Parents' employment, income, and finances before and during the COVID‐19 pandemic

Abstract

The COVID‐19 pandemic brought grave financial concerns for families in the United States as they attempted to navigate the multifaceted impacts of the pandemic. The present descriptive study examined Florida families' employment characteristics, credit card debt, savings characteristics, use of savings based on employment and income variables, and patterns of use of the first 2020 economic impact payment during the early stages of the COVID‐19 pandemic. Responses to an online questionnaire were collected from 526 Florida residents, age 18 or older, who were parents of minor children during the time the study was conducted. Findings are indicative of varying financial impacts on families based on gender, marital status, income level, and employment status related to COVID‐19. Implications are presented for employers, educators, researchers, policymakers, and families.

1. LITERATURE REVIEW

The novel coronavirus disease (COVID‐19) emerged in late 2019 and quickly spread across populations, causing severe respiratory illness, and complications (Centers for Disease Control and Prevention [CDC], 2020). By March 2020, the United States declared a national emergency and the World Health Organization (WHO) officially characterized COVID‐19 as a pandemic (WHO, 2020). Stay at home orders, business and school closures, and economic disruptions brought grave financial concerns for families in the United States as they attempted to navigate the multifaceted impacts of the pandemic.

1.1. Employment

The novel coronavirus pandemic uprooted the lives of American families and debilitated the national economy. By April 2020, the unemployment rate had increased from 4.4% to 14.7%, which was the highest rate since U.S. Bureau of Labor Statistics (BLS) data collection began (BLS, 2020c). This increase in the national unemployment rate equates to over 23 million Americans out of work. Other workers saw reduced hours or furloughs. BLS (2020a) further reported average workweek declines including −2.1 h for manufacturing and −1.3 h for construction.

Previous research has suggested unemployed and insecurely employed individuals experience increased negative mental health outcomes such as stress, anxiety, and depression (Mantler et al., 2005). Unemployment and employment uncertainty can also negatively impact other health outcomes, including increased risk for cardiovascular disease and suicide (Jin et al., 1995).

Emerging pandemic research has set out to illustrate the impacts of COVID‐19 employment uncertainty. The limited early COVID‐19 employment research has focused on identifying groups vulnerable to pandemic employment changes. Moen et al. (2020) found that young adults, that is, those in their 20s, without a college degree were at the greatest risk of unemployment during the pandemic. The employment vulnerability of young adults complements the nearly 47% job loss in the leisure and hospitality industry reported for April 2020 (BLS, 2020a). Further, Collins et al. (2020) examined gender differences in COVID‐19 employment and found a gender gap in pandemic weekly work hours as women were working less hours in response to lockdown school closures. In fact, single mothers were more likely to experience unemployment during the pandemic compared to those who had another working‐age adult in the household (Heggeness et al., 2021).

1.2. Income

BLS (2020b) reported more than a 7% increase to the national average for hourly wages in the 12‐month period ending April 2020. This statistic reflects the pandemic‐related loss of low wage jobs, rather than an increase to household income levels. Similarly, personal income rose over 10% (1.97 trillion dollars) from March 2020 to April 2020 in response to the release of increased unemployment benefits and economic impact stimulus payments (U.S. Bureau of Economic Analysis, 2020a). The rise in personal income disguises the decrease in personal income from February 2020 to March 2020 of $413.8 billion dollars at the start of the COVID‐19 lockdown.

1.3. Credit use

The U.S. Federal Reserve reported that outstanding credit card balances dropped 36 billion dollars in April 2020. New credit purchases declined sharply in April 2020, but began to recover between May and June 2020 (Adams & Bord, 2020). Outstanding revolving credit balances remained low as Americans continued to make increased payments and utilize credit less (Adams & Bord, 2020). Further, the Consumer Financial Protection Bureau (CFPB) (2020) found the number of revolving credit card applications decreased 40% in March 2020. Protections from the federal Coronavirus Aid, Relief, and Economic Security (CARES) Act may have factored into the decline in consumer credit usage. CARES Act provisions include mortgage and student loan forbearances and increased monetary benefits such as stimulus payments and increased unemployment benefits (U.S. Department of the Treasury, 2020).

