The data for our analyses were collected from April 20 to July 7, 2020 using a customised Qualtrics survey, COVID-19 and YOUR Wellbeing.Footnote 3 In addition to very basic socio-demographic indicators, the survey asked about many outcomes relevant to the crisis, including personal events experienced due to COVID-19, financial wellbeing, subjective wellbeing, and mental health (24 question blocks in total). Respondents could opt out of the survey at any time, and their responses up to that point in time would be recorded. Respondents always had the option to respond “prefer not to say”.
A combination of snowball sampling and targeted advertisement on social media was used to recruit participants. The link to the survey was shared on Twitter and Facebook by the researchers. A Facebook page was created with information on the study and link to the survey. Facebook advertisements were run weekly between April 28 and July 7, 2020. The advertisements were targeted at people living in Australia, USA, UK, Spain, Italy, and Germany, although in this study we restrict our analysis to respondents who stated in the survey that they lived in Australia. 99.6% of the impressions and 99.9% of link clicks of these campaigns were generated via the Facebook newsfeed. The advertisements had an average cost of $0.31 per link click. Approximately 81% of the impressionsFootnote 4 (82% of link clicks) were generated via the mobile app, 9% via the mobile web (12%), and 9% via the desktop (7%). As respondents were not paid to participate in the survey, the survey was kept short to maximise response rates. It took participants on average 8 min (median: 7 min) to complete the survey.
Of those who said that they were Australian residents, in the labour force, and between 18 and 64 years old (2619 observations), we drop 9% who filled out the survey more than onceFootnote 5 in order to create a cross-sectional dataset. Of the remaining 2375 unique observations, 87% had non-missing information in the variables needed for the analysis, leading to a final analysis sample of 2078 Australian residents as of July 7, 2020. The advertisements were not targeted at specific socio-demographic characteristics such as by gender. This led to an over-representation of women in the sample (86% in the sample vs 48% in the corresponding Australian labour force population), as has been documented occurring previously in social media–based advertising campaigns (Ali et al. 2020). People living in Victoria were also over-sampled (46% vs 27%) as well as the older age groups (age 55–64: 24% vs 15%). To make the sample representative of the general Australian population, we apply population weights based on the age, gender, occupation, and state composition of the Australian working population, from the 2016 Census, throughout the analysis. Appendix Table 2 compares the descriptive statistics of the weighted with the unweighted data as well as the descriptives for the total population. Weighting achieves an excellent match to the total population compositions with respect to age, gender, occupation, state, household size, and employment status. The unemployment rate is slightly higher in the weighted sample than the Australian one from the 2016 Census (8% compared to 7%), which is to be expected and in line with the increasing unemployment rates in June 2020 due to the pandemic.
While online surveys are now common in social sciences, some limitations should be considered. Even though we use Census-based population weights, as with any mode of survey delivery such as quota-based telephone surveys, there is still the possibility that people self-select into the survey based on other unobserved characteristics, including concern with the research topic, in our case the pandemic. An additional factor with online surveys is that they miss people who do not have a mobile device or computer with access to the internet, although this issue has become less in recent years with the rapid availability of online technologies. As our advertisement analytics show, the majority of people accessed the survey via the mobile app (82% of link clicks generated through the campaigns). Furthermore, it has been recently shown that “re-weighted online samples can produce response patterns that are indistinguishable, statistically and quantitatively, from those of mixed-mode survey”, and thus be representative of the entire population (Grewenig et al. 2018). Grewenig et al. (2018) showed that this is because differences between offliners and onliners in the mixed-mode survey can be attributed to face-to-face vs online survey mode effects rather than differences in unobserved characteristics.
An advantage of online recruitment through advertisements compared to crowdsourcing service sites such as Mechanical Turk is that advertisements tend to attract people from more diverse backgrounds, as respondents are reached who were not looking to participate in a study (Antoun et al. 2016). Online surveys are also less likely to suffer from social desirability bias than face-to-face sampling methods (Grewenig et al. 2018). Our results have to be interpreted in light of these advantages and disadvantages with each recruitment method and survey mode.
Financial wellbeing has been defined in many ways in previous research. We follow Comerton-Forde et al. (2018:6), who reviewed many different conceptualisations and developed a definition that was appropriate for the Australian context. They define financial wellbeing as “the extent to which people both perceive and have (i) financial outcomes in which they meet their financial obligations, (ii) financial freedom to make choices that allow them to enjoy life, (iii) control of their finances, and (iv) financial security – now, in the future, and under possible adverse circumstances.” Their definition incorporates elements that have been considered in other definitions, including those proposed by the U.S. Consumer Financial Protection Bureau (2017), Muir et al. (2017), and Netemeyer et al. (2018). Working from this definition and based on a set of 33 initial questions, Comerton-Forde et al. (2018) undertook a rigorous psychometric analysis, including factor analyses and Item Response Theory (IRT) modeling, to develop a 10-item scale of self-reported financial wellbeing. Botha et al. (2020b) derived an abbreviated 5-item version of the scale, found that it has high reliability (Cronbach’s alpha was 0.86), and showed that it performs very similarly to the original 10-item scale. The IRT results also demonstrated that each of the responses to each of the 5 items provides significant and unique information to the underlying financial wellbeing construct and that each of the items discriminates nearly equally well. To keep the total survey length to 10 min, the COVID-19 and YOUR Wellbeing Survey used the 5-item scale.
