Determinants of financial worry and rumination
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debruijn2020 - p. 3
Financial rumination and worry, the key concepts of interest of this paper, are derivatives of the broader constructs of “rumination” and “worry.”
debruijn2020 - p. 3
we define financial rumination as repetitive, passive, and pessimistic thinking about the possible causes and consequences of one’s financial concerns.
debruijn2020 - p. 3
We define financial worry as repeated and negative thinking about the uncertainty of one’s (future) financial situation.
debruijn2020 - p. 3
hile both rumination and worry involve repetitive thinking patterns accompanied by negative emotions, the characteristics of these thinking patterns and the type of emotions differ. The thinking styles (or cognitions) differ conceptually in terms of orientation (rumination involves focusing on the past and present, worry focuses on the present and future), central theme (dealing with a loss vs. a threat), conscious motive (understanding the meaning of events vs. anticipation of a threat), and unconscious motive (avoiding aversive situations vs. negative affect) (Nolen-Hoeksema et al., 2008).
debruijn2020 - p. 3
Making ends meet can be defined as having enough income to meet expenses and pay all bills in a particular period Financial buffer refers to liquid assets or buffer stock savings that can directly be used for consumption. The magnitude of one’s financial buffer provides information about the susceptibility of a household’s finances to financial shocks Perceived debt refers to an individual’s perception regarding one’s overall debt position.
debruijn2020 - p. 5
Financial rumination. We used the 12-item financial rumination scale developed by Johar et al. (2015) and separately presented the items for emotion-related and cognition-related items in the questionnaire (see Table 1).
debruijn2020 - p. 5
inancial worry. As far as we know, a financial worry scale distinguishing between worry-related cognitions and worry-related emotions does not exist.2 To fill this gap, we constructed an 8-item financial worry scale in a similar style as the financial rumination
debruijn2020 - p. 5
1040
Note: samplesize
debruijn2020 - p. 6
All financial rumination and financial worry items were answered on a 5-point Likert-type scale ranging from 1 (completely disagree) to 5 (completely agree). The items were presented to the respondents within four blocks and were randomly ordered within these blocks
debruijn2020 - p. 6
ast and future changes in one’s financial situation. We used single items from the Michigan Index of Consumer Sentiment (ICS) to measure both past and expected changes in the household’s financial situation. For changes in the past, we used the item “The financial situation of my household has in the last 12 months … (1 = clearly worsened and 5 = clearly improved)” and for future changes “I expect that my household’s financial situation will … (1 = clearly worsen and 5 = clearly improve) in the next 12 months.”
debruijn2020 - p. 6
ncome. Net income was measured using the following question: “At the moment, what is the net monthly income of your household?” In answering this question, respondents could choose between five income categories in euros (≤1000, 1001–1350, 1350–1800, 1800–2150, and ≥2151)
debruijn2020 - p. 6
Financial buffer. Household financial buffer was measured using a slightly adapted single item proposed by the OECD (2015) and widely used in financial literacy surveys: “If you lost your main source of household income, how long could your household continue to cover living expenses, without borrowing any money or moving house?”
debruijn2020 - p. 6
Making ends meet. People’s ability to make ends meet was measured using an item derived from the EU-SILC questionnaire (Eurostat, 2014): “In an average month, how easy or difficult is it for you to make ends meet and pay all your bills?” Respondents could rate their ability to make ends meet on a 5-point scale ranging from 1 (very difficult) to 5 (very easy)
debruijn2020 - p. 6
Perceived debts. We measured participants’ perceptions of their debt position using a single item proposed by Lusardi et al. (2018): “To what extent do you agree or disagree with the next statement? I have too much debt right now.” 5-point Likert-type scale ranging from 1 (completely disagree) to 5 (completely agree).
debruijn2020 - p. 7
We used two-step Structural Equation Modeling (SEM) to analyze the data. In step 1, we conducted a Confirmatory Factor Analysis (CFA) to assess the measurement-model fit of financial worry and rumination.