Conversely, a September 2020 survey by Money.com reported that 29% of pandemic consumers were utilizing their credit cards more than pre‐COVID‐19 (Bhardwaj, 2020). This survey of 2200 US adults also found that, while more than one in four consumers had increased their credit card use, over 50% of survey participants already had or planned to use stimulus check funds to apply to an outstanding debt balance (Bhardwaj, 2020). Auxiliary factors contributing to increased credit usage could include factors such as the national coin shortage, increases in online shopping, and the desire to limit the use of paper dollars as a COVID‐19 safety precaution.

Research of consumer credit use during times of financial crisis shows credit overuse is commonplace. The COVID‐19 decline in consumer debt accrual is a departure from the increases to credit use during the Great Recession. Dunn and Mirzaie (2016) found that, while bank loans and mortgages declined during the Great Recession, other types of consumer debt, including payday loans (38%), credit card (18%), and student loan debt (23%), increased from 2008 to 2011. The use of risky credit products like payday loans have been linked to higher food insecurity for children in low‐income families, whereas thriftiness and saving behaviors lead to lower food insecurity (Loibl et al., 2017).

1.4. Savings

Emergency funds and savings are an important aspect to financial stability. Current research reveals two contrasting consumer saving behaviors during the COVID‐19 pandemic where some consumers saved more money while other consumers spent their savings. According to a CNBC + Acorns Invest In You Savings Survey of 5400 adults in August 2020, almost 14% of Americans have depleted their emergency funds, with young adults in the age range of 25–34 being the hardest hit at 26% (Dickler, 2020). The Federal Reserve Bank of Richmond reported the consumer saving rate at 33% from March 2020 to April 2020 (Corcoran & Waddell, 2020). This increase in consumer saving is the product of the reduction of consumer spending and the increase to personal income for April 2020. Additionally, the Federal Reserve Bank of St. Louis reported the total dollars held in savings accounts increased in that same period about 4.4%. By July 2020, the personal saving rate decreased to 18%, but was still well above the July 2019 rate of 7% (U.S. Bureau of Economic Analysis, 2020b). One factor influencing fluctuations in savings account balances during this period could be a reduction in consumer banking confidence as a result of the Great Recession. Van der Cruijsen et al. (2012) found major financial crisis events like the Great Depression can shake long‐term consumer confidence in the failed industry and have a life‐long impact on consumer behavior.

Recent pandemic research from the Harvard T.H. Chan School of Public Health reported 44% of households with children have depleted their savings, not including the more than one in 10 households that did not have emergency funds before COVID‐19 (Rura, 2020). Further, lower income families (i.e., those below $100,000 per year) with children were found to be more likely to report significant pandemic financial problems with greater vulnerabilities to Latinx families (86%) and Black families (66%) (Rura, 2020). A recent study in the United Kingdom by the Institute for Fiscal Studies acknowledged “forced savings” during the lockdown as the shutdown of businesses forced consumers to either save money normally spent or spend it elsewhere (Davenport et al., 2020). Higher wealth households were more likely to be impacted by “forced savings” behavior as discretionary consumer spending options were limited (Davenport et al., 2020).

1.5. Stimulus use

On March 27, 2020, the over $2 trillion dollar Coronavirus Aid, Relief, and Economic Security (CARES) Act was signed into law. The purpose of the CARES Act was to provide swift economic relief for citizens, businesses, and local governments amid the COVID‐19 crisis. By June 5, 2020, over 159 million economic impact payments (EIP) of up to $1200 per individual and additional $500 per claimed dependent under 17 had been released to Americans (Kane & Loudenback, 2020).

