Organizational Communication and Workplace Dynamics: The Impact of Paid Family Leave Policies During the COVID-19 Pandemic

2.1 Paid Family Leave (PFL) Policy

Recent literature demonstrates that PFL policies can have a positive impact on labor force attachment without hurting productivity or turnover rates. Rossin-Slater (2017) and Appelbaum and Milkman (2011) found that PFL policies do not endanger workplace productivity because firms would simply pay for overtime or train new hires to take over the marginal production responsibilities of those on PFL. At the same time, PFL policies have been shown to strengthen labor force attachment for parents, increase the likelihood of women returning to work, increase wages for women, and reduce costs for employee replacement (Bana, Bedard, and Rossin-Slater 2020; Davison and Blackburn 2022; Rowe-Finkbeiner et al. 2016; Rossin-Slater 2017). Indeed, these findings have led to the tentative consensus in economic literature that PFL policies may potentially boost the U.S. economy.

Such benefits of PFL policies proved beneficial for employees and employers alike during the pandemic. For example, PFL policies helped employees, especially women and parents, with job continuity. Rossin-Slater (2017) reported that PFL strengthened retention rates for women for several years after childbirth, while Bana, Bedard, and Rossin-Slater (2020) argued that PFL led to an increase in labor force attachment for parents. Employed women and parents who take PFL during the pandemic also have the advantage of ensuring the health and safety of loved ones without sacrificing their entire wage bill and employment security (Bartel et al. 2022).

PFL policies implemented during the pandemic also enabled employers to retain their employees. Policy benefits such as continued wages and time off are forms of investment for employers. In return for all the costs associated with sponsoring an employee on PFL, such as missing expertise, output shortage, overtime pay, and training costs, employers attract more job applicants, retain valuable employees, and save other various short and long-run costs when employees resume work (The Council of Economic Advisers 2014). That being said, the fragility of small businesses during the pandemic posed a threat to the payoff of PFL policies for employers. Because roughly 30 % of U.S. small businesses fear they will not survive the pandemic, immediate costs of PFL policies may have larger than usual negative effects on this specific employer population (Brooks 2021).

Only a few studies have explored the effect of PFL on employee morale. On the topic of paid parental leave, Brenøe et al. (2020) posited that employees in Denmark taking paid leave incur negligible costs related to their attitudes towards firm and coworkers. Although coworkers saw temporary increases in working hours, earnings, and likelihood of employment, there were no measurable effects on their well-being in the workplace. Lerner and Appelbaum (2014) reached a similar conclusion after conducting a study on employee morale corresponding to the New Jersey Family Leave Insurance, first passed in 2008 and modified in subsequent years. New Jersey’s insurance plan, in its current form, provides workers “cash benefits for up to twelve weeks to bond with a newborn or newly placed adoptive, or foster child, or to provide care for a seriously ill or injured loved one” (Official Site of the State of New Jersey 2020). The study, corroborated by Ramirez (2012), reported no evidence of employees resenting their coworkers who took leave.

2.2 Fringe Benefits, Employee Morale and Productivity

Organization research studies have found that fringe benefits such as PFL correlate not only with employee morale, but also with employee productivity. A study by Chukwudumebi and Kifordu (2018) studied employee surveys from Shell Petroleum and Development Company Warri in Southern Nigeria, which asked employees about how fringe benefits influence their morale and productivity. Survey results and a Chi-square statistical method showed a significant positive correlation between morale, productivity, and fringe benefits such as cash bonuses, better living conditions, housing allowances, overtime pay, health schemes, pension plan, and motor vehicle allowance. That being said, Chukwudumebi and Kifordu observed that fringe benefits can represent a double-edged sword for employers. On one hand, fringe benefits can attract, retain, and motivate employees. On the other hand, fringe benefits cut into production costs.

