Families first: A comparative study of company responses to paid care leave programs in the COVID-19 pandemic

For caretakers, paid care leave (PCL) policies encompass the group of policies that offer paid time off to provide care for a family member or self-care.

Care leave will be defined as any leave to provide care for a family member or self-care. Family leave comprises any leave to provide care for a family member – child, parent, or other immediate relative. Sick or disability leave, or self-care, in this context, is short- to mid-term time off to tend to or recover from an illness or medical procedure.

Even though PCL has grown in global popularity over the past century, especially as a part of larger welfare state regimes (Lewis, 1992), the United States is a notable outlier as the only advanced economy without a paid care leave policy at the federal level. Instead, the United States traditionally relies on a “go-it-alone vision of personal responsibility” where individuals acquire jobs with PCL benefits, dispelling a shared or universalist risk approach in favor of employers taking on employee risk (Hacker, 2019). For Americans, the Family Medical Leave Act (FMLA) of 1993 is the only federal regulation providing care leave for a family member – the policy is unpaid, provides job-protection for up to 12 weeks, and only covers approximately 50 percent of the total US workforce (Kelly, 2010; Ruhm, 1997). Apart from a 2015 update to the definition of spouse to include same-sex marriage, FMLA has remained mostly unchanged for almost 30 years.

The COVID-19 pandemic created a unique opportunity to analyze how businesses respond to PCL laws, an understudied portion of the PCL literature. In 2020, the Families First Coronavirus Response Act (FFCRA), an expansion of the FMLA, provided emergency paid sick leave and paid family and medical leave, referred to here as PCL, for eligible employees experiencing effects of COVID-19. As FFCRA was only applicable (on a mandatory basis) for an abbreviated period (April 1, 2020 to December 31, 2020), I use the FFCRA timeline, and two state-level PCL policies with implementation dates on either side of 2020, the New York State Paid Family Leave (NYS-PFL) and the Massachusetts Paid Family and Medical Leave Act (MA-PFML), to establish a policy experiment. As a reference, Table 1 details the stipulations of the relevant PCL policies and it should be noted that the 500-employee cutoff used in FFCRA is a common metric signifying small versus large companies in the United States, per the US Small Business Administration (United States Small Business Administration, 2018).

In the policy experiment research design, I analyze employer responses and perceptions on PCL through a statistical analysis on a newly created dataset. As companies are typically reluctant to publicly disclose any of their internal actions (Kitching et al., 2015; Shaffer, 1995), my dataset comes from a survey created and administered in late 2020 to 306 business owners and managers in the New York and Boston metropolitan areas (by county and zip code) – sample survey questions are in the Appendix. By asking managers about their 2020 business decisions, my results use odds ratios from binary logistic regressions to report how a variety of American companies use (or opt not to use) PCL laws as tools to manage PCL cost concerns and other business disruptions.

Given the American policy landscape, there is an assumption that PCL creates additional costs, although the implication is only just starting to be confirmed.

In general, most studies suggest regulation imposes (regressive) costs (Kitchling et al., 2015). In 2021, there is emerging literature to confirm the cost concern associated with PCL mandates, or the idea that PCL creates cost-related burdens on firm owners and managers. Notably, Bartel et al. (2021) examine the cost assumption in New York firms, where they found only a small share of NY companies opposed NYS-PFL, and Huebener et al. (2021) confirm the cost assumption through an analysis of German firms.