The purpose of the pandemic EIP was to assist financially unstable Americans and stimulate the economy with the newly added purchasing power of consumers. According to June 2020 data from the Federal Reserve Bank of New York, less than one in three Americans (i.e., 29%) used EIP stimulus payments on consumer spending (Armantier et al., 2020). The 29% in consumer consumption spending is further broken down into 18% for essential items, 8% for nonessential items, and 3% for donations (Armantier et al., 2020). This same study found the majority of consumers used stimulus funds to either pay off debts or to save. Research from the Kellogg School of Management revealed most Americans used stimulus payments for multiple items, with the first focus on food and daily essential goods followed by bill and rent payments, within the first few days of receiving the stimulus funds (Baker et al., 2020).

Historically, economic stimulus payments are not a new concept to Americans. In February 2008, the Economic Stimulus Act of 2008 sent over $120 billion dollars to U.S. consumers in the hopes of thwarting the developing Great Recession (Amadeo, 2020). Research on consumer spending from the 2008 stimulus payments revealed 48% of consumers paid off debts, 32% allocated it toward saving, and less than 20% spent the funds (Shapiro and Slemrod, 2009). Of the one in five respondents that elected to spend their 2008 stimulus payment, 25% used the funds for a major household item and 21% used the funds for recreation or travel (Sahm et al., 2010). Further research from Broda and Parker (2008) found that, while a significant majority of families utilized 2008 stimulus payments to pay off debt or save, lower income households were almost twice as likely to spend the additional funds.

2. PURPOSE AND OBJECTIVES

Research is needed to further explore the financial impacts of and responses to COVID‐19 among US citizens supporting families during the pandemic. As such, the purpose of this descriptive study was to examine Florida families' employment, finances, and savings/EPI spending patterns and changes during the early stages of the COVID‐19 pandemic. Five objectives guided this study:

1. Describe respondents' employment characteristics before and during the COVID‐19 pandemic.

2. Describe respondents' credit card debt before and during the COVID‐19 pandemic.

3. Describe respondents' financial savings characteristics and use before and during COVID‐19.

4. Examine patterns in respondents' use of financial savings during COVID‐19 based on employment status, income level, gender, and marital status variables.

5. Examine patterns in respondents' use of the 2020 EIPs based on employment characteristics.

3. METHODOLOGY

The population of interest for this study was Florida residents, age 18 or older, who were parents of minor children during the time the study was conducted. The authors chose to focus on one state to better control for the variation of COVID‐19 guidelines and laws at the state level, and Florida was chosen due to author residence. An online questionnaire was distributed in June 2020 to a total of 975 residents. An initial pilot test of 50 respondents was conducted to assess survey flow and quality of data. Attention filters (e.g., select “strongly agree” for this answer) were used to identify respondents not paying attention to the questions. Respondents who (a) did not complete all items of the instrument, (b) did not select the appropriate answer to attention filters, and (c) did not fall within the parameters of being a Florida resident, 18 years of age or older, or a parent or guardian of a minor child were excluded from analyses. Useable responses were obtained from 526 residents for a 53.9% participation rate. Full demographic characteristics of respondents in this study are displayed in Table 1. Potential exclusion, selection, and non‐participation biases can limit the use of nonprobability samples (Baker et al., 2013). Specifically, the sample is limited in that upscale bias is reflected in the use of online surveys (i.e., access to the internet) and respondents' having higher income levels (59% reported income $50,000 or greater), higher educational levels (43% reported a bachelor's degree or higher), and being married or in a domestic partnership (73%). In addition, almost 50% of the sample reported having no credit card debt. Therefore, the results of this study should not be generalized beyond the sample obtained for this study.

TABLE 1.

