The Effects of Family Responsibilities Discrimination on Public Employees’ Satisfaction and Turnover Intentions: Can Flexible Work Arrangements Help?

Abstract

Discrimination against caregivers is a topic of interest for the public sector workplace. This study assesses the degree to which family responsibilities discrimination (FRD) can diminish work satisfaction and lead to intentions of leaving the public service. It also examines the effects of flexible work arrangements on work satisfaction and intentions to leave. Applying Mahalanobis Distance Matching, we examine the Canadian Public Service Employee Survey and find evidence that family status discrimination has some impact on employees’ intention to leave the public service and can diminish satisfaction with work arrangements. We also find that federal public servants who feel like work and family/personal obligations adversely affected their career progression were less satisfied with their work arrangements and were more likely to want to leave their positions.

Case Analysis: Equal Opportunity and Safety at PT. XYZ-Striving for Fairness in a Family Business

“…Talent mapping, as the basis of employee promotions, is conducted purely by considering objective measurements, such as competency, capability, and performance. And there is no segregation between family and nonfamily employees, as argued by Mullins, Charbonneau, & Riccucci (2020), the absence of segregation or discrimination will improve satisfaction and lower employee turnover.…”

Assessing the Promise and Performance of Agencies in the Government of Canada

“…The descriptive data are informative of general trends by organization type, but there are a number of other drivers of employee sentiment that must be controlled for in order to separate out the agency effects on the key variables of interest. This is primarily done via matching methods with pretreatment covariates of gender, age, job satisfaction, occupation groups, and region (consistent with Mullins et al, 2021), but the results presented below are multivariate regressions using the same covariates. While there are different views in the literature on how to proceed with estimates in matched samples, we take the advice of Ho et al (2007) that the analysis that you would have done before matching is the one that you should do after matching.…”

“…And while country-based or comparative analysis of agencies around the world have not yielded any definitive answers with respect to agencification trends and organizational innovation and efficiency, until now we have had no basis to make claims, positive or negative, about what is happening in Canada in a broad sense. This study aimed to address this gap by drawing on high-quality data available from the Government of Canada, responding to recent efforts to make better use of PSES micro data (Cooper and Turgeon, 2021; McGrandle, 2019; Mullins et al, 2021). The data do not include provincial government departments or agencies but represent an original and systematic first look into agency organizational dynamics in Canada.…”

“…The quasi-experimental method of MDM is utilized in this study, which is increasingly applied in the analysis of large government employee surveys (Kim and Lee, 2020; Mullins et al, 2021). Quasi-experimental methods such as matching are useful for examining the effect of a particular experience (for example, working in an agency) when a randomized experiment is not feasible.…”

Some Parents Survive and Some Don't: The Army and the Family as “Greedy Institutions”

“…As family structures evolve and more families face work–family conflict (WFC), family support policies have become an important personnel management tool in the public sector. Family‐friendly policies enhance job satisfaction and performance, and flexible work arrangements have been linked to less WFC and reduced intention to leave (Feeney and Stritch 2019; Kim and Wiggins 2011; Mullins, Charbonneau, and Riccucci 2021). However, personnel management practices vary greatly among public agencies because organizational factors impact the policy adoption and implementation process (Kim and Wiggins 2011).…”