Setting aside fringe benefits for the moment, literature suggests that morale has an inherent relationship with productivity (Weakliem and Frenkel 2006). One common perspective is that morale’s influence on productivity suffers from diminishing returns. That is, increases from low to moderate morale levels have more effect on productivity than increases from moderate to high morale levels. Weakliem and Frenkel (2006) derived said reasoning from Perrow (1986), who speculated that employees with unusually high levels of morale may contribute less to productivity than their low or moderate-level coworkers. For example, employees may offer their best service to a customer. While they spend more time on each customer, they will serve fewer customers than employees who aim for adequate service. Such a threat of productivity reduction raises incentive for employers to limit the scope of exceptionally good performance. Employers who are unaware of morale’s non-linear influence on productivity run the risk of dipping below optimal production levels on top of increasing costs for incentivizing employee morale. They also run the risk of increasing workplace costs coming from employees of high morale who implement expensive measures to provide their best customer service (Shister 1950).

Another perspective of employee morale’s influence on productivity is that certain sources of morale have a larger effect on productivity than other sources. Frenkel (2003) argued that morale relating to emotional security and respect in the workplace enable workers to perform more effectively. Productivity could be enhanced by a “high road” approach whose features include “employment security, teamwork, employee participation in decision making, and a relatively egalitarian status system” (Frenkel 2003, 141). Their argument rests on the assumption that workers perform more effectively when they feel secure and respected. On the other hand, Hodson (2001) and Organ (1988) attribute morale relating to “employee citizenship behavior” to productivity increase. Yet another argument is from Vikander (2009), who observed, from a game theoretical perspective, that the morale of altruist employees can increase productivity while that of egoist employees can decrease productivity. Firms should consider the proportion of altruist to egoist employees at hiring time – measures to encourage morale may actually convince opportunistic team members to imitate and later take advantage of cooperative coworkers. These team members may then slack off, thereby lowering their own productivity.

This study explores the relationship between PFL policies implemented by private U.S. employers and employee morale during the pandemic. Unlike aforementioned studies on PFL, I sample across states unaffected by sub-national PFL policies. Additionally, whereas these studies inferred employee morale from managerial and employee surveys, I infer employee morale from company reviews on Glassdoor. While this study shares the objective of exploring the relationship between PFL policy and employee morale, it differs from existing research with regard to both scope and design. I aim to contribute an empirical analysis on how company-led PFL policies affect employee morale, driven by big data and conducted at the national level.

3.1 Company Reviews as a Measure of Employee Morale

Vikander (2009, 2) defines morale as “an ambiguous notion, related to trust in colleagues, a willingness to cooperate, a sense of common purpose and a belief that outcomes are fair.” Consequently, company reviews on Glassdoor are considered to effectively reflect employee attitudes and align with this definition of morale. To elucidate this further, I will examine Glassdoor’s guidelines for its platform community and reviewers.

Glassdoor positions itself as a platform where administrators maintain a hands-off approach to all reviews, except those originating from untrustworthy sources, including coerced employees or automated systems. According to its website, Glassdoor’s mission is as follows: “Glassdoor strives to be the most trusted and transparent place for today’s candidate to search for jobs and research companies” (Glassdoor 2021h). Furthermore, they utilize proprietary technology filters and algorithms to detect attempted abuse and manipulation of the system. According to community guidelines,

We [administrators] love it when companies encourage their employees and job candidates to share honest reviews on Glassdoor, but do not offer incentives in exchange for reviews. We will remove positive reviews where we have evidence that employees were compensated and/or coerced into leaving a review (Glassdoor 2021h).

These community guidelines establish a standard for administrative action and data integrity. I utilize this data, assuming that data extracted from Glassdoor reviews is free from coercion and administrative bias, thereby reflecting employees’ genuine perspectives.