Demographic characteristics of respondents

Variable	f	%
Gender
Male	137	26.0
Female	389	74.0
Age
20–29	97	18.4
30–39	204	38.8
40–49	157	29.8
50–59	66	12.5
60–69	2	0.5
Ethnicity
Hispanic/Latino(a)/Chicano(a)	84	16.0
Not Hispanic/Latino(a)/Chicano(a)	442	84.0
Race
White	402	76.4
Black	64	12.2
Asian	21	4.0
American Indian	6	1.1
Multi‐racial	11	2.1
Other	22	4.2
Education
Less than 12th grade (did not graduate high school)	21	4.0
High school graduate (includes GED)	89	16.9
Some college, no degree	103	19.6
2‐year college degree (Associate, Technical, etc.)	87	16.5
4‐year college degree (Bachelor's, etc.)	145	27.6
Graduate or professional degree (Master's, PhD, MBA, etc.)	81	15.4
Income
$24,999 or less	90	17.1
$25,000–$49,999	125	23.8
$50,000–$74,999	125	23.8
$75,000–$149,999	145	27.5
$150,000–$249,999	29	5.5
$250,000 or more	12	2.3
Marriage/partnership status
Single, never married	86	16.3
Currently married or in a domestic partnership	382	72.6
Divorced/separated	53	10.1
Widowed	5	1.0
Number of children
One	223	42.4
Two	192	36.5
Three	75	14.3
Four	23	4.4
Five	9	1.7
Six	2	0.4
Seven or more	2	0.3

Data were collected via a researcher‐developed online survey questionnaire distributed through a third‐party research company, Qualtrics. Through Qualtrics, respondents are recruited using traditional, actively managed market research panels and social media platforms. Qualtrics also employs digital fingerprinting technology, IP address checks, and works with panel partners that also employ such methods to help exclude duplication and ensure validity when obtaining non‐probability opt‐in samples in market research (Qualtrics, 2019). Non‐probability sampling is an approach commonly used to make population estimates (Baker et al., 2013). This sampling method has become more common in research examining public opinion of emerging issues due to higher response rates compared to common probability‐based methods (e.g., random digit dialing of landline numbers), increased access to internet, relatively low costs of online surveys, and overall greater ease of reaching members of the population of interest (Lamm & Lamm, 2019).

Five sections of the questionnaire were used for data analyses in this study. Employment status prior to COVID‐19 was assessed by asking respondents to indicate which best described their status (e.g., employed full‐time, retired, etc.). Employment status during COVID‐19 was formatted to assess employment status and change due to COVID‐19 (e.g., “I am still employed but have experienced a significant decrease in hours/income due to COVID‐19”). Those who indicated still being employed to some degree were then asked whether they were working from home due to COVID‐19 and, if not, why. To assess credit card debt, respondents were asked to indicate whether they had credit card debt and, if so, to enter the amount. Amounts of debt provided by respondents were then coded into debt categories ranging from less than $1000 to $30,000 or more. To assess financial savings and use, respondents were asked if, prior to COVID‐19, they had set aside any emergency or rainy day savings funds that could cover their expenses in case of sickness, job loss, economic downturn, or other emergencies. Answer choices were “yes” and “no.” They were also asked how much of their financial savings they had used to cover COVID‐19‐related expenses. Responses were collected using a 5‐point ordinal scale (1 = none; 2 = a small amount; 3 = a moderate amount; 4 = a great deal; 5 = all of it). Economic Income Payment (i.e., COVID‐19 stimulus check) use was assessed by asking respondents if they had received or expected to receive such a payment. Those who had received or expected to receive a stimulus check were then asked to indicate, by checking all that apply, how they had used or planned to use that income. Lastly, income was measured by asking respondents to select the income bracket category that represented their income before taxes during the 2020 financial year.

Data were analyzed using the SPSS26 software package. Data analyses consisted of descriptive statistics (e.g., means, standard deviations, frequencies, and percentages). Analyses conducted for objectives four and five also consisted of cross tabulations to examine descriptive results by employment and income categories, as well as independent samples t‐tests to examine differences between two groups (single, never married mothers, and others).

