 .22 .19

23. Controlled spending (T2) −.11 −.05 −.05 .05 .09 .07 −.19 −.24 −.18 .07 .19 .13 .30 .42 .33 .31 .49 .30 .24 .36 .22 .50

24. Controlled spending (T3) −.02 −.01 −.04 .00 .06 .04 −.04 −.15 −.13 .02 .09 .16 .26 .34 .50 .23 .34 .41 .15 .18 .27 .36 .57

Mean 2.88 3.44 2.32 2.90 3.33 1.91 3.10 3.14 1.76 3.43 3.39 3.42 3.28 3.36 3.33 2.79 3.35 3.04 3.11 3.25 3.20 3.80 3.91 3.89

SD 1.54 1.49 1.45 1.45 1.50 1.17 1.41 1.59 1.02 0.98 0.93 0.92 0.93 0.91 0.94 0.94 0.97 0.93 1.15 1.16 1.15 .73 .69 .69

Note. All correlations equal or above .09 and underlined are statistically significant at p < .05.

Table 2.
Zero-Order Correlations Between Perceived Features of Emerging Adulthood and Objective and Subjective Aspects of Financial Well-Being and Financial Behaviors Measured at Three Different Occasions.

Financial Life Variables Income Economic Dependency Financial Strain Financial Satisfaction Financial Planning Cash-Flow Monitoring Saving Controlled Spending

Features of EA Time T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Identity explorations T1 −.04 −.03 −.04 .09 .08 .04 .11 .09 .03 .00 −.01 .02 .18 .12 .15 .04 .04 .06 .02 .05 .05 .08 .09 .10

Identity explorations T2 −.03 .02 −.02 .01 .01 .02 .08 .10 .07 −.08 −.08 −.05 .15 .15 .15 .14 .07 .10 .05 .08 .01 .03 .11 .07

Identity explorations T3 −.07 −.05 −.10 .04 .03 .08 −.02 .06 .07 −.03 −.07 −.06 .02 .04 .07 .05 −.02 .00 .04 .00 .03 .05 .08 .10

Instability T1 −.07 −.06 −.05 .06 .08 .03 .19 .21 .19 −.29 −.25 −.23 .12 .04 .10 .00 −.01 −.03 −.02 −.01 −.07 .00 .02 .02

Instability T2 −.05 −.02 −.07 −.01 −.02 −.03 .12 .22 .19 −.19 −.31 −.21 .07 .07 .07 .02 −.05 −.03 −.01 −.02 −.07 .00 .00 −.04

Instability T3 −.06 −.03 −.04 .00 −.03 .02 .13 .16 .22 −.25 −.29 −.33 .06 −.02 .06 −.01 −.17 −.06 −.02 −.10 −.09 −.01 −.02 −.05

Self-focus T1 .06 −.01 .04 .04 .03 .03 .05 .07 .03 .01 .01 −.01 .10 .08 .08 .05 .08 .07 .05 .02 .05 .11 .11 .17

Self-focus T2 −.01 .03 .04 .00 .01 .03 −.01 −.03 .03 .06 .05 .04 .08 .07 .09 .09 .07 .01 .01 .06 .01 .04 .13 .06

Self-focus T3 .00 .02 .07 .08 .08 .00 .05 .04 .03 .02 .01 .06 .03 .11 .13 .01 .04 .05 −.03 −.03 .08 .09 .19 .17

Feeling in-between T1 −.10 −.10 −.07 .17 .12 .11 .08 .12 .07 −.04 −.02 .03 .07 .00 .00 .01 −.07 −.01 −.04 −.13 −.04 .02 −.06 .00

Feeling in-between T2 −.19 −.11 −.13 .14 .15 .11 .06 .07 .10 −.05 −.07 −.05 .11 .10 .01 .01 .04 −.05 −.04 −.02 −.09 .00 .10 −.03

Feeling in-between T3 −.14 −.13 −.10 .13 .09 .07 .07 .09 .08 −.07 −.08 −.02 .01 .00 .02 −.01 −.09 −.03 −.09 −.14 −.01 .05 .02 .05

Perceived possibilities T1 .10 .09 .15 .03 −.02 −.05 −.05 −.03 −.10 .12 .14 .14 .13 .14 .10 .06 .06 .07 .06 .10 .05 .08 .12 .11

Perceived possibilities T2 .02 .04 .14 −.03 −.05 −.05 .03 −.02 .03 .04 .04 .09 .05 .10 .06 .07 .04 .11 .03 .08 .08 .01 .12 .08

Perceived possibilities T3 .03 .02 .07 .04 −.02 −.06 −.01 .00 −.04 .08 .04 .15 .08 .07 .14 .04 .00 .11 −.02 −.02 .11 .12 .15 .19

Note. All correlations equal or above .09 and underlined are statistically significant at p < .05. EA = emerging adulthood.

Measurement Invariance Analyses

Two sets of measurement invariance analyses were conducted: one for latent variables representing financial well-being and financial behaviors and one for latent variables representing perceived features of EA. In both cases, results indicated that all measurement model parameters were stable across the three measurement occasions (see Table 3), as adding equality constraints did not produce significant changes in model fit (ΔCFI < .01) in any step of the analyses.

Table 3.
Results of Structural Equation Modeling Analyses.