Glassdoor also sets review guidelines to preserve a “helpful, balanced, and authentic community” (Glassdoor 2021h). First, users are required to certify their employee relationship with companies when posting any content. Second, reviews “should be truthful and constitute your own personal opinion and experience with your current or former employer.” Third, reviews “should be original – no substantial quoted material from other sources, including (but not limited to) websites, e-mail correspondence, other reviews, etc.” Fourth, “Each individual is allowed one review, per employer, per year, per review type.” Fifth, Glassdoor encourages reviewers to “think about work from a few perspectives and include both a pro and a con to provide a balanced review” (Glassdoor 2021h). Every review comes with a “Pros” and “Cons” section. If a reviewer chooses not to contribute to a particular section, they may write phrases such as “nothing for now.” Examination of the dataset indicates that most reviews contain significant content in both the “Pros” and “Cons” sections. I leverage this robust data with the assumption that reviewers submit original work, and that any sub-collection of reviews is representative of a diverse range of reviewer perspectives.

For the statistical analysis and its interpretation, I rely on the assumption that Glassdoor provides datasets that are reflective of reviewers’ insights into their employers and workplaces. Specifically, the difference-in-differences approach mitigates the impact of biased or inauthentic reviews. If some reviews were subject to outside manipulation, coercion, overrepresentation of certain reviewers, or non-original work, the influence of these factors is mitigated by the difference-in-differences approach.

3.2 Keywords

I analyze Glassdoor reviews to quantify the frequency of specific keywords that are closely associated with employee morale and PFL policies. This keyword count serves as a measurement and this primary variable in the difference-in-differences statistical test. The keyword count in the “Pros” section is interpreted as a measure of positive employee morale, analogous to a positive discrete variable. Conversely, the keyword count in the “Cons” section is interpreted as a measure of negative employee morale, analogous to a negative discrete variable.

Table 1 presents a collection of thirty-five keywords relevant to PFL policy and employee morale. Keywords such as “cooperat” and “productiv” are truncated to encompass words sharing a common root, such as “cooperate,” “cooperative,” “cooperativeness,” “productive,” and “productiveness.”

The selection of these keywords is based on quotes directly related to PFL policies implemented by private U.S. employers during the pandemic, sourced from media outlets including The New York Times. For example, in response to Facebook’s policy offering a specific PFL policy for parents, a Twitter user wrote that recent Facebook policies “have primarily benefited parents” (Wakabayashi and Frenkel 2020). One of The New York Times article further states: “An employee commented in a video feed that it was unfair that non-parents could not benefit from the same leave policy provided to parents” (Wakabayashi and Frenkel 2020). Such quotes reflect sources of employee complaints for PFL policies. After measuring individual morale from each review, I sum across all reviews with at least one hit to estimate a company’s net employee morale. Occasionally, a review will have a duplicate or use a keyword irrelevant to employee morale or family leave. A manual search for such false positives returns an approximate 1:1 ratio across time frames – the effect of those in time frame 2 cancels out the effect of those in time frame 1.

3.3 Company Sample

Data were collected from Glassdoor reviews for twenty-six Russell 1,000 companies (Table 2) representing the software, retail, food, technology hardware, and telecommunications industries, as identified by JUST Capital’s COVID-19 Corporate Response Tracker (2020). The tracker provides information on whether companies offered paid leave, hazard pay, adjusted hours of operation, and other pandemic-related.

Across the six industries represented on the website, the thirteen largest companies were selected that implemented a new PFL policy during the treatment period (March 11–April 19, 2020). Here, “largest” refers to the number of employees. Furthermore, it was confirmed that these companies exceed 10,000 employees to exclude companies potentially affected by the federal Families First Coronavirus Response Act (FFCRA). The resulting thirteen treatment companies in Table 2 have implemented a new PFL policy, are equally unaffected by FFCRA, and correspond to a large number of employee reviews on Glassdoor. They make up the treatment group.