4. RESULTS

4.1. Objective one

Objective one was to describe respondents' employment characteristics before and during COVID‐19. Prior to COVID‐19, more respondents indicated being employed full‐time (f = 305; 58%) than any other employment category (see Table 2). During COVID‐19, more respondents indicated still being employed and making the same income as they were prior to COVID‐19 (f = 205; 39%) or that they were not employed prior to COVID‐19 and still were not employed (f = 133; 25.3%) compared to other response categories (see Table 3).

TABLE 2.

Respondents' employment status prior to COVID‐19

Employment status	f	%
Employed full‐time	305	58.0
Homemaker	88	16.7
Employed part‐time	55	10.5
Not employed, but looking for a job	27	5.1
Temporarily laid off	16	3.0
Disabled and not working	15	2.9
Retired	9	1.7
Student	7	1.3
Not employed and not looking for a job	4	0.8

TABLE 3.

Respondents employment status during COVID‐19

Employment status	f	%
I am still employed and making the same income as I was prior to COVID‐19.	205	39.0
Not applicable because I was not employed prior to COVID‐19 and am still not employed.	133	25.3
I am still employed but have experienced a significant decrease in hours/income because of COVID‐19.	114	21.6
I am unemployed and not working because of COVID‐19.	74	14.1

Respondents who indicated they were still employed during COVID‐19 (n = 318) were then asked whether they were working from home. A little over one‐third of respondents (f = 127; 39.8%) reported they did not work from home prior to COVID‐19, but then worked from home due to COVID‐19. Additionally, 115 (36.1%) respondents did not work from home due to COVID‐19, and 77 (24.1%) worked from home both prior to and during COVID‐19.

Respondents who indicated they did not work from home during COVID‐19 (n = 115) reported not doing so because they are considered an essential employee (f = 76; 66.1%) or that their job is not one that can be done from home (f = 33; 28.7%). Few respondents reported not working from home due to their company/employer not following recommendations for employees to work from home (f = 5; 4.3%) or because they chose not to work from home (f = 1; 0.9%).

4.2. Objective two

Respondents were first asked if they had any credit card debt prior to COVID‐19. Half of the respondents (f = 263; 50%) had credit card debt prior to COVID‐19, and the other half did not. Respondents who indicated they had credit card debt were also asked to indicate how much debt they had accrued. Table 4 depicts the amount of debt respondents had accrued prior to COVID‐19. Respondents were then asked to indicate if they had incurred new credit card debt due to COVID‐19 to pay for daily living expenses. More respondents (f = 393; 74.7%) had not incurred new debt than those who had (f = 133; 25.3%).

TABLE 4.

Respondents' credit card debt before COVID‐19

Response item	f	%
None	261	49.6
Less than $1000	50	9.5
$1000–$4999	69	13.1
$5000–$9999	62	11.8
$10,000–$14,999	42	8.0
$15,000–$19,999	12	2.3
$20,000–$24,999	8	1.5
$25,000–$29,999	4	0.8
$30,000 or more	12	2.3
Unsure of credit card debt amount	6	1.1

4.3. Objective three

To describe financial savings characteristics, respondents were first asked to indicate whether they had set aside any “rainy day” or emergency savings prior to COVID‐19. A little over half of the respondents (f = 307; 58.4%) indicated they had set aside an emergency savings fund that could cover them in case of sickness, job loss, economic downturn or other emergencies; 219 (41.6%) respondents indicated they had not.

Next, respondents were asked whether they have had to use money from their savings to cover expenses due to COVID‐19. Compared to other response category options, more respondents indicated no, they had not used any savings at all (f = 183; 34.8%) or had used a small amount (f = 127; 24.1%). Relatively fewer respondents reported they had used all of it (f = 85; 16.2%), a moderate amount (f = 84; 16.0%), or a substantial amount (f = 47; 8.9%).