Model Tested (Model Compared With) Model Fit Statistics Model Comparison

χ² df n _par p CFI RMSEA [90% CI] SRMR Δχ² Δdf p ΔCFI ΔRMSEA

Longitudinal measurement invariance analysis for features of emerging adulthood (IDEA measure)

Configural 1,429.50 795 285 <.001 .934 .039 [.035, .042] .055 — — — —

Weak (vs. configural) 1,464.69 815 265 <.001 .932 .039 [.035, .042] .058 35.23 20 .019 −.002 .000

Strong (vs. weak) 1,514.33 835 245 <.001 .929 .039 [.036, .042] .059 49.59 20 <.001 −.003 .000

Strict (vs. strong) 1,535.02 865 215 <.001 .930 .038 [.035, .041] .065 36.80 30 .182 .001 −.001

Longitudinal measurement invariance analysis for positive financial behaviors and financial satisfaction

Configural 1,794.84 1,068 309 <.001 .952 .036 [.033, .039] .046 — — — —

Weak (vs. configural) 1,819.62 1,092 285 <.001 .952 .035 [.032, .038] .047 22.65 24 .541 .000 −.001

Strong (vs. weak) 1,886.42 1,116 261 <.001 .949 .036 [.033, .039] .048 70.15 24 <.001 −.003 .001

Strict (vs. strong) 1,997.95 1,150 227 <.001 .944 .037 [.034, .040] .050 97.77 34 <.001 −.005 .001

LDS model linking financial well-being, financial behaviors, and features of emerging adulthood

Measurement model 7,643.58 4,777 1,000 <.001 .907 .034 [.032, .035] .051 — — — — —

Model A 7,998.64 4,946 829 <.001 .901 .034 [.033, .035] .054 380.03 169 <.001 −.006 .000

Model B 8,257.92 5,115 660 <.001 .898 .034 [.034, .035] .057 259.23 169 <.001 −.003 .000

Model C 8,331.46 5,193 582 <.001 .898 .034 [.034, .035] .058 77.29 78 .501 .000 .000

Note. N = 533. CFI = comparative fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual; CI = Confidence Interval; n_par = number of free parameters in the model; LDS = latent difference score.

p < .05. p < .01. p < .001.

Reciprocal Links Between Objective and Subjective Aspects of Financial Well-Being and Positive Financial Behaviors

After addressing measurement invariance (see Supplementary Material), we proceeded to build a change regression LDS model (McArdle, 2009). In the initial analysis, parental SES was dummy coded and two dichotomous variables (lower vs. medium and lower vs. higher) were added as predictors of initial level and change score variables. However, analyses in which parental SES was treated as an interval variable produced similar results. As such, we decided to continue with the second approach.

Following an often-used sequence in CLPM analyses (e.g., Little, 2013), we also checked if a more parsimonious LDS model had a similar fit with the data. The initial model (Model A in Table 3) had an adequate fit to the data and did not differ significantly (ΔCFI < −.01) from a measurement model in which all variables were allowed to freely correlate. Next, we constrained all paths linking level and difference score variables to be time invariant to evaluate whether the effects were the same between different waves (Model B in Table 3). These constraints did not worsen model fit. Finally, we tested whether constraints added for residual correlations linking the same change score variables, but at different occasions would decrease model fit (Model C in Table 3). Adding these constraints also had no effect on model fit and thus these were retained.

To make the results of the model more readable, we displayed them in three figures. Simple correlations on the first occasion for financial well-being and behaviors are not presented, as these replicate the results of correlation analyses. We also do not present longitudinal associations between perceived EA features (although these were estimated), as these are not focal interests in the present study. To interpret the model results, we focused on (a) the reciprocal longitudinal associations, that is, associations between level of predictor variable at the previous occasion and the subsequent difference score of the outcome variable (presented in Figures 1 –3) and (b) correlated change, that is, correlations between difference score variables (presented in Table 4).

Figure 1.
Averaged standardized estimates of the change regression latent difference score (LDS) model linking parental socioeconomic status (SES), aspects of financial well-being, financial behaviors, and perceived features of emerging adulthood. Only reciprocal links between aspects of financial well-being, financial behaviors, and parental SES are displayed in this figure. Although not displayed for sake of clarity, this model includes correlations between variables measured at the first occasion, residual correlations change variables measured at second and third occasion, measurement model parameters, fixed effects used to build LDS model, and statistically insignificant effects. All visible paths in the model are statistically significant at p < .05 in the unstandardized model solution. The path linking parental SES and the change of financial planning is significant only for the change of financial planning from T2 to T3.

Figure 2.
Averaged standardized estimates of the change regression latent difference score (LDS) model linking parental socioeconomic status (SES), aspects of financial well-being, financial behaviors, and perceived features of emerging adulthood. Only longitudinal links between parental SES, aspects of financial well-being, financial behaviors, and self-perceived possibilities are displayed in this figure. Although not displayed for sake of clarity, this model includes correlations between variables measured at the first occasion, residual correlations change variables measured at second and third occasion, measurement model parameters, fixed effects used to build LDS model, and statistically insignificant effects. All visible paths in the model are statistically significant at p < .05 in the unstandardized solution. The path linking parental SES and the change of identity explorations is significant only for the change of identity explorations from T2 to T3.

Figure 3.
Averaged standardized estimates of the change regression latent difference score (LDS) model linking parental socioeconomic status (SES), aspects of financial well-being, financial behaviors, and perceived features of emerging adulthood. Only the effects of features of emerging adulthood on financial well-being aspects and financial behaviors are displayed in this figure. Although not displayed for sake of clarity, this model includes correlations between variables measured at the first occasion, residual correlations change variables measured at second and third occasion, measurement model parameters, fixed effects used to build LDS model, and statistically insignificant effects. All visible paths in the model are statistically significant at p < .05 in the unstandardized model solution. The path linking parental SES and the change of financial planning is significant only for the change of financial planning from T2 to T3.

Table 4.
Correlations Between Change Scores for Objective and Subjective Financial Well-Being, Positive Financial Behaviors, and Perceived Emerging Adulthood Features at the Second Occasion.

