Data and variables

We used data from the Longitudinal Internet Studies on Social Sciences (LISS) panel (initial N = 1114). The LISS panel consists of a representative sample of approximately 5,000 households drawn by the Central Bureau of Statistics of the Netherlands⁵⁸. Respondents fill in monthly questionnaires on various topics, such as health, family, work, personality, and economic situation. To ensure that vulnerable households can participate, they are supplied with a laptop and internet connection if necessary. The rich dataset enabled us to examine the relationship between developments in households’ economic situation, financial stress, and mental health. We used three measurements to compare the situations before and during COVID-19: April – November 2018 (t = 0), December 2019 – March 2020 (t = 1), and December 2020 – March 2021 (t = 2).

The methods were performed in accordance with relevant guidelines and regulations and approved by CentERData. The current study used secondary data provided by CentERData. Informed consent was obtained from all participants by CentERData. Before participating in the LISS panel, participants must consent to CentERData to save their responses and make them available for scientific, policy, and social research.

The literature suggests that the most prevalent mental health problems related to COVID-19 are anxiety and mood disorders. To assess mental health, we, therefore, used the Mental Health Index (MHI-5), a brief and reliable measure of mental health with good validity for anxiety and mood disorders⁵⁹, and a subset of the validated SF-36 Health Survey⁶⁰ (Cronbach’s α = 0.87). MHI-5 asks respondents how often they felt nervous, down, calm, depressed, and happy in recent weeks. Respondents’ scores on each item ranged from 1 (never) to 6 (continuously). We recoded the items so that a higher score reflected better mental health. LISS’ health questionnaire measures MHI-5 every year. We used the measurements administered in November/December 2018, 2019, and 2020.

We used the Psychological Inventory of Financial Scarcity (PIFS) (Cronbach’s α = 0.93) to measure financial stress^48,49. The PIFS assesses the subjective experience of financial stress and captures appraisals of insufficient financial resources and lack of control over one’s financial situation, responses regarding financial rumination and worry, and a short-term focus. Respondents’ scores on each item range from 1 (totally disagree) to 7 (totally agree). Higher scores indicate more financial stress. The PIFS was administered in April 2018, February 2020, and August 2020.

We included four aspects of a household’s economic situation in the analyses: income, income volatility, savings, and debts. We used monthly income data for 2018, 2019, and 2020. For savings and debts, we used the last available measurement before the outbreak of COVID-19. This measurement was held in June/July 2019 and concerned households’ financial situation at the end of 2018.

The LISS panel measures net monthly household income in euros. We summed the net monthly household incomes for 2018, 2019, and 2020 to obtain yearly net household incomes. Since the needs of a household grow with each additional member, we corrected for household size. To consider economies of scale, we adjusted household income by dividing it by the square root of household size, in line with OECD guidance⁶¹. We included income at the first measurement and income changes between the three measurements as independent variables in our model.

Savings may serve as buffers against unexpected expenditures and income shocks. Ruberton et al. stressed the importance of liquid wealth for wellbeing⁵⁶. We, therefore, included the amount of household liquid savings in our analyses. Respondents were asked: “What was the total balance of your banking account, savings accounts, term deposit accounts, savings bonds or savings certificates, and bank savings schemes on 31 December 2018?”. If they responded, “I don’t know,” the questionnaire asked, “To what category did the total balance (total value) belong on 31 December 2018 (positive or negative)?” and given 15 categories (less than € 50 to € 25,000 or more). We used the category midpoints to calculate savings.

To calculate debt amounts, we excluded mortgages and student loans from our analyses and focused on consumer credit. We argue that, for most households, having a mortgage contributes less to financial stress than other types of debt since a mortgage is not a sign of financial difficulties in most situations. Also, the home’s value usually amply compensates for the mortgage loan’s value. Student loans in the Netherlands have favorable conditions and are waivered if one has difficulties repaying them and should, therefore, also contribute less to financial stress. The survey asked respondents to indicate whether they had (a) one or more personal loans, revolving credit arrangement(s), or financing credit(s) based on a hire-purchase or installment plan, (b) a loan or credit arrangement based on a pledge, (c) overdue payments on one or more credit cards (d) money loaned from family, friends, or acquaintances, and (e) any other credits, loans or debts. Respondents indicating that they held one or more of these debts were then asked: “What was the total amount of the loans, credits, and debts that you had on 31 December 2017? This concerns the total of all the components you check-marked in the previous question.” If they responded, “I don’t know,” the questionnaire asked, “To what category did the total balance (total value) belong on 31 December 2018 (positive or negative)?” and given 14 categories (less than € 500 to € 100,000 or more). We used the category midpoints to calculate debt amounts.

We used age, education level, household composition, and personality traits as control variables in our analyses. Age and education level may confound the association between income and financial stress. Furthermore, research has shown that mental health during COVID-19 may differ between households with different compositions^12,19,20,35. We distinguished four household types: (1) no partner, no children, (2) children, no partner, (3) partner, no children, and (4) partner with children.

We considered the Big-Five personality traits (extraversion, agreeableness, openness, conscientiousness, and emotional stability)⁶² as potential confounders of the relationship between mental health and one or more independent variables. Several studies have indicated that personality traits influence saving behavior, impulse buying, debts, and financial stress. The literature provides the most support for extraversion, conscientiousness, and emotional stability as potential covariates. For example, conscientiousness is positively associated with savings and negatively with debts⁶³ and financial stress. Extraversion negatively predicts debts⁶⁴. Emotional stability shows a negative association with financial stress⁴⁸. We, therefore, included subscales for emotional stability, conscientiousness, and extraversion (α = 0.77, 0.89, and 0.87, respectively) in our analyses.

We parsed out the variance between six controls (age, education level, household composition, emotional stability, conscientiousness, and extraversion) and the independent variables. This allowed us to examine the unique relationship between economic variables, financial stress, and mental health.