Subsequently, the control group was identified using the same selection criteria for thirteen companies that did not implement new PFL policies either during the treatment period or in the subsequent timeframe. Due to the uneven distribution of companies qualifying for the treatment group across industries, certain industries are consequently underrepresented in the final sample. Nevertheless, within each industry, the sample size is approximately balanced between the treatment and control groups, with a maximum difference of one company (Glassdoor 2021a, 2021b, 2021c, 2021d, 2021e, 2021f, 2021g, 2021i, 2021j, 2021k, 2021l, 2021m, 2021n, 2021o, 2021p, 2021q, 2021r, 2021s, 2021t, 2021u, 2021v, 2021w, 2021x, 2021y, 2021z, 2021aa).

3.4 Treatment Period

Companies in the treatment group have varying dates on which they implemented a new PFL policy. To facilitate pre- and post-treatment comparisons, a treatment period is defined spanning March 11, 2020 to April 19, 2020, corresponding to the period when treatment companies implemented their PFL policies. The pre-treatment period commences on September 7, 2019 and continues until March 10, 2020, while the post-treatment period commences on April 20, 2020 and continues until November 22, 2020.

3.5 Location

Although the twenty-six companies in the sample are not subject to the FFCRA, their employees may still qualify for sub-national PFL policies at the state, county, or city level. International employees may also qualify for PFL policies in their home countries. Consequently, Glassdoor reviews were filtered by geographical location to isolate the effect of company-specific PFL policies from the influence of sub-national PFL policies.

U.S. reviews were filtered from specific U.S. states, counties, and cities using a combination of Glassdoor’s built-in filters and MATLAB. This filtering process ensures that reviewers in the dataset are primarily influenced by their company’s PFL policy. Table 3 details the U.S. locations excluded from the dataset due to the presence of sub-national PFL policies (Bipartisan Policy Center 2020).

3.6 Dataset Creation

For efficient dataset creation, the Chrome extension Web Scraper (2019) was utilized to extract company reviews and organize them into CSV spreadsheets. For each company, a spreadsheet was generated by Web Scraper, categorizing each review by its webpage link, “Pros” section content, “Cons” section content, subject, date, and location. These twenty-six spreadsheets were then processed to construct the final dataset, summarized in Table 4.

3.7 Statistical Test Variables

From these spreadsheets, four keyword quantification methods were derived for each company: the raw count of keywords in “Pros” and “Cons” reviews, and the proportion of keywords in “Pros” and “Cons” reviews relative to the total keywords. For each of these four keyword measures, paired t-tests were conducted to assess the statistical significance of changes in keyword counts across the defined timeframes. Difference-in-differences (DID) tests were also performed to assess the statistical significance of the difference in keyword count changes between the treatment and control groups across timeframes (Table 5).

3.8 Paired t-Test

A paired sample t-test, or simply a paired t-test, assesses whether the mean of the differences between paired observations is statistically significantly different from zero. In the context of this study’s t-tests, for each company, there are paired observations: one from the pre-treatment period and one from the post-treatment period. Each pair of observations consists of the keyword counts for a single company during the pre-treatment and post-treatment periods.

The statistical procedure for the two-tailed paired t-test has two hypotheses: the null hypothesis, and the alternative hypothesis:

In this analysis, the test statistic from the paired t-test follows a t-distribution with 12 degrees of freedom (df = n − 1, where n = 13 companies). A statistically significant t-statistic (i.e., a t-statistic sufficiently far from zero) suggests a statistically significant difference in mean keyword counts between the pre-treatment and post-treatment periods within the sample. The direction of this difference is indicated by the sign of the t-statistic. A negative t-statistic implies a decrease in the mean keyword count from the pre-treatment to the post-treatment period, while a positive t-statistic indicates an increase. I infer correlation from these paired t-tests instead of causality.

3.9 Difference-in-Differences t-Test

The difference-in-differences (DID) test compares observations between the treatment and control groups, with each group having observations from both the pre-treatment and post-treatment periods. The treatment period is defined as March 11–April 19, 2020, corresponding to the period during which treatment companies implemented their new PFL policies. The treatment group consists of companies that adopted a new PFL policy during the treatment period, and the control group consists of companies that did not adopt a new PFL policy during the treatment period. Similar to the paired t-test, the difference in differences test has two hypotheses: a null hypothesis and an alternative hypothesis:

The resulting test statistics follows a t-distribution with a conservative 12 degrees of freedom, a non-zero test statistic indicates a difference in mean keywords between time frames for the treatment and control group. A negative test statistic indicates a reversal in the directionality of the effect. Any such difference in the difference of means is not causal. I can only infer a correlation between the treatment period and changes in keyword counts, and consequently, a correlation between the treatment period and any inferences about employee morale.