Aspects of financial well-being and financial behaviors, and perceived features of emerging adulthood 1 2 3 4 5 6 7 8

1. Income (change)

2. Economic dependency (change) −.33*

3. Financial strain (change) −.10* .07*

4. Financial satisfaction (change) .10* −.06* −.32*

5. Financial planning (change) .02 −.04 .02 .07*

6. Cash-flow monitoring (change) .11* .00 −.06* .15* .66*

7. Saving (change) .08* −.11* −.11* .17* .36* .59*

8. Controlled spending (change) −.02 .06 −.11* .21* .41* .47* .29*

9. Identity explorations (change) −.04 .08 .03 −.05 .06 −.01 .05 .08

10. Instability (change) .02 .03 .12* −.23* .09 .02 .01 .00

11. Self-focus (change) .05 −.01 −.05 .03 .08 −.01 .12* .13*

12. Feeling in-between (change) .00 .03 −.03 .03 .17* .10* .18* .19*

13. Possibilities (change) .00 −.03 −.05 .08 .08 −.01 .09 .10*

*p < .05.

Figure 1 displays reciprocal longitudinal links between aspects of financial well-being and financial behaviors and associations with parental SES. In general, we found that financial well-being aspects were interrelated across time. Income predicted subsequent decreases in financial dependency, and higher financial dependency predicted decreases in income. Residual correlations between changes in these variables were also negative, showing that as income increased economic dependency decreased. Interestingly, higher levels of financial strain predicted a decrease of economic dependency, while financial satisfaction had an opposite effect. Residual correlations between changes in these variables showed that decreases in economic dependency were associated with decreases in financial strain and increases in subjective financial well-being. Higher levels of economic dependency predicted later increases in financial satisfaction, while financial satisfaction predicted the decrease of financial strain. Residual correlations between changes in financial satisfaction and financial strain were negative.

Higher parental SES was linked to higher initial levels of income and financial satisfaction, thus supporting Hypothesis 1. However, contrary to Hypothesis 2, SES was not positively associated with positive financial behaviors. In fact, a negative link between SES and controlled spending suggested that those coming from higher SES tended to exercise less control on their spending. In addition, a negative link of SES with the change of financial planning between Waves 2 and 3 suggested that those coming from higher SES tended to engage less in financial planning in the longer term. As a supplementary analysis, we investigated indirect effects of SES on the change of study variables (only for the change between T1 and T2), via the initial higher levels of income, financial satisfaction, and lower levels of controlled spending. For the financial domain variables, we found that higher parental SES predicted (a) a decrease in financial strain via a higher initial level of income (effect = −.018, 95% confidence interval [CI] = [−.031, −.005]), (b) an increase of economic dependency via a higher initial level of financial satisfaction (effect = .016, 95% CI [.002, .031]), (c) a decrease of economic dependency via a higher initial level of income (effect = −.014, 95% CI [−.022, −.005]), and (d) a decrease of financial planning via a higher initial level of financial satisfaction (effect = −.015, 95% CI [−.028, −.003]). Taken together, these results suggested that parental SES had an effect on all financial well-being aspects, and most interestingly, it had a somewhat mixed effect on changes in economic independence. Specifically, there was a positive effect (higher SES linked to increases in economic dependency) via financial satisfaction and a negative effect (higher SES linked to decreases in economic dependency) via initial higher levels of income.

The reciprocal associations between financial behaviors were mostly in line with our Hypothesis 6. Specifically, higher levels of planning predicted increases in cash-flow monitoring and saving, while cash-flow monitoring predicted increases in controlled spending. In addition, we found that saving predicted decreases in cash-flow monitoring. Residual correlations between changes of all four financial behaviors were also positive, indicating that increases and decreases in these behaviors were also dynamically interrelated.

Higher levels of both saving and controlled spending predicted decreases of financial strain. This only partially supported our Hypothesis 3 because the hypothesized link between financial behaviors and financial satisfaction was not found. However, negative residual change correlations between positive financial behaviors and financial strain and subjective financial well-being were in line with Hypothesis 3. This suggests that as these behaviors increased, financial strain decreased, and well-being increased. A somewhat unexpected finding was that higher levels of financial strain predicted a subsequent decrease in controlled spending.

Reciprocal Links Between Objective and Subjective Financial Well-Being, Positive Financial Behaviors, and Perceived Features of EA

Figure 2 illustrates the reciprocal longitudinal links between aspects of financial well-being and behaviors with subsequent changes in EA features. Hypothesis 4 that financial well-being indicators would be associated with perceived EA features was partly supported. For objective aspects of financial well-being, we found that higher levels of income predicted decreases of “in-between” feelings and perceived possibilities, while higher initial levels of subjective well-being predicted an increase in self-focus. However, we also found some effects of financial behaviors on perceived features of EA. Specifically, cash-flow monitoring predicted increases in perceived possibilities, while saving predicted decreases in self-focus, feeling in-between, and perceived possibilities. Although levels of financial well-being aspects and behaviors did not predict subsequent change in instability, residual correlations suggested that as the financial strain increased and subjective financial well-being decreased, perceptions of instability increased. Some positive residual correlations were also found between financial behaviors and feelings of being “in-between,” self-focus, and possibilities. For economic dependency, we found no substantial effects (with the exception of zero-order correlations at the three occasions), suggesting that during the first year after school completion, the level of economic dependency does not shape perceived features of EA. As such, our Hypothesis 5 was not supported by the study results.