In addition to being rigorously validated, our measure of financial wellbeing is distinct from other related concepts and offers several advantages. Financial wellbeing is positively correlated to income, yet they are distinct constructs. Several studies provide evidence on this, including Bonke and Browning (2009); Brown and Gray (2016), Schmeiser and Seligman (2013), and Shim et al. (2009). When analysing financial wellbeing, Haisken-DeNew et al. (2018) show that people experience a range of financial wellbeing outcomes at all levels of income, with some high-income people experiencing modest financial wellbeing and some low-income people experiencing good financial wellbeing. As an individual outcome measure, financial wellbeing is preferable to income in that it is multi-faceted, capturing several dimensions of individual financial enjoyment such as uncertainty and future-oriented consumption that a simple income measure cannot. It also uses several items across a range of outcomes, averaging out measurement error. Finally, it can be constructed from only a few unintrusive questions that most survey respondents will gladly answer, whereas income item nonresponse in surveys is pervasive and troublesome for statistical analysis (Riphahn and Serfling 2005).
Our multi-item, concrete outcome-based, measure of financial wellbeing is also superior to single-item financial satisfaction measures, where it is unclear what the people answering this one subjective question think of, which makes comparisons across groups very difficult. Bond and Lang (2019) demonstrate the difficulties of identifying differences between group averages based on these single-item ordered questions. Moreover, Comerton-Forde et al. (2018), among others, find that financial satisfaction questions have poor psychometric properties (e.g. show patterns of extreme reporting). Financial wellbeing, and in particular the financial wellbeing measure we use in this paper, addressed specifically many of these shortcomings.
Finally, in contrast to most existing financial wellbeing measures, the financial wellbeing instrument used in this paper has been rigorously tested and validated. See Comerton-Forde et al. (2020) for a thorough review of the existing literature. Moreover, our chosen financial wellbeing measure is explicitly designed to maintain its measurement properties when the 5 items are combined in a summative scale, making this scale more transparent and simpler to implement than other financial wellbeing scales constructed with data-specific item weights such as factor loadings.
Figure 1 lists each of the possible responses for each of the five items in our financial wellbeing scale and shows the proportion of people who selected each response. The items cover current and future dimensions of financial wellbeing. Items 1, 3, and 4 relate to respondents’ immediate day-to-day financial outcomes; item 2 relates to maintaining future financial wellbeing during unexpected events; and item 5 relates to sustaining financial wellbeing over time and reaching long-term financial goals.
Botha et al. (2020b) reported results from factor analyses that showed that all five items load on a single factor. The financial wellbeing scale is obtained by simply summing the five items and multiplying the sum by five to obtain a financial wellbeing score that ranges from 0 (low financial wellbeing) to 100 (high financial wellbeing); this scale has a reliability coefficient of 0.91 in this dataset.Footnote 6 Across all items, a significant portion of people report low financial wellbeing. For example, 16% report that they cannot enjoy life at all or very little because of the way they are managing their money; 29% could not handle a major unexpected expense at all or very little; 18% do not feel on top of their finances; 21% are not comfortable with their current level of spending; and 34% report not to have enough money to provide for their financial needs in the future.
The core explanatory variables for our analyses relate to events specifically because of COVID-19. We ask: “Regarding the worldwide Corona Virus COVID-19 pandemic, there have been many far-reaching economic and social implications, even if you or your family does not have the virus. Because of COVID-19, since Dec 1, 2019Footnote 7 have you experienced any of the following (may choose multiple):
The terminology of “benefits” has been kept purposefully generic to be applicable worldwide; however, in Australia, these benefits relate specifically to “JobSeeker” government programs (a minimal base-level unemployment assistance) and are a fixed base amount paid fortnightly.Footnote 8 Anyone fulfilling the requirement of officially looking for work would normally receive these benefits. Those who had left the labour market would not have been eligible for benefits, until actively seeking re-employment. We combine the shocks of entry into unemployment or applying for benefits to reflect the Australian “JobSeeker” population. We combine the shocks of salary reduction and hours worked reduction to reflect the nature of the Australian “JobKeeper” population (short time work benefits, or wage subsidies paid through the employer, for those officially still classified as “employed”, but facing reduced industry demand and potentially reduction in work hours).Footnote 9Footnote 10
We consider the association with financial wellbeing of each of the two labour market shocks, and also of whether a person has experienced either of these shocks.Footnote 11 Additional demographic controls include the respondent’s age group, gender, occupation field, household size and Australian state, a linear time trend, and a time trend-state interaction. Given the 10-min response limit of the online survey, elicitation of additional demographic information was not possible.
Appendix Table 2 reports the descriptive statistics on the main variables used in this paper, including a comparison of the unweighted, weighted and population-level descriptives. The weighting achieves a good representativeness across the key variables. Therefore, all descriptive tables and figures as well as estimations use weights from hereon, unless otherwise specified. Mean (weighted) financial wellbeing is 59.3 on the 0–100 scale. The percentile ratios indicate the presence of substantial inequality in the financial wellbeing distribution: 90/10 = 3.80; 75/25 = 2.00; 90/50 = 1.46; 10/50 = 0.39 and the Gini index is 0.234.Footnote 12 About 29% of respondents experienced a reduction in working hours and salaries, whereas about 26% experienced job loss and/or had to apply for unemployment benefits. Almost 36% of Australian residents experienced at least one labour market shock. Appendix Table 3 shows that the labour market shocks of the pandemic seem to be felt across all demographic groups, but especially by women, the young, those in larger households (likely families with several children), and those working as sales workers and labourers.
Figure 2 depicts the observed or “factual” distribution of financial wellbeing as well as the distribution for the “treated” (those who experienced COVID-19 labour market shocks).Footnote 13 The largest mass of the observed distribution is between 55 and 75 on the financial wellbeing scale of 0–100.Footnote 14 This picture changes dramatically for the distribution for the treated only (having experienced a COVID-19-related labour market shock), with its mass situated much further to the left.Footnote 15