4.4. Objective four

Objective four examined the amount of financial savings used by respondents during COVID‐19 base on their employment status, income level, gender, and marital status characteristics. Of the respondents who indicated having to use none of their savings to cover costs associated with COVID‐19, more fell within the category of still being employed and making the same income as prior to COVID‐19 (f = 110; 20.9%). Few respondents indicated having to use all of their savings to cover costs associated with COVID‐19. Those who did were more likely to be those who were still employed but had experienced significant decreases in hours/income due to COVID‐19 (f = 26; 4.9%; see Table 5). Respondents' uses of financial savings during COVID‐19 were also examined by their income brackets. Respondents in all income brackets reported having used varying amounts of their savings to cover COVID‐19 related expenses. Compared to other income brackets, a larger number of respondents who used all of their savings to cover COVID‐19 expenses were those who made $49,999 or less, whereas no respondents who made $250,000 or more reported having used all of their savings. Table 6 depicts the full results of respondents' use of financial savings by income. Lastly, respondents' use of financial savings during COVID‐19 were compared using independent samples t‐tests between those who identified as single, never married females (f = 74) and those who did not (f = 452). Respondents who were single mothers (M = 2.99; SD = 1.60) were significantly more likely to have had to use a larger amount of their financial savings during COVID‐19 than those who were not (M = 2.39; SD = 1.41); t(524) = 3.31; p = 0.001.

TABLE 5.

Respondents' use of savings based on employment status during COVID‐19 (N = 526)

	Amount of savings spent to cover COVID‐19 expenses f (%)
Employment status	None	Small	Moderate	Substantial	All
Still employed and making same income as prior to COVID‐19.	110 (20.9)	58 (11.0)	31 (5.9)	13 (2.5)	12 (2.3)
Still employed but have experienced a significant decrease in hours/income because of COVID‐19.	19 (3.6)	36 (6.8)	32 (6.2)	17 (3.2)	26 (4.9)
Unemployed and not working because of COVID‐19.	9 (1.7)	12 (2.3)	9 (1.7)	15 (2.9)	20 (3.8)
Not employed prior to COVID‐19 and still not employed (includes retired, homemakers).	48 (9.1)	20 (3.8)	17 (3.2)	7 (1.3)	15 (2.9)

TABLE 6.

Respondents' use of savings during COVID‐19 based on income bracket

	Amount of savings spent to cover COVID‐19 expenses f (%)
Income bracket	None	Small	Moderate	Substantial	All of it
$29,999 or less	22 (4.2)	13 (2.5)	11 (2.1)	5 (1.0)	22 (4.2)
$25,000–$49,999	36 (6.8)	23 (4.3)	23 (4.3)	10 (1.9)	21 (4.0)
$50,000–$74,999	40 (7.6)	34 (6.5)	25 (4.7)	16 (3.0)	15 (2.9)
$75,000–$149,999	68 (12.9)	37 (7.0)	22 (4.2)	17 (3.2)	11 (2.1)
$150,000–$249,999	13 (2.5)	7 (1.3)	7 (1.3)	2 (0.4)	5 (1.0)
$250,000 or more	5 (1.0)	11 (2.1)	3 (0.6)	2 (0.4)	0 (0.0)

4.5. Objective five

Objective five examined respondents' use of the COVID‐19 stimulus check based on employment status during COVID‐19. Of the respondents, 391 (74.3%) indicated they had received or expected to receive a COVID‐19 stimulus check. 96 (18.3%) respondents had not/did not expect to receive a check, and 39 (7.4%) were unsure.

Respondents who indicated they had received/expected to receive a COVID‐19 stimulus check (n = 391) were then asked to indicate, by checking all that apply, how they intended to use the stimulus money. The uses selected by the largest number of participants included to pay rent or other bills (f = 208; 53.2%) and to cover income/job loss due to COVID‐19 (f = 123; 31.5%). Stimulus check uses selected by the fewest number of respondents were to donate some of the money (f = 15; 3.84%) and for fun (f = 17; 4.35%). Use of stimulus check money was then examined based on employment status during COVID‐19. Full results of respondents' use of stimulus check payments based on employment status are displayed in Table 7.

TABLE 7.