Unexpectedly, we found that perceived features of EA also predicted the subsequent change of financial domain variables. Figure 3 presents reciprocal longitudinal links between parental SES, levels of perceived EA features, and subsequent change in financial well-being aspects and behaviors. In particular, higher initial levels of instability and feelings “in-between” were associated with subsequent increases in financial strain. In addition, higher levels of self-focus predicted increases in financial planning and controlled spending, while perceived possibilities predicted increases in income, savings, and controlled spending. Importantly, feeling “in-between” predicted increases in financial strain and decreases in all four financial behaviors (planning, cash-flow monitoring, saving, and exercising control in spending decisions). Another noteworthy finding was the lack of significant associations between parental SES and initial levels of EA features. As for the indirect effects, we found that higher parental SES predicted the decrease of feeling “in-between” via the higher initial level of income (effect = −.028, 95% CI [−.050, −.006]).

Discussion

This study addressed how financial well-being and financial behaviors shape the change of the perception of EA features during the first year after finishing high school in an understudied Eastern European context. We found substantial longitudinal interplay between indicators of objective and subjective financial well-being, financial behaviors, and features of EA in a sample of students enrolled in their freshman year of higher education. Below, we will discuss these findings in detail.

First, we found a set of effects indicating interplay between financial domain variables. Importantly, changes in economic dependence were primarily affected by other aspects of financial well-being. However, these effects together provided a somewhat puzzling picture, indicating that objective and subjective financial well-being contribute differently to reaching economic independence. The finding that participants characterized with higher levels of income were more likely to depend less on parental support later is quite understandable, showing that the decrease of the need of parental financial assistance becomes smaller when other sources of income become available. However, contrary to income effects, satisfaction with the current financial situation predicted an increased reliance on parent financial support. This effect was also present for financial strain, with higher financial strain predicting a decrease in financial dependence. That is, our findings suggest that during the early years of EA, emerging adults who are more satisfied with their financial situation (after accounting for income levels) seem comfortable in remaining dependent on their parents’ financial support. The less satisfied, instead, tend to seek additional sources of income and consequently reduce reliance from parental assistance even more. More so, we found that those who maintain support tend to feel more satisfied with their financial situation than those who are less dependent. Notably, parental SES also contributed to effects of later decrease in economic dependence via higher initial income levels and to increases in dependence via higher initial financial satisfaction. As such, on the one hand, our findings support suggestions (e.g., Silva, 2016) that some EA experiences may be tied to financial privilege (also see Terriquez & Gurantz, 2015), as prolonged parental economic dependence seems to be an option for those whose parents have the means to ensure financial security. On the other hand, our results show that for some, higher parental SES may also actually help reduce the necessity of financial assistance from parents by creating more opportunities to get a better paid job. As such, at least in the Lithuanian context, parental SES effects on attainment of economic independence are complex.

Study results indicated that SES was positively associated with financial well-being and supported our Hypothesis 1. Contrary to our Hypothesis 2, parental SES was not substantially associated with the emerging adult’s financial behaviors in the present study. This is in contrast with previous studies, which indicated an association between higher parental SES and offspring’s financial capabilities (Lee & Mortimer, 2009, Luhr, 2018; Serido et al., 2010; Xiao et al., 2011). A few of the effects we found suggested the opposite. That is, emerging adults coming from higher SES families tended to exercise less control in spending decisions (initially) and less planning (in the longer term). As such, these results support speculations made in previous studies (Serido et al., 2010), suggesting that young people coming from higher income families can rely more on parental financial assistance and consequently may not feel the need to manage their finances. This might impair the earlier development of financial skills. On the other hand, these findings also suggest that SES effects may vary for different financial capabilities, and as far as short-term money management is considered, parental SES may not have such a big direct effect. It may also be that parental SES relates more indirectly to financial capabilities, via financial socialization processes, but these were not investigated in this study.

As for the reciprocal associations between financial behaviors, our results supported Hypothesis 6. Results also support the need to consider a multidimensional structure of positive financial behaviors (Xiao, 2015), as we included different behaviors of which only some contributed to an increase in financial well-being. In general, our results indicate links between positive financial behaviors and financial well-being (financial strain), partly supporting Hypothesis 3, the predictions of the FFS model (Gudmunson & Danes, 2011), and the findings from earlier longitudinal studies on these issues (e.g., Serido et al., 2013). However, our results also highlight the multidimensionality of financial behaviors (Xiao, 2015) and the need to consider the effects of separate financial behaviors for financial well-being as not every behavior contributed directly to well-being. Results also show that studying these behaviors from a self-regulation perspective (Faber & Vohs, 2011; Vohs et al., 2008) can be useful.

Most importantly, our results indicated the presence of reciprocal longitudinal associations between perceived EA features and indicators of functioning in the financial domain, also providing at least partial support for our Hypothesis 4, but not Hypothesis 5. Although identity exploration as a central EA feature was not longitudinally related to other financial variables, the other four EA features were. Notably, higher parental SES was not directly associated with initial levels of the perceived EA features and had only one link with an increase of self-focus at later time point, supporting Arnett’s (2016a) views that EA features are not strongly associated with parental SES. On the other hand, parental SES was linked to financial well-being aspects, which contributed to the change of perceptions of EA features. This suggests that in the Lithuanian context, characterized by low income levels among emerging adults, parental SES is linked to EA features via financial well-being.

First, we found notable zero-order and change score associations between instability perceptions and subjective financial well-being as well as financial strain, suggesting that as financial well-being decreases, perceptions of instability increase. Thus, the more negative experiences of EA are related to emerging adults’ financial situation. If this situation worsens, negative views about EA increase. Interestingly, the subsequent change of financial well-being may also be driven by the level of perceived instability. However, it is unclear whether this is due to a direct effect of instability perceptions or perhaps the effect of substantial changes in emerging adults’ life that generate these perceptions. It may very well be that this effect is generated by sudden life changes (e.g., leaving the parental home) immediately triggering instability perceptions, which in the longer term create a higher demand for financial resources.