My study adds two major contributions to the existing literature. First, my study confirms the existence of a general PCL cost concern, or the perception of a cost concern, as 54.6 percent of responding companies reported some level of concern about the cost of programs such as NYS-PFL, MA-PFMLA, and FFCRA. Second, my study finds that when companies report cost concerns about PCL programs, they are, in general, more likely to result in non-employee focused operational outcomes such as an increase in prices (of the goods and services of responding companies) versus the predicted explicit outcomes like layoffs or pay cuts. In other words, I do not find statistically significant evidence to suggest employers do not directly shift the burden of mandated benefits to employees as predicted by economics literature (i.e. Gruber, 1994), at least in the medium term. Additionally, similar to Kitching et al., (2015), the study gives reason to believe that (small- and mid-sized) companies respond to regulations in a variety non-uniform of ways, many of which are reflective of the internal firm culture (i.e. Bana et al., 2018). For example, the existence of an internal paid family leave (PFL) policy pre-2020 shows a statistically significant correlation to decreasing wages in small companies – specifically, companies that do not have an internal PFL policy are about 68 percent less likely to decrease wages compared to their counterparts with PFL. The dynamic nature of company responses to legislation warrants further research, where companies are not treated in a uniform way but a more stratified or intersectional way.

Further, there is evidence to suggest that the policy experiment-specific sub-categories of businesses, presented here by the 500-employee cutoff stipulation of FFCRA into large company and small company designations, have unique responses to the cost concerns presented by PCL. For example, large companies (more than 500 employees) reporting some level of PCL cost concerns, who often have lower overhead and administrative costs for administering benefits than small companies (O’Brien, 2003), are more likely to provide new or expanded paid family leave (PFL). On the other hand, small companies (less than 500 employees) that reported some level of PCL cost concerns are more likely to increase the number of independent contractors used by the firm. As part-time employees and independent contractors are typically not eligible for benefits (Kessler, 2018), the hiring process and general management at small firms (in 2020) may include an aspect of avoiding employee benefits costs altogether. Therefore, the perceptions of costliness may influence the continued absence of PCL in US companies, but when businesses are required to offer PCL, the outcomes may not be as detrimental as originally predicted. Paid care leave laws and their associated costs, in the US, are largely treated like any other additional cost incurred by a company, resulting in strategic decisions targeting all costs (not just PCL costs).

It should be noted that the severity of the COVID-19 pandemic may exaggerate any differences in firm responses, which tempers the interpretation of PCL effects. In the analysis, firm characteristics are included as covariates to control for COVID-19 effects. Further, as FFCRA was paid for by the US federal government (through tax credits), the program did not impose a (net) cost burden (on policy users), as it was intended to help small companies maintain business continuity. Thus, my study aims to be a contribution to research regarding benefits administration (in the US) and company behavior. By examining how companies respond to various types of legislation, managers and policymakers can better predict the ripple effect of such policies, while solidifying the assertion that there is no uniform way 21^st century companies respond to business disruptions.

The remainder of the paper is divided into the following sections: an overview of the relevant literature, data and methodology, the empirical results, and lastly, a discussion with a conclusion.

The COVID-19 pandemic highlighted a troublesome reality for American workers with care-giving responsibilities: only 24 percent of private companies provided paid family leave in 2019 (Society for Human Resource Management, 2019). The lack of paid leave provisions forces American workers, especially low-wage, frontline workers, to choose between a paycheck and caring for a loved one (Ansel & Boushey, 2017). For firms, PCL mandates often create hard-to-measure administrative and cost burdens (Phillips, 2002) – burdens exaggerated by the COVID-19 pandemic.

The overall objective of paid care leave policies is to alleviate economic risk by providing time off to workers, helping to support workers’ family life as well as economic activity. As a family-friendly program, PCL has gained notoriety for its ability to promote employment and productivity while improving health status, but the lack of uptake in the US is an anomaly. Per Bartel et al. (2021), “the lack of policy action in the US partially reflects concerns about the potential cost of [PCL], especially on employers,” (1). Further, Bana et al. (2018) suggest firm-specific factors such as earnings premium and company culture can amplify disparities in the use of PCL, even when the benefits are more or less universally available (i.e. California). Therefore, further examination of how American companies perceive and react to PCL policies is warranted.