Respondents' stimulus check spending based on employment during COVID‐19 (n = 391)

	Use of COVID‐19 stimulus check payment f (%)
Employment during COVID‐19	Pay rent or other bills	Cover income/job loss	Cover increased cost of food	Make payments on loans/debt	Put all into savings	For fun	Donate some	Other
Still employed and making same income as prior to COVID‐19.	68 (17.4)	33 (8.4)	43 (11.0)	46 (11.8)	49 (12.5)	8 (2.0)	9 (2.3)	12 (3.1)
Still employed but have experienced a significant decrease in hours/income because of COVID‐19.	51 (13.1)	44 (11.3)	28 (7.2)	20 (5.1)	15 (3.8)	2 (0.51)	2 (0.51)	1 (0.26)
Unemployed and not working because of COVID‐19.	36 (9.2)	31 (7.9)	16 (4.1)	10 (2.6)	2 (0.51)	2 (0.51)	1 (0.26)	1 (0.26)
Not employed prior to COVID‐19 and still not employed (includes retired, homemakers).	53 (13.6)	17 (4.3)	30 (7.7)	29 (7.4)	12 (3.1)	6 (1.5)	3 (0.77)	2 (0.51)

5. DISCUSSION AND IMPLICATIONS

It comes as no surprise to discover that a pandemic, which effectively shut down the national economy, would have financial impact on families across the United States. It should be noted that bias may exist in this sample, particularly regarding upscale bias and the likeliness of higher income individuals having access to the online link used for data collection in this study. It is important to note that 35.8% of the total sample (and 47.8% of those in the sample who were employed before the COVID‐19 pandemic) had experienced a significant decrease in hours and/or income or had experienced unemployment due to COVID‐19. These results are consistent with findings from the Parker et al. (2020) which indicated over 40% of adults in the United States had experienced job loss or income reduction as a result of COVID‐19.

It is interesting to note that, of those respondents in this study who were still employed during COVID‐19 (n = 318), a little over one‐third indicated they had been able to shift their jobs from being physically present at their employer's location to being able to work remotely from home. Paired with the additional one‐fourth of respondents who worked from home both before and during COVID‐19, the results are indicative of a relatively flexible workforce that is able to adapt to drastic changes in societal functioning. Of those who were still employed but not able to work from home, the vast majority indicated this was due to being an essential employee or performing a job that could not be done from home. However, it is troubling that some participants, although very few, were forced to work away from home due to their employer not following recommendations for employees not to work from home.

Because the sample was composed of parents of minor children, of whom the majority were having to juggle online education or alternate childcare arrangements during school and daycare closures while also having to work from home or maintain their job in their normal location, there may be implications of additional childcare supports needed for those who continued to be employed during the COVID‐19 pandemic. While many employees have continued to work from home throughout the United States, others have returned to work at their employer's location and many schools and daycares have reopened. It is important for employers to have viable contingency plans for employees who may be sent back home to isolate or quarantine due to COVID‐19 exposure. It is also important for employers and families to have contingency plans in case of school and daycare closures and/or continued virtual school. Contingency plans should consider areas such as the employee's caregiving responsibilities, work duties, and ability to work remotely with the goal of maintaining the family's income level. Policymakers should consider these needs when developing future economic stimulus packages.

Results pertaining to credit card usage and debt also revealed some key implications that warrant further examination. According to the National Foundation for Credit Counseling 2020 Consumer Financial Literacy Survey report (The Harris Poll, 2020) published on March 23, 2020 (i.e., only 12 days after the WHO declared COVID‐19 a pandemic) (WHO, 2020), 62% of adults in the United States had carried credit card debt in the past 12 months. Only half of respondents in the present study indicated that they had credit card debt prior to COVID‐19, which indicates the sample in this study is slightly unrepresentative of the larger population in terms of credit debt. Additionally, when asked about accruing new credit card debt during the pandemic, most respondents had not incurred any new debt at the point in time of the survey. While counter‐intuitive and a departure from typical credit overuse common during times of financial crisis, this finding is congruent with the U.S. Federal Reserve's (2020) findings of new credit card purchases declining sharply in April of 2020 and Americans continuing to make increased payments and utilize credit less (Adams & Bord, 2020).