Other notable positive effects were found for self-focus. In particular, higher self-focus predicted subsequent increases in financial planning and controlled spending, supporting the positive contribution of this feature for emerging adults’ daily life skills (Arnett, 2014). However, results also showed that self-focus was associated with higher levels of satisfaction with the financial situation, suggesting that the positive effects attributed to this feature may primarily apply to those emerging adults who feel more secure with their financial situation (Silva, 2016).

Even more notable effects were found for “in-between” feelings. In particular, correlations suggested that financial dependence is indeed linked to feeling more “in-between,” replicating results of previous studies (Benson & Furstenberg, 2006; Shanahan et al., 2005). However, longitudinal associations suggested that the objective financial situation, which is associated with lower parental SES, may matter more than economic dependence for “in-between” perceptions. Most importantly, the uncertainty related to achieved adult status was not only predicted by income but also contributed to poorer management of personal finances and higher levels of financial strain. This supports previous findings on the negative effects of this feature (Baggio et al., 2017; Lanctot & Poulin, 2018; Smith et al., 2014).

For the fifth feature of EA, we found some unexpected results. Higher income levels negatively predicted perceptions of open life possibilities, while higher levels of financial strain positively predicted these. Although these results seemed somewhat contrary to the claims that those with higher income levels have more options to steer their life in the desired direction (Silva, 2016), they became more understandable when we reexamined the items of this scale. Specifically, this scale may target the search for new possibilities and life directions more than it assesses perceptions of existing ones. Consequently, these results suggest that in the early years of EA, those with higher levels of income and lower levels of financial strain tend to engage less in the search of new life directions and possibilities, while those with higher financial well-being tend to engage in this search more. However, it is also very interesting that this search for new possibilities also predicted a subsequent increase in income and healthy financial behaviors. This suggests that a search for new life directions can be driven by lower financial well-being but can also lead to improvement of functioning in this domain. As such, these results suggest that the EA years indeed may have the potential for life changes and potential for improving one’s financial situation.

Limitations and Future Directions

The findings of the study should be considered in light of its limitations. The first and most evident limitation is that our sample was nonrandomly selected and did not involve emerging adults who were both unemployed and not in education. Although this certainly limits the generalizability of the study’s results, we still consider that these results may be generalizable to a large part of young Lithuanian emerging adults. In particular, getting enrolled into at least some higher education is still very popular, even though the percentage of those enrolled has decreased during the last 5 years. In 2014, the percentage of those entering higher education right after finishing high school was 77.3%, and in 2017, it was 70% (Statistics Lithuania, 2019; noteworthy is that this number does not include those studying abroad and conscripted to Lithuanian Armed Forces). Moreover, the sample included participants coming from families characterized by a broad range of SES. Still, this study missed around a quarter of young Lithuanian emerging adults who were not enrolled in higher education.

This study only addressed changes in financial domain variables and EA features that happened during the first years after high school completion. Therefore, it may very well may be that in more advanced years of, or after finishing higher education, the effects of financial domain variables could be more immediate. For example, the sudden increase of income may reduce the levels of perceived instability rather quickly, while other effects may be more prolonged. As such, further investigation of the effects using more long-term longitudinal designs or using more intensive measurement designs (i.e., measurement bursts) may be good directions for future research. Investigations with older emerging adults (e.g., 25-year-olds) may also lead to somewhat different results, given that they are more likely to no longer attend education.

The third limitation of the study relates to the measure of EA features. In particular, the Inventory of Dimensions of Emerging Adulthood was designed to capture the perceptions about participants’ current life rather than the factors that drive these perceptions. This may affect the interpretation of some effects rather substantially (e.g., how perceptions of instability may increase financial strain). As such, future research could address the underlying dimensions more thoroughly (e.g., by addressing the instability in the three general life domains, where most of the instability in this stage occurs: residential, work, and romantic partnership). More objective measures of income (e.g., by asking exactly how much they earn or receive from support) could also clarify these effects.

Conclusions

Despite these limitations, our findings highlight the importance of functioning in the financial domain and the effects it may have on how one experiences EA. Specifically, our findings indicate that financial well-being promotes more desirable perceptions of EA features, such as increased self-focus and decreased “in-between” feelings. Financial difficulties tend to be related to more negative perceptions such as instability. Our findings also show that EA is a time for opportunities for changing one’s current financial situation, suggesting that perceptions of EA features contribute to how one functions in financial life. Specifically, more positive features, such as self-focus and perceived possibilities, tend to improve functioning in the financial domain, while negative ones, such as instability or feeling “in-between” tend to worsen it. Finally, findings also show that parental SES does play at least some role in these associations. Our findings provide important insights on how financial functioning and perceived EA features are associated in the Lithuanian context, thereby hopefully inspiring more research into this relatively understudied and likely partially context-dependent matter.