4.1 Polarization of Employee Morale

Table 6 demonstrates that the treatment period is associated with a polarization of PFL-related feedback for companies that implemented PFL policies as well as companies that did not implement PFL policies. Notice the difference of means column, which calculates the average change in keyword counts from the pre-treatment to the post-treatment period. Rows 1 and 2 show an increase in negative feedback for the average company, regardless of policy implementation. At the same time, rows 3 and 4 show an increase in positive feedback regardless of policy implementation. This suggests that a general increase in user engagement on Glassdoor likely contributes to the observed polarization effect in PFL-related feedback.

Detailed analysis shows an unequal rise in positive and negative keyword frequencies, indicating a shift in the distribution of PFL-related feedback. The proportion of negative feedback drops slightly, while the proportion of positive feedback rises slightly. Concurrently with the increased polarization, there is a shift in the distribution of PFL-related feedback that is skewed towards positive feedback.

The observed polarization of feedback for treatment companies may reflect the heterogeneous impact of PFL policies on employees. Potential benefits for policy takers include assured paid time off, job security, and peace of mind. Conversely, potential benefits for policy non-takers include incentives for promotion, overtime pay, and a sense of normalcy amidst the turbulent pandemic environment. Additionally, altruistic policy non-takers may derive happiness from their coworkers’ peace of mind. Future-oriented employees may also derive utility from a sense of security that PFL policies provide. Conversely, potential downsides for policy takers could include the foregone benefits of not taking leave. Potential downsides for policy non-takers include disutility arising from mandatory overtime, envy, and isolation if the workplace employee population changes drastically. I aim to explain the reasons most concerned with the polarization in feedback in the next chapter section.

Furthermore, polarization of feedback for control group companies may reflect the broader conditions of the COVID-19 pandemic and the heterogeneous impact of existing PFL policies or the absence of new PFL policies on employees. The increase in negative PFL-related feedback may reflect a general underlying dissatisfaction stemming from the fear and anxiety associated with the pandemic. Employees may also express dissatisfaction due to the lack of PFL policies or inadequate PFL provisions. For example, one Georgia Kroger employee wrote in June 2020, “Management isn’t consistent enforcing policy” (Glassdoor 2021q). A Tennessee Kroger employee wrote a month later:

Spends money running PR stunts and framing their bare-minimum gestures for employees as kind gestures to increase public goodwill, while looking for ways to cut hours, fire employees, and remove benefits at every opportunity even during a pandemic with booming business. (Glassdoor 2021q)

Conversely, the increase in positive feedback may stem from satisfaction with existing PFL policies, other employee benefits, or a sense of camaraderie among colleagues during the pandemic. As another Kroger employee from Texas wrote in July 2020, “Flexible Health Insurance is super affordable” (Glassdoor 2021q).

4.2 Polarization Magnitude

Table 7 demonstrates a greater increase in the polarization of PFL-related feedback for companies implementing PFL policies during the COVID-19 pandemic compared to those that did not. The difference-in-differences (DID) t-statistic for “Keywords in Cons Reviews” indicates that companies implementing PFL policies experienced a significantly larger increase in negative feedback compared to those that did not implement such policies during the pandemic. These companies also demonstrated a larger increase in positive feedback, as indicated by the difference-in-differences (DID) statistic of the method “Keyword in Pros Reviews.” Indeed, companies with new PFL policies receive more polarized feedback than companies without.