As such, it is important to acknowledge PCL policies are only applicable to part of the population: those who are engaged in formalized labor. Additionally, people without caretaking responsibilities may not necessarily deem paid care leave as a true welfare policy with universal benefits. The following section will provide an overview of the literature on mandated benefits and documented outcomes associated with PCL.

The experimental format allows my research design to address the gaps in the literature while focusing on a change in the law that created exogenous variation in the variable of interest (cost concern from PCL), allowing for a rich analysis of an event (FFCRA) (List, 2011). Here, the unit of analysis is the firm, so the study aims to uncover constraints perceived and/or experienced by the responding companies because of variation of inputs (the availability of PCL policies) (Bandiera et al., 2011). Further, the policy experiment research design allows for observation of the exogenous rollout of FFCRA. Since the research is guided by the assumption that PCL programs impose costs on firms, a key variable is established in the survey-based data to represent that assumption: PCL cost concern (PCOST). Developed using a Likert scale, PCOST was derived from the following question (with the associated codes and frequencies):

As the company representative, how concerned were you that state and federal PCL policies would create additional cost concerns for the business? Ex. NYS-PFL, MA-PFML, and/or FFCRA

Extremely unconcerned (1): 7.8%

PCOST quantifies the perception of cost concerns regarding PCL programs across survey respondents: with a mean of 3.41, generally, firm managers in New York and Boston reported some level of concern about the cost of PCL programs like FFCRA.

As such, I establish a comparative framework to examine how companies reporting PCL cost concerns behave in the COVID-19 pandemic. First, the chosen metro areas of New York and Boston were subject to state-level PCL effective dates on either side of 2020 and FFCRA: New York's policy was enacted in January 2018 and Massachusetts's state policy was enacted in January 2021 (see Table 1 for policy details). In other words, New York had an established state-level PCL policy available during the COVID-19 pandemic, while Massachusetts companies were (potentially) aware of, but unable to use the state-level policy in 2020. Per Table 2, NY firms would have an extra PCL resource over their MA peers and, all else held constant, would be expected to be better equipped for business disruptions. It is expected that, if possible, companies would select the policy most beneficial for them.

While not proxies for the states of New York and Massachusetts, the metropolitan areas represent a large portion of the population of each state, generate significant economic, political, and social power, and act as host for many company headquarters suitable for the survey administration – enabling appropriate units of analysis. The inclusion of geographical information as a firm control also captures any effects resulting from local labor conditions and policies not expressly discussed. For example, NYC passed a gradual minimum wage law in 2017, one year before the start of NYS-PFL, which may have lingering effects in 2020 business decisions (Bartel et al., 2021).

It should be noted that the inclusion of New York City is a special case as the city was the center of the COVID-19 pandemic in the US, and therefore may not be generalizable to other US cities (Thompson et al., 2020).

My survey aims to make previously unavailable perceptions and actions regarding PCL policies available for examination. As access to firm decision-making data is limited and often private, survey or questionnaire-based research using managers as subjects allows for the collection of large sample sizes for quantitative study (Shaffer, 1995). The approximately 15-minute online survey solicited managers’ knowledge in the following areas both before and after the onset of the COVID-19 pandemic in March 2020: firm and respondent demographics, care policies, employee management, and business operations.

Survey questions come from a variety of sources including Appelbaum & Milkman (2015), Gadhoke & Gevorkyan (2015), Hacker et al. (2014), Lerner & Appelbaum (2014), Society for Human Resource Management (2019), and Society for Human Resource Management & Oxford Economics, (2020). As a method of reliability and validity, a pilot survey was conducted over two two-week periods in June 2020 to a small group of about 30 managers in the NYC area. The full survey is available upon request.

Using company city, zip code, and job title of the respondent as identifiers, my survey was administered by Qualtrics through its proprietary Small Business Panel in late 2020, returning 306 complete entries. Qualtrics limited complete entries to classifications of managers that typically have operational knowledge as well as decision-making capacity within firms, and respondents were asked to confirm that knowledge before completing the survey. Further, as the survey data was collected in November and December 2020, responding companies presumably had realized unique or specific costs due to the COVID-19 pandemic.