Supplemental Material

Supplemental Material, ESM_EMERGING_ADULTHOOD_AND_FINANCIAL_LIFE - How Does Financial Life Shape Emerging Adulthood? Short-Term Longitudinal Associations Between Perceived Features of Emerging Adulthood, Financial Behaviors, and Financial Well-Being

Supplemental Material, ESM_EMERGING_ADULTHOOD_AND_FINANCIAL_LIFE for How Does Financial Life Shape Emerging Adulthood? Short-Term Longitudinal Associations Between Perceived Features of Emerging Adulthood, Financial Behaviors, and Financial Well-Being by Rimantas Vosylis and Theo Klimstra in Emerging Adulthood

Financial Life Variables	Income	Economic Dependency	Financial Strain	Financial Satisfaction	Financial Planning	Cash-Flow Monitoring	Saving	Controlled Spending
Identity explorations	T1	−.04	−.03	−.04	.09	.08	.04	.11	.09	.03	.00	−.01	.02	.18	.12	.15	.04	.04	.06	.02	.05	.05	.08	.09	.10
Identity explorations	T2	−.03	.02	−.02	.01	.01	.02	.08	.10	.07	−.08	−.08	−.05	.15	.15	.15	.14	.07	.10	.05	.08	.01	.03	.11	.07
Identity explorations	T3	−.07	−.05	−.10	.04	.03	.08	−.02	.06	.07	−.03	−.07	−.06	.02	.04	.07	.05	−.02	.00	.04	.00	.03	.05	.08	.10
Instability	T1	−.07	−.06	−.05	.06	.08	.03	.19	.21	.19	−.29	−.25	−.23	.12	.04	.10	.00	−.01	−.03	−.02	−.01	−.07	.00	.02	.02
Instability	T2	−.05	−.02	−.07	−.01	−.02	−.03	.12	.22	.19	−.19	−.31	−.21	.07	.07	.07	.02	−.05	−.03	−.01	−.02	−.07	.00	.00	−.04
Instability	T3	−.06	−.03	−.04	.00	−.03	.02	.13	.16	.22	−.25	−.29	−.33	.06	−.02	.06	−.01	−.17	−.06	−.02	−.10	−.09	−.01	−.02	−.05
Self-focus	T1	.06	−.01	.04	.04	.03	.03	.05	.07	.03	.01	.01	−.01	.10	.08	.08	.05	.08	.07	.05	.02	.05	.11	.11	.17
Self-focus	T2	−.01	.03	.04	.00	.01	.03	−.01	−.03	.03	.06	.05	.04	.08	.07	.09	.09	.07	.01	.01	.06	.01	.04	.13	.06
Self-focus	T3	.00	.02	.07	.08	.08	.00	.05	.04	.03	.02	.01	.06	.03	.11	.13	.01	.04	.05	−.03	−.03	.08	.09	.19	.17
Feeling in-between	T1	−.10	−.10	−.07	.17	.12	.11	.08	.12	.07	−.04	−.02	.03	.07	.00	.00	.01	−.07	−.01	−.04	−.13	−.04	.02	−.06	.00
Feeling in-between	T2	−.19	−.11	−.13	.14	.15	.11	.06	.07	.10	−.05	−.07	−.05	.11	.10	.01	.01	.04	−.05	−.04	−.02	−.09	.00	.10	−.03
Feeling in-between	T3	−.14	−.13	−.10	.13	.09	.07	.07	.09	.08	−.07	−.08	−.02	.01	.00	.02	−.01	−.09	−.03	−.09	−.14	−.01	.05	.02	.05
Perceived possibilities	T1	.10	.09	.15	.03	−.02	−.05	−.05	−.03	−.10	.12	.14	.14	.13	.14	.10	.06	.06	.07	.06	.10	.05	.08	.12	.11
Perceived possibilities	T2	.02	.04	.14	−.03	−.05	−.05	.03	−.02	.03	.04	.04	.09	.05	.10	.06	.07	.04	.11	.03	.08	.08	.01	.12	.08
Perceived possibilities	T3	.03	.02	.07	.04	−.02	−.06	−.01	.00	−.04	.08	.04	.15	.08	.07	.14	.04	.00	.11	−.02	−.02	.11	.12	.15	.19

Model Tested (Model Compared With)	Model Fit Statistics	Model Comparison
Longitudinal measurement invariance analysis for features of emerging adulthood (IDEA measure)
Configural	1,429.50	795	285	<.001	.934	.039 [.035, .042]	.055	—	—	—	—
Weak (vs. configural)	1,464.69	815	265	<.001	.932	.039 [.035, .042]	.058	35.23	20	.019	−.002	.000
Strong (vs. weak)	1,514.33	835	245	<.001	.929	.039 [.036, .042]	.059	49.59	20	<.001	−.003	.000
Strict (vs. strong)	1,535.02	865	215	<.001	.930	.038 [.035, .041]	.065	36.80	30	.182	.001	−.001
Longitudinal measurement invariance analysis for positive financial behaviors and financial satisfaction
Configural	1,794.84	1,068	309	<.001	.952	.036 [.033, .039]	.046	—	—	—	—
Weak (vs. configural)	1,819.62	1,092	285	<.001	.952	.035 [.032, .038]	.047	22.65	24	.541	.000	−.001
Strong (vs. weak)	1,886.42	1,116	261	<.001	.949	.036 [.033, .039]	.048	70.15	24	<.001	−.003	.001
Strict (vs. strong)	1,997.95	1,150	227	<.001	.944	.037 [.034, .040]	.050	97.77	34	<.001	−.005	.001
LDS model linking financial well-being, financial behaviors, and features of emerging adulthood
Measurement model	7,643.58	4,777	1,000	<.001	.907	.034 [.032, .035]	.051	—	—	—	—	—
Model A	7,998.64	4,946	829	<.001	.901	.034 [.033, .035]	.054	380.03	169	<.001	−.006	.000
Model B	8,257.92	5,115	660	<.001	.898	.034 [.034, .035]	.057	259.23	169	<.001	−.003	.000
Model C	8,331.46	5,193	582	<.001	.898	.034 [.034, .035]	.058	77.29	78	.501	.000	.000