Conversely, companies with new PFL policies exhibited a smaller change in the distribution of feedback. As previously discussed in relation to Table 6, the general trend indicated a decrease in the proportion of negative feedback and an increase in the proportion of positive feedback across all companies. Row 3 of Table 7 indicates that the proportion of negative feedback decreased to a lesser extent for treatment companies compared to control companies. The complementary difference-in-differences t-statistic would have the same absolute magnitude but with the opposite sign, and would indicate that the proportion of positive feedback increased to a lesser extent for treatment companies compared to control companies. Thus, PFL policies appear to be correlated with a smaller shift in the distribution of PFL-related feedback.

4.3 Keywords’ Driving Forces

Keywords pertaining to “supervisors and management”^[1] and work-life balance^[2] are key drivers of the increase in negative feedback for companies implementing PFL policies during the COVID-19 pandemic. Figure 1 illustrates the trend of negative feedback over time for companies implementing PFL policies, categorized by keyword. These keywords exhibit a consistent increase post-treatment, with the exception of a temporary dip in July 2020. The keyword count peaks in October 2020, reaching 74 % of the total negative feedback at that point. A similar figure for companies not implementing PFL policies during the pandemic is included in the Appendix. Keyword trends remain largely consistent between treatment and control companies.

Figure 1:

PFL-related negative feedback (treatment group).

A key observation from Figure 1 is that most employees who like to post or feel strongly enough to post about PFL blame management more than other factors for their dissatisfaction. For example, the negative feedback related to supervisors in October 2020 is sevenfold the negative feedback related to fairness, familial responsibilities, and coworkers. Thus, a strong correlation appears to exist between negative PFL-related feedback and employee dissatisfaction with management. Although other factors also show an increase in keyword counts post-treatment, Figure 1 indicates that a greater proportion of employees attribute their PFL-related disutility to management.

Figure 2 illustrates the trend of positive feedback over time for companies implementing PFL policies, categorized by keyword. The increase in positive feedback is primarily driven by keywords related to “coworkers.” These keywords exhibit a consistent increase post-treatment. The keyword count peaks in October 2020, reaching 46 % of the total positive feedback at that point. A similar figure for companies not implementing PFL policies during the pandemic is included in the Appendix. Keyword trends remain largely consistent between treatment and control companies, with the exception of a temporary dip in “coworker”-related keywords in July for control companies. The increase in positive feedback associated with “coworkers” suggests a greater tendency among employees to attribute their PFL-related utility to their colleagues. For instance, in October 2020, positive feedback associated with “coworkers” was twice as large as positive feedback related to “supervisors and management,” and sixteen times larger than positive feedback related to “policies.” A plausible explanation is that employees demonstrated increased empathy and altruism towards each other within the sensitive and precarious pandemic environment. Consequently, their positive feedback regarding PFL policies may reflect strengthened camaraderie. There appears to be a strong relationship between positive PFL-related feedback and employee satisfaction with colleagues. While other factors also see a rise in count in the post-treatment period, Figure 2 indicates that a greater proportion of employees attribute their PFL-related utility to their colleagues (Figures 3 and 4).

Figure 2:

PFL-related positive feedback (treatment group).

5.1 Workplace Morale

The observed polarization of PFL-related feedback suggests a corresponding polarization of PFL-related workplace morale. Consistent with the methodology outlined in Section 3, PFL-related feedback is interpreted as a proxy measure of employee morale. Increased negative PFL-related feedback suggests a decline in workplace morale post-policy implementation. Conversely, increased positive PFL-related feedback suggests an improvement in workplace morale post-policy implementation. These divergent trends likely stem from variations in employee preferences and highlight the polarization at the extremes of the workplace morale distribution.