If firms are expected to face cost burdens from a PCL mandate like FFCRA, those companies are also expected to offset the costs of FFCRA. The two methods of offsetting PCL costs examined here are explicit outcomes, those that have direct employee-focused implications, and implicit actions, methods of cutting costs across the business without directly affecting employees (although associated ripple effects may affect employees in the future). For all questions in the survey, each outcome variable was sorted into the appropriate category. The final list of 19 outcomes analyzed, including the variable names and identifiers are separated by type of outcome and are listed in Table 3; Appendix Tables 10 and 11 include validity and reliability tests for the outcome variables.

All questions in the survey were written in a multiple-choice format, with some questions allowing for more than one option (“Select all that apply”) and additional open-ended write-in comments (“Other”). The resulting data is largely categorical, except for select Likert scale questions, and the outcomes in Table 3 are dichotomous. As a quantitative methodology tool, minimizing open-ended questions allows the data to be coded and statistically interpreted (Jain et al., 2016). The survey also elicits anonymous responses by not allowing for lengthy answers that may bias or reveal the respondent.

As many of the survey questions are categorical in nature, a binary logistic regression is used to evaluate the predictive capacity of the cost concern variable (PCOST) and covariates representing company characteristics on the set of 19 dichotomous outcomes from Table 3 (Egessa et al., 2020; Field, 2009). The analysis follows the equation: Yi=β0+β1X1i+β2X2i+β3X3i+β4X4i+β5X5i+β6X6i+β7X7i+εi {{Y}_{i}}={{\beta }_{0}}+{{\beta }_{1}}{{X}_{1i}}+{{\beta }_{2}}{{X}_{2i}}+{{\beta }_{3}}{{X}_{3i}}+{{\beta }_{4}}{{X}_{4i}}+{{\beta }_{5}}{{X}_{5i}}+{{\beta }_{6}}{{X}_{6i}}+{{\beta }_{7}}{{X}_{7i}}+{{\varepsilon }_{i}} where Y is an outcome variable from Table 3 and X denotes a predictor variable:

X_1i is PCL cost concern (PCOST)

for firm i, and ɛ is the error term.

The selected variable Y can also be interpreted as the probability or the odds of the outcome occurring. As such, logistic regressions analyze the logit transform of the probability of Y and connect the probability to the linear predictors in the model (Egessa et al., 2020).

It should be noted that PCOST is an imperfect measure of the severity of the program's impact as it relies on the perception of the responding manager. Additionally, survey respondents are asked to only consider PCL programs, but it is difficult to truly separate PCL costs from overall business costs during a disruptive global event like the COVID-19 pandemic. For example, if a manager responds “Somewhat unconcerned” regarding PCL costs, that does not imply that there is no cost (and therefore no corresponding concern), or that the lack of concern would disincentivize a company to offset PCL costs. Some COVID-19 cost concerns may address multiple issues including PCL, such as the ability to work remotely – a business expense that has many operational implications for business continuity. As previously discussed, the dataset is limited to job titles that are assumed to have decision making responsibilities, but some managers may have more knowledge or oversight than others. For example, a respondent with the job title of CEO or Owner is assumed to have ultimate oversight and knowledge of business operations, whereas a Manager may have knowledge and decision making ability within a department or division but not across the entire organization and all of its operations. Thus, LMAN is included in the equation above as a control for varying levels of manager included in the study, and the associated categories of LMAN are list in Table 4.

Initially, the variable of interest (PCOST) is tested across the 19 outcomes in the full dataset (N=306). Subsequent analysis is performed on three additional subsets of the data to reflect the policy experiment format – large firms (500 employees or more), small firms (0–100, 100–200, and 200–500 employee categories), and FFCRA users.