Aspects of financial well-being and financial behaviors, and perceived features of emerging adulthood	1	2	3	4	5	6	7	8
1. Income (change)
2. Economic dependency (change)	−.33*
3. Financial strain (change)	−.10*	.07*
4. Financial satisfaction (change)	.10*	−.06*	−.32*
5. Financial planning (change)	.02	−.04	.02	.07*
6. Cash-flow monitoring (change)	.11*	.00	−.06*	.15*	.66*
7. Saving (change)	.08*	−.11*	−.11*	.17*	.36*	.59*
8. Controlled spending (change)	−.02	.06	−.11*	.21*	.41*	.47*	.29*
9. Identity explorations (change)	−.04	.08	.03	−.05	.06	−.01	.05	.08
10. Instability (change)	.02	.03	.12*	−.23*	.09	.02	.01	.00
11. Self-focus (change)	.05	−.01	−.05	.03	.08	−.01	.12*	.13*
12. Feeling in-between (change)	.00	.03	−.03	.03	.17*	.10*	.18*	.19*
13. Possibilities (change)	.00	−.03	−.05	.08	.08	−.01	.09	.10*

Footnotes

Author Contributions

Rimantas Vosylis contributed to conception, design, acquisition, analysis, and interpretation; drafted the manuscript; critically revised the manuscript; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy. Theo Klimstra contributed to conception, analysis, and interpretation; drafted the manuscript; critically revised the manuscript; gave final approval; and agreed to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the European Social Fund under the No. 09.3.3-LMT-K-712 “Development of Competences of Scientists, Other Researchers, and Students through Practical Research Activities” measure.

Open Practices

All data have been made publicly available via the Open Science Framework and can be accessed at https://bit.ly/361PQpA. The design and analysis plans for the experiments were not preregistered. The complete Open Practices Disclosure for this article can be found at . This article has received the badges for Open Data.

ORCID iD

Rimantas Vosylis

Supplemental Material

Supplemental material for this article is available online.

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Aspects of financial well-being and financial behavior	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24
1. Income (T1)
2. Income (T2)	.58
3. Income (T3)	.53	.63
4. Economic dependency (T1)	−.30	−.18	−.19
5. Economic dependency (T2)	−.23	−.36	−.30	.61
6. Economic dependency (T3)	−.18	−.28	−.45	.55	.74
7. Financial strain (T1)	.02	.06	.10	.02	−.07	−.12
8. Financial strain (T2)	.02	−.05	−.03	−.01	−.04	−.10	.50
9. Financial strain (T3)	−.06	−.02	−.07	−.01	−.08	−.03	.38	.50
10. Financial satisfaction (T1)	.16	.07	.05	.11	.19	.20	−.49	−.39	−.34
11. Financial satisfaction (T2)	.07	.09	.05	.20	.22	.21	−.33	−.49	−.35	.66
12. Financial satisfaction (T3)	.09	.09	.10	.15	.18	.11	−.28	−.40	−.44	.62	.74
13. Financial planning (T1)	−.03	.01	.05	−.04	−.02	−.08	.06	.04	.01	−.05	−.03	−.05
14. Financial planning (T2)	.08	.11	.13	−.06	−.08	−.12	.05	.04	.03	−.05	−.02	−.01	.53
15. Financial planning (T3)	.10	.12	.11	−.04	−.07	−.12	.08	.06	.04	−.09	−.08	−.01	.50	.58
16. Cash-flow monitoring (T1)	.03	.01	.05	−.07	−.06	−.04	−.10	−.04	−.01	.10	.05	.04	.59	.39	.35
17. Cash-flow monitoring (T2)	.01	.10	.07	−.07	−.06	−.10	−.20	−.19	−.18	.08	.16	.14	.38	.62	.37	.41
18. Cash-flow monitoring (T3)	.10	.10	.14	−.04	−.02	−.07	.06	.01	−.04	−.01	.02	.10	.41	.50	.58	.44	.45
19. Saving (T1)	.10	.01	.02	−.16	−.06	−.05	−.20	−.14	−.16	.10	.05	.03	.34	.30	.22	.43	.46	.31
20. Saving (T2)	.06	.09	.07	−.14	−.14	−.16	−.14	−.18	−.19	.05	.12	.09	.33	.47	.26	.32	.75	.38	.58
21. Saving (T3)	.08	.10	.14	−.07	−.09	−.17	−.07	−.09	−.19	.09	.16	.19	.26	.32	.37	.23	.46	.52	.40	.56
22. Controlled spending (T1)	−.13	−.13	−.10	.04	.03	−.02	−.17	−.12	−.13	.09	.11	.09	.46	.27	.27	.49	.31	.33	.32	.22	.19
23. Controlled spending (T2)	−.11	−.05	−.05	.05	.09	.07	−.19	−.24	−.18	.07	.19	.13	.30	.42	.33	.31	.49	.30	.24	.36	.22	.50
24. Controlled spending (T3)	−.02	−.01	−.04	.00	.06	.04	−.04	−.15	−.13	.02	.09	.16	.26	.34	.50	.23	.34	.41	.15	.18	.27	.36	.57
Mean	2.88	3.44	2.32	2.90	3.33	1.91	3.10	3.14	1.76	3.43	3.39	3.42	3.28	3.36	3.33	2.79	3.35	3.04	3.11	3.25	3.20	3.80	3.91	3.89
SD	1.54	1.49	1.45	1.45	1.50	1.17	1.41	1.59	1.02	0.98	0.93	0.92	0.93	0.91	0.94	0.94	0.97	0.93	1.15	1.16	1.15	.73	.69	.69