Since Glassdoor does not differentiate between employment statuses, reviews from former employees may introduce confounding factors into the observations of workplace morale. Although the difference-in-differences (DID) methodology in this study mitigates the influence of reviews from past employees through cancellation effects, it does not specifically account for reviews from employees currently on PFL. It is plausible that employees utilizing PFL experience improved well-being and higher morale. PFL, as an optional employee benefit, is likely utilized by rational decision-makers seeking to maximize their individual utility. As observed in the preceding section, the distribution exhibited a positive shift. Excluding reviews from PFL-takers from the dataset would likely shift the distribution of PFL-related feedback towards a more negative direction. Such an exclusion might reveal that PFL policy implementation is associated with a more pronounced decrease in workplace morale and a less significant increase in workplace morale.

These findings introduce a nuanced perspective to existing literature, which has generally reported minimal impact of PFL policies on average workplace morale. Discrepancies in findings may arise from methodological differences. For instance, Milkman and Appelbaum (2014) utilized surveys to study a state-level PFL policy, whereas this study analyzes textual data reflecting firm-level PFL policies nationwide within a natural experiment framework. While this study does not determine the average relationship between PFL policy and workplace morale, it does identify this relationship specifically among employees who are inclined to post reviews on platforms like Glassdoor. Consequently, this study contributes to the existing literature by providing insights into the polarization of workplace morale at its extremes.

5.2 Workplace Teamwork

PFL policies implemented during the pandemic may potentially foster workplace disharmony or disrupt teamwork among employees, based on principles of game theory and employee-management relations. Elevated morale stemming from PFL policies could incentivize opportunistic employees to exploit their colleagues (Vikander 2009). The subsequent erosion of trust could diminish camaraderie and teamwork. Furthermore, the findings indicate a tendency among employees to attribute PFL-related disutility to management. Moreover, a significant proportion of employees taking leave may contribute to instability in colleague dynamics. Consequently, workplaces aiming to maintain positive workplace relationships should anticipate potential disruptions related to employee composition, the polarizing effects of PFL policies, and the prevalence of PFL utilization.

Despite these challenges, PFL policies may also foster optimism and cooperation among employees during the pandemic. During periods of adversity, such as a pandemic, individuals may unite more closely, and management may prioritize enhancing teamwork to maintain performance (Tannenbaum et al. 2020). One AT&T agent from Idaho writes in April, 2020:

This is the best management team I’ve ever worked for… With covid 19 they set up paid time off before the government mandated it, and extended it to 4 weeks. Although no one has had it they closed 1 day to sterilize the center and are maintaining all CDC recommendations on social distancing etc. And as a high risk person they are setting me up to work from home. I intend to retire from here. (Glassdoor 2021d)

Furthermore, as indicated by Figure 2 and supported by findings in Section 4, the implementation of new PFL policies during the pandemic appears to correlate with increased camaraderie among colleagues. Employees generally tend to attribute PFL-related utility to colleagues, while attributing a smaller proportion of PFL-related disutility to them. These observations align with previous research that has found no indication of employees resenting colleagues who utilize PFL (Lerner and Appelbaum 2014; Ramirez 2012). Specifically, among employees expressing strong sentiments regarding PFL policies during the pandemic, there is minimal evidence of resentment towards colleagues utilizing PFL. On the contrary, employees appear to exhibit increased collegiality following the PFL policy implementation period.

5.3 Workplace Productivity

Weakliem and Frenkel (2006) previously established a correlation between increased workplace morale and enhanced productivity. This study indicates that PFL policy implementation results in an increase in both positive and negative workplace morale. This finding implies that workplaces likely experienced a significant polarization of productivity. Consistent with the observations regarding workplace morale, these findings concerning workplace productivity introduce further complexity to existing literature, which has generally reported minimal impact of PFL policies on workplace productivity.

According to existing literature, the impact of high morale on productivity is subject to diminishing returns, and excessively high morale may lead to increased workplace costs due to employee actions (Perrow 1986). Therefore, management should consider this potential negative relationship before implementing additional PFL benefits or prioritizing morale enhancement initiatives for all employees. Morale polarization, as observed in this study, may actually be more beneficial for productivity than uniformly high morale. Workplaces seeking to optimize productivity may consider leveraging PFL policies to foster morale, but within a measured and strategic approach.