Ultimately, the FFCRA Users subset is dropped from the analysis due to insufficient data (N=40), in conjunction with presumably usage and compliance issues associated with FFCRA, which are considered in the Discussion section.

The next section compiles the results of the binary logistic regressions. I use the odds-ratios, with appropriate indications for significance. The estimated odds-ratios, or the change in odds which is denoted by Exp(B) in SPSS, show how a change of one unit of an independent variable influences the chance of a particular outcome variable, and therefore can only take positive values (Field, 2009). Values between zero and one mean a negative influence of the independent variable and indicate a negative coefficent, or as the predictor increases, the likelihood of the outcome (dependent) variable decreases. Conversely, all values greater than one indicate a positive influence and a positive coefficient, or as the predictor increases, the likelihood of the outcome also increases. For example, if the odds ratio is 1.468, as it is in Table 5's statistical significance correlating PCL cost concerns (PCOST) to an increase in prices (IPRICE), it means as cost concerns increase by one unit, companies are 46.8 percent more likely to increase their prices.

The statistical analysis seeks to evaluate if companies that reported cost concerns regarding PCL laws (PCOST) have any predictive power on a set of 19 outcomes collected through a custom survey. As all independent variables except PCOST are categorical in nature, as discussed in the previous section, many are broken out in all results tables by category (Tables 5–7 in this section).

Similarly, industry (IND) and level of responding manager (LMAN) are not broken out by category in the regression analysis due to a lack of significance.

Adding controls like FPFL expands past business demographics to (incompletely) account for firm culture, which is assumed to influence greater employee management practices (Bana et al., 2018). By adding the firm characteristics, the regression isolates not only the relationship between PCOST and the set of outcome variables from external influences, but it helps identify what outcomes may be COVID-19 and/or economic effects instead of specific PCL cost shifting actions. Again, it should be noted that industry (IND) and the level of responding manager (LMAN) were not broken out into their categories due to a lack of significance, so the two variables are treated jointly for the purposes of my study.

Traditionally, aside from emergency and safety net scenarios, the United States relies on the private sector to administer social and health benefits to its workers, whether at the option of the employer or mandated by the government. The lack of paid leave provisions forces American workers, especially low-wage, frontline workers to choose between a paycheck and caring for a loved one (Ansel & Boushey, 2017, Ghilarducci & Farmand, 2020). For firms, PCL mandates often create hard-to-measure administrative and cost burdens (Phillips, 2002) – burdens exaggerated by the COVID-19 pandemic.

Leveraging Kitching et al. (2015), the expectation for my study was for firms to engage in dynamic responses to regulation, but in ways explicitly affect employees. Within a dynamic response to legislation, firms were expected to shift (perceived) additional costs of PCL to employees (Gruber, 1994), which could have surfaced in a variety of ways: from changes to the head count like layoffs or reclassifications (i.e. part-time to full-time employees or vice versa) or a change in compensation and benefits such as wage cuts or additional paid leave.

My analysis confirms the general assumption that PCL laws are perceived to be costly: 54.6 percent of responding firms reported to be “somewhat concerned” or “extremely concerned” about the costs associated with PCL regulations like NYS-PFL, MA-PFML, and FFCRA. Yet, the idea that paid care leave policies and subsequent costs will inflict detrimental employee management outcomes on its beneficiaries is, in general, unsupported by my study. Overall, companies that reported cost concerns from PCL mandates (PCOST) were more likely to enact implicit actions, such as an increase in prices of the company's goods and services (IPRICE), instead of direct or explicit outcomes such as a decrease in wages or benefits. However, it should be noted that an implicit outcome may have ripple effects that affect employees – closing physical locations (CLOC) typically results in fewer hours, wages, and/or employees. For reference, Table 18 in the Appendix is a tally of all significant outcomes and iterations of the data, with special notation for PCOST significance.