Financial Life Variables		Income			Economic Dependency			Financial Strain			Financial Satisfaction			Financial Planning			Cash-Flow Monitoring			Saving			Controlled Spending
Features of EA	Time	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3	T1	T2	T3
Identity explorations	T1	−.04	−.03	−.04	.09	.08	.04	.11	.09	.03	.00	−.01	.02	.18	.12	.15	.04	.04	.06	.02	.05	.05	.08	.09	.10
Identity explorations	T2	−.03	.02	−.02	.01	.01	.02	.08	.10	.07	−.08	−.08	−.05	.15	.15	.15	.14	.07	.10	.05	.08	.01	.03	.11	.07
Identity explorations	T3	−.07	−.05	−.10	.04	.03	.08	−.02	.06	.07	−.03	−.07	−.06	.02	.04	.07	.05	−.02	.00	.04	.00	.03	.05	.08	.10
Instability	T1	−.07	−.06	−.05	.06	.08	.03	.19	.21	.19	−.29	−.25	−.23	.12	.04	.10	.00	−.01	−.03	−.02	−.01	−.07	.00	.02	.02
Instability	T2	−.05	−.02	−.07	−.01	−.02	−.03	.12	.22	.19	−.19	−.31	−.21	.07	.07	.07	.02	−.05	−.03	−.01	−.02	−.07	.00	.00	−.04
Instability	T3	−.06	−.03	−.04	.00	−.03	.02	.13	.16	.22	−.25	−.29	−.33	.06	−.02	.06	−.01	−.17	−.06	−.02	−.10	−.09	−.01	−.02	−.05
Self-focus	T1	.06	−.01	.04	.04	.03	.03	.05	.07	.03	.01	.01	−.01	.10	.08	.08	.05	.08	.07	.05	.02	.05	.11	.11	.17
Self-focus	T2	−.01	.03	.04	.00	.01	.03	−.01	−.03	.03	.06	.05	.04	.08	.07	.09	.09	.07	.01	.01	.06	.01	.04	.13	.06
Self-focus	T3	.00	.02	.07	.08	.08	.00	.05	.04	.03	.02	.01	.06	.03	.11	.13	.01	.04	.05	−.03	−.03	.08	.09	.19	.17
Feeling in-between	T1	−.10	−.10	−.07	.17	.12	.11	.08	.12	.07	−.04	−.02	.03	.07	.00	.00	.01	−.07	−.01	−.04	−.13	−.04	.02	−.06	.00
Feeling in-between	T2	−.19	−.11	−.13	.14	.15	.11	.06	.07	.10	−.05	−.07	−.05	.11	.10	.01	.01	.04	−.05	−.04	−.02	−.09	.00	.10	−.03
Feeling in-between	T3	−.14	−.13	−.10	.13	.09	.07	.07	.09	.08	−.07	−.08	−.02	.01	.00	.02	−.01	−.09	−.03	−.09	−.14	−.01	.05	.02	.05
Perceived possibilities	T1	.10	.09	.15	.03	−.02	−.05	−.05	−.03	−.10	.12	.14	.14	.13	.14	.10	.06	.06	.07	.06	.10	.05	.08	.12	.11
Perceived possibilities	T2	.02	.04	.14	−.03	−.05	−.05	.03	−.02	.03	.04	.04	.09	.05	.10	.06	.07	.04	.11	.03	.08	.08	.01	.12	.08
Perceived possibilities	T3	.03	.02	.07	.04	−.02	−.06	−.01	.00	−.04	.08	.04	.15	.08	.07	.14	.04	.00	.11	−.02	−.02	.11	.12	.15	.19

Model Tested (Model Compared With)	Model Fit Statistics							Model Comparison
Model Tested (Model Compared With)	χ²	df	n _par	p	CFI	RMSEA [90% CI]	SRMR	Δχ²	Δdf	p	ΔCFI	ΔRMSEA
Longitudinal measurement invariance analysis for features of emerging adulthood (IDEA measure)
Configural	1,429.50	795	285	<.001	.934	.039 [.035, .042]	.055	—	—	—	—
Weak (vs. configural)	1,464.69	815	265	<.001	.932	.039 [.035, .042]	.058	35.23	20	.019	−.002	.000
Strong (vs. weak)	1,514.33	835	245	<.001	.929	.039 [.036, .042]	.059	49.59	20	<.001	−.003	.000
Strict (vs. strong)	1,535.02	865	215	<.001	.930	.038 [.035, .041]	.065	36.80	30	.182	.001	−.001
Longitudinal measurement invariance analysis for positive financial behaviors and financial satisfaction
Configural	1,794.84	1,068	309	<.001	.952	.036 [.033, .039]	.046	—	—	—	—
Weak (vs. configural)	1,819.62	1,092	285	<.001	.952	.035 [.032, .038]	.047	22.65	24	.541	.000	−.001
Strong (vs. weak)	1,886.42	1,116	261	<.001	.949	.036 [.033, .039]	.048	70.15	24	<.001	−.003	.001
Strict (vs. strong)	1,997.95	1,150	227	<.001	.944	.037 [.034, .040]	.050	97.77	34	<.001	−.005	.001
LDS model linking financial well-being, financial behaviors, and features of emerging adulthood
Measurement model	7,643.58	4,777	1,000	<.001	.907	.034 [.032, .035]	.051	—	—	—	—	—
Model A	7,998.64	4,946	829	<.001	.901	.034 [.033, .035]	.054	380.03	169	<.001	−.006	.000
Model B	8,257.92	5,115	660	<.001	.898	.034 [.034, .035]	.057	259.23	169	<.001	−.003	.000
Model C	8,331.46	5,193	582	<.001	.898	.034 [.034, .035]	.058	77.29	78	.501	.000	.000