Whereas National Foundation for Credit Counseling found that nearly 70% of US adults have non‐retirement savings, only a little over half of the respondents in the present study indicated having emergency savings that could cover them in the event of sickness, job loss, economic downturn, or other emergencies (The Harris Poll, 2020). This finding provides further evidence that the COVID‐19 financial circumstances of Florida based families in the present study may differ from national averages. As such, it is recommended that COVID‐19 relief programs and efforts be tailored to the needs of families on a state‐to‐state basis to address the unique needs and circumstances of state residents. Regarding respondents' use of savings due to COVID‐19, one‐third of respondents who had savings indicated that they had not used any at all, and one‐fourth indicated they had used only a small amount. Further, differences were observed in savings usage based on both employment status income level, gender, and marital status. For instance, while three‐fourths of those who were still employed and making the same income as prior to 2019 had spent none or a small amount of their savings to cover COVID‐19 expenses, only a third of those who were unemployed and not working because of COVID‐19 had spent none or a small amount of their savings. However, it is important to note that one‐third of those unemployed and not working because of COVID‐19 had spent all of their savings. Future research is needed to better understand the vast differences in savings usage among those unemployed due to COVID‐19. Similarly, Rura (2020) found that 44% of households with children who had an emergency fund have depleted their savings as a result of the pandemic. Both the rate of marriage (Curtin & Sutton, 2020) and the birth rates for unmarried women (Martin et al., 2021) have declined in the United States over the past decade. In examining differences in use of financial savings based on gender and marital status, 14% of the participants in this study identified themselves as single, never married mothers, with findings indicating that they were significantly more likely to have had to use a larger amount of their financial savings during COVID‐19 than their counterparts. This disparity is one that should be considered in future research.

This gives pause to consider new trends. While the pandemic created a financial crisis for some, others saw increases to household income from the EIP. It is possible that reduced credit card use/debt accrual and relatively low spending of savings is due in part to use of the COVID‐19 stimulus checks (EIPs) provided by the CARES Act. Nearly three‐fourths of respondents indicated they had received or expected to receive a COVID‐19 stimulus check. In every employment category, the highest frequency of respondents indicated they would use their stimulus money to pay rent or other bills. For those who were still employed and making the same income as prior to COVID‐19, 17.2% planned to make payments on debts/loans, and 18.3% planned to put it all into savings. Conversely, for those who were unemployed and not working due to COVID‐19, only 10.1% planned to make payments on debts/loans and only 2% planned to put it all into savings. It is important to recognize that the majority of financial crises, especially at the individual/family level, do not come with stimulus funds to counter the crisis. To better prepare for future pandemics or major economic disruptions, qualitative research should be conducted to further explore families' financial experiences and uses of stimulus payments in crisis situations. Research should further examine the effects of gender and marital status on family financial fragility during crisis situations. Research of this nature could help inform officials and policymakers of best methods of supporting US families financially or through other means (e.g., loan forgiveness, food distributions, etc.) based on their needs.

The pandemic will likely have long lasting implications for society as a whole. For example, the pandemic has highlighted the fragility of families and the ongoing need for families to have emergency savings. The pandemic has also highlighted the ongoing childcare issues faced by mothers in the workforce, creating particularly difficult circumstances for single mothers who experienced unemployment or were able to remain employed and faced lack of childcare. It has shown the need for flexibility in the workforce, particularly for those employees who have caregiving responsibilities, as well as the need for viable contingency plans. Employers, educators, researchers, policymakers, and families should heed this call to action moving forward.

Garrison, S. T. , Rampold, S. D. , Vasquez, K. , Gillen, M. , & Baker, L. M. (2022). Parents' employment, income, and finances before and during the COVID‐19 pandemic. Journal of Consumer Affairs, 56(1), 276–291. 10.1111/joca.12443