In my analysis, PCL cost concern (PCOST) acts as a statistically significant predictor of select results, such as the ability of large companies to expand company-sponsored paid leave programs (MPFL) or the increase in independent contractors (IINDC) at small firms. The prevalence of firm characteristics, like company size (SIZE), postulates that many of the reported outcomes may be caused by the COVID-19 pandemic or general economic conditions instead of PCL cost concerns. Thus, I estimate that there is no evidence that PCL cost concerns are large enough to cause detrimental effect on employees or business continuity. Instead, PCL cost concerns could influence company operations in a way similar to general cost concerns and potentially in ways indirectly damaging to employees. Managers carry assumptions or preconceived notions that PCL is costly, but my study shows that those concerns do not carry a large enough magnitude to be harmful to (existing) employees in a statistically significant way. Although, the lack of statistical significance in industry (IND) as a covariate is worth reviewing in more detail. My study used NAICS codes as a categorical variable, which produced low reference points for certain industries like Wholesale Trade (N=2). Further, industry codes may not be reflective of the hybrid nature of newer companies or conglomerates. Splitting the data by LMAN may be an interesting step in further research to examine and magnify the role of cost concern across varying levels of management (and company structure).

FFCRA is an important focal point of my study, but analysis specific to users of the policy was not significant for several reasons. Aside from the power issues with the small number of data points, and despite the disclaimer completed by all 306 managers serving as representatives, 17 of the 40 companies that reported using FFCRA also reported a firm size of more than 500 employees – as FFCRA is not applicable to companies with less than 500 employees, the results seem inaccurate. The discrepancy may be addressed by FFCRA guidelines, as companies that are public agencies or other non-Federal government units were subject to FFCRA, regardless of company size. The survey did not have an indicator for public versus private sector companies, but public sector businesses are included in the Qualtrics Small Business Panel. As such, some of the respondents, may be public sector employers. The existence of FFCRA users that seemingly do not qualify for eligibility is a concerning outcome that requires more attention.

Firms may identify as large companies with more than 500 employees, but because of franchising agreements or other hierarchical structures, the firm should be considered small, with less than 500 employees (or vice versa). For example, in July 2020, the Wage and Hour Division of the Department of Labor investigated a Subway franchise location in Irvine, California after an employee complained of being denied paid sick leave under FFCRA (Kay, 2020). The Subway location in Irvine was managed by a franchisee. The Department of Labor considers two or more entities as separate employers, and therefore subject to different employee counts, unless the firms meet the integrated employer test under FMLA, which indicates firms should not be considered separate entities if there is common management, interrelation between operations, centralized control of labor relations, and/or a degree of common ownership of financial control (United States Department of Labor, 2012, 2020). Additionally, the way the employee count question was presented to survey takers was a range that made it impossible to account for companies on either side of the 500-employee mark, and therefore impossible to identify if companies could easily manipulate headcount estimates to use the benefit, or conversely, avoid the mandate.

The issues of noncompliance and eligibility highlighted by FFCRA users adds to an already extensive array of literature outlining issues with American PCL programs. FMLA is notoriously only eligible to approximately 50 percent of American workers (Kelly, 2010; Ruhm, 1997), which, in some cases, extends to FFCRA eligibility as well. In the case of US state-based PCL, residents of states such as California and New Jersey were unaware of the existence of the benefit, let alone how to file a claim (Lerner & Appelbaum, 2014; White et al., 2013). For FFCRA, Long & Rae (2020) noted that roughly 17.7 million essential workers were not eligible for FFCRA – about 70 percent of those essential workers were women and about 35 percent were people of color. Furthermore, Schriefer & Born (2020) noted the increase in Employment Practices Liability (EPL) claims associated with FFCRA. It is expected that the trends of non-compliance and/or ineligibility would continue in the United States as PCL, along with many other safety net-style social welfare programs, which are means-tested (Garland, 2016).