debruijn2020 - p. 7
Mardia’s multivariate kurtosis and skewness tests (Mardia, 1970) were significant (p < .001). Because the observed variables did not appear to be multivariate-normally distributed, these results indicated that the latent variables (factors) were not normally distributed. Therefore, we decided to use Maximum Likelihood estimation with Satorra-Bentler adjusted standard error estimation (ML-SB). This method provides nonnormality-adjusted standard errors and corrected Goodness-of-Fit statistics (GoFs) (Satorra & Bentler, 1994), is appropriate for similar datasets (Finney & DiStefano, 2006), and performs better than alternative methods for sample sizes comparable to ours
Note: Green flag
debruijn2020 - p. 7
we investigated the item reliability to assess whether we should drop particular items. Following the guidelines of Pituch and Stevens (2016), we used a standardized loading of .4 as a threshold. We found one item (item 11 in Table 1) with a loading below this threshold in all models and dropped this item from further analyses. Second, we compared the GoFs of the models using a Satorra-Bentler scaled Chi-square difference test (Satorra & Bentler, 2010) indicating that the 4-factor model had a significantly better fit than all other models (see Table 2)
debruijn2020 - p. 7
However, we found no support for discriminant validity of the 4-factor model. The factors of the 4-factor model correlated strongly (all between .908 and .996) implying that the four factors did not differ enough from each other to interpret them as separate constructs, thus rejecting Hypothesis 1
debruijn2020 - p. 7
We decided to examine this solution post-hoc. To obtain a proper estimate of g, two methods (bifactor and higher-order factor model) can be used (Murray & Johnson, 2013)
debruijn2020 - p. 7
Even while the included restriction might backfire AIC and BIC, the results yielded better AIC and BIC than the 4-factor model suggesting that the restricted bi-factor model performed best.
debruijn2020 - p. 8
he results indicated good reliability for the g factor ( ’ = .928) and poor reliability for the group factors ( ’* ranged from .002 to .142). These results could be explained by the fact that the group factors were residual factors and that the items loaded higher on the general factor than on the group factors
debruijn2020 - p. 9
(1) FWR-data violated the normal-distribution assumption (as noted before) and (2) some of the financial variables contained missing values (see Table 5). To solve these problems, we decided to use two methods for estimating the structural model. As primary estimation method, we chose ML-SB (same as for the measurement model) with the key advantage that it allowed to assess and compare models using several GoFs. However, ML-SB may result in biased estimates of our effect sizes, because it uses listwise deletion of missing values (Hair et al., 2014). To solve this problem, we used Full Information Maximum Likelihood including the “robust” technique for estimation of standard errors (FIMLrobust) as a robustness check for the effect estimates.
debruijn2020 - p. 9
Because we performed tests for several model relationships, we controlled for multiplicity to prevent inflated familywise Type I error rates. To this end, we used an adjusted Bonferroni procedure proposed by Smith and Cribbie (2013).8 This procedure is specifically designed for SEM-analyses and incorporates both the number of parameters estimated and the degree of correlations between parameters.
debruijn2020 - p. 9
Model 1 fitted the data acceptably (see Table 6 for the number of observations included and for the results); all GoFs met their thresholds, except for TLI (SB-correction = 1.09, (24) 2 = 90.8, p < .001; RMSEA = .070, CFI = .924, TLI = .864, SRMR = .042).
debruijn2020 - p. 9
Regarding the relationships between the mediators and FWR, perceived debt was significantly positively related to FWR ( = .188, 1a p < .001). Both financial buffer and making ends meet were negatively associated with FWR (Financial buffer: = .160, 1a p < .001; Making ends meet: = .466, 1a p < .001).
debruijn2020 - p. 11
Using modification indices, we found that we could improve the model fit by adding a path from past changes in financial situation toward making ends meet (MI = 52.5)
debruijn2020 - p. 11
A recent deterioration in one’s financial situation (e.g., a drop in income) may make it more difficult to make ends meet because it takes time to adapt income or expenditures to the new financial situation.
debruijn2020 - p. 14
ia making ends meet −.116*** −.105*** −.114***
debruijn2020 - p. 15
lower income was associated with higher FWR-scores implying that people worry and ruminate more about their finances, the lower their income is