Thus, the costs associated with PCL mandates may have implications for employees, but perhaps it is best to predict the effects of PCL the same as any other cost concern a company faces. The takeaway regarding the statistically significant correlation between PCOST and an increase in prices is important to consider as PCL becomes more popular in the United States, and potentially part of the larger US and global policy landscape. If firms are more likely to offset (perceived) costs of paid care leave programs, or any mandated benefit, through actions like price increases, future policies can take such operational effects into account. It should be noted that although certain outcomes were classified as implicit or non-employee-facing, events like site closures would also be expected to lead to changes in headcount if the employees are not re-assigned to other existing locations.

Furthermore, the significance of firm characteristics, instead of (or in addition to) cost concern, as predictors of the 19 outcomes tracked in my study indicate that many of the changes businesses experienced in 2020 are related to (COVID-19) economic conditions. Per Bana et al. (2018), firm characteristics can be indicative of the company culture, which may be the larger driving factor versus any specific policy provision. The data may be useful in a larger study of business disruptions and management. In my study, characteristics such as location and existing internal PFL increase the probability of actions such as changes in number of locations and operating hours, in addition to the way companies measure productivity of their employees – customer retention rates also proved to be a common significant result across firms that reported PCL cost concerns. Thus, if it is possible to predict which companies offset PCL costs and how those cost shifts would manifest, future business disruptions can be better managed.

It is hard to discuss any PCL policy usage without acknowledging the types of employees that are using paid care leave: caretakers. The role of caretaker is typically influenced by cultural standards – most caretakers worldwide are women and/or people of color (Adler & Lenz, 2016; Nataraj et al., 1998). Although my study did not specifically ask about the characteristics of employees taking care leave, the assumption remains that caretakers are women, and therefore has stratified implications for workers. My study not only establishes the presumption of cost concern regarding PCL laws, but it also adds to existing literature suggesting many Americans who seek benefits such as PCL are being excluded and detrimental outcomes such as gender gaps continue to occur despite the intentions of such policies (Ginja et al., 2023). More evidence on which employees are using the policies firms find costly would be beneficial to the overall analysis.

Cost proves to be an incentive. PCOST acts as a proxy variable, but is an imperfect measure of the magnitude of PCL concerns – it cannot be inferred that “unconcerned” managers do not incur costs. The inclusion of firm characteristics such as company size, industry, location, and existing company PCL establishes a good control for the disruptions caused by the larger COVID-19 event. Although the job title of the responding manager is included in the model, any differing reasons for reporting PCL cost concerns did not produce statistically significant results. However, the higher likelihood of implicit outcomes may result from a trend that my data did not track: litigation concerns.

The Equal Employment Opportunity Commission (EEOC) has seen a dramatic rise in retaliation claims in the last 20 years, increasing from 27.1 percent of all charges in 2000 to 55.8 percent of charges in 2020 (United States Equal Employment Opportunity Commission, 2020). Employers may have chosen not to engage in employee-focused cost shifting mechanisms to avoid potential litigation, which, “may be increasing employers’ liability exposure, further complicating the legal considerations for employers as they navigate evolving guidelines for employee safety in the midst of the [COVID-19] pandemic,” (Schriefer & Born, 2020: 13).

There is more work to be done focusing on US firms to understand a type of PCL implementation that would be helpful to all company stakeholders, at a minimal cost, assuming PCL policies would be beneficial for all parties. The results provide an important step in the evaluation of PCL policies and how companies perceive PCL mandates. The research design is intentional as it can be replicated in future instances of new PCL programs regardless of the state of the economy. As a study of policy evaluation, the effectiveness interpreted here is time inconsistent, as respondents may evaluate their circumstances in a different way outside of the COVID-19 pandemic, at different periods of time (Fitoussi & Stiglitz, 2013). The usage implications of FFCRA, along with the takeaways on firm behavior acquired from the regression analysis, establish meaningful contributions to the literature. Gathering evidence to analyze long-term policy success is a timely process, which has only just begun in the United States.