| Literature DB >> 35435073 |
Mrinal Chadha1, John Kennedy2, Nata Duvvury1.
Abstract
BACKGROUND: In low and middle-income countries (LMICs), violence against women and girls (VAWG) is rampant, primarily due to patriarchy. However, there is little understanding of its ripple effect in the workplace in LMICs. While recent studies in LMICs have attempted to understand the effects of VAWG on productivity using data collected from colleagues, survivors, or perpetrators, there is limited research on the employers' perspective of the impact of VAWG on productivity.Entities:
Keywords: businesses; economic costs; management; violence against women and girls; workplace violence
Year: 2022 PMID: 35435073 PMCID: PMC9465544 DOI: 10.1177/21650799221081262
Source DB: PubMed Journal: Workplace Health Saf ISSN: 2165-0799 Impact factor: 2.338
Managers Reporting Productivity Impacts Due to IPV and NPSV Experienced by Women Employees (n = 27).
| Productivity impacts | % of managers aware of IPV against female employees reporting impacts
| % of managers reporting impacts of those who witnessed NPSV/H |
|---|---|---|
| Arrived late by an hour or more | 8 (57) | 2 (15) |
| Left early by an hour or more | 5 (33) | 6 (43) |
| Missed appointments at work | 6 (43) | 6 (46) |
| Missed work for one or more days | 9 (60) | 6 (43) |
| Not as productive as normal | 9 (60) | 14 (82) |
| Had difficulties dealing with customers or clients | 8 (53) | 11 (65) |
| Had difficulties working with colleagues | 4 (33) | 14 (78) |
| A manager raised concerns about their work | 6 (43) | 6 (43) |
The base for percentages is not same as some managers mentioned “prefer not to say” and were thus excluded for that particular impact.
Gender, Length of Employment, and Gender of Managers (n = 74).
| Characteristics of Employees and Managers | Ghana | South Sudan | Pakistan | Total |
|---|---|---|---|---|
| ( | ( | ( | ( | |
| Gender (%) | ||||
| Male | 25 (68) | 27 (57) | 22 (78) | 74 (67) |
| Female | 25 (32) | 27 (43) | 22 (22) | 74 (33) |
| Length of employment (%) | ||||
| Less than 2 years | 0 (0) | 0 (0) | 5 (23) | 5 (7) |
| Over 2 and up to 5 years | 11 (44) | 6 (22) | 7 (32) | 24 (32) |
| Over 5 and up to 10 years | 8 (32) | 21 (78) | 5 (23) | 34 (46) |
| More than 10 years | 6 (24) | 0 (0) | 5 (23) | 11 (15) |
| Gender of employee’s manager (%) | ||||
| Male | 19 (76) | 24 (89) | 21 (95) | 64 (86) |
| Female | 6 (24) | 3 (11) | 1 (5) | 10 (14) |
Source. Authors’ own.
Bivariate Associations (n = 27).
| Productivity impacts | Intimate partner violence | Non-partner sexual violence/harassment | ||||
|---|---|---|---|---|---|---|
| Countries | Managers’ gender | Size of the firm | Countries | Managers’ gender | Size of the firm | |
| Arrived late by an hour or more | 8.2 (0.8) | 3.1 (0.5) | 3.8 (0.5) | 0.7 (0.2) | 0.4 (0.5) | 3.8 (0.5) |
| Left early by an hour or more | 8.6 (0.8) | 1.2 (0.3) | 1.6 (0.3) | 2.9 (0.5) | 0.05 (0.1) | 0.1 (0.1) |
| Missed appointments at work | 14 (1) | 1.8 (0.4) | 3.3 (0.5) | 0.3 (0.1) | 2.8 (0.5) | 4.4 (0.6) |
| Missed work for one or more days | 9 (0.8) | 3.5 (0.5) | 0.4 (0.2) | 0.9 (0.3) | 1.8 (0.4) | 2.1 (0.4) |
| Not as productive as normal | 11.4 (0.9) | 0.1 (0.1) | 4.4 (0.5) | 0.8 (0.2) | 0.8 (0.2) | 3.2 (0.4) |
| Had difficulties dealing with customers or clients | 11.6 (0.9) | 2.6 (0.4) | 2.9 (0.4) | 1.6 (0.3) | 1.9 (0.3) | 1.4 (0.3) |
| Had difficulties working with colleagues | 12 (1) | 1.2 (0.3) | 1.9 (0.4) | 0.3 (0.1) | 0.6 (0.2) | 1 (0.2) |
| A manager raised concerns about their work | 2.7 (0.4) | 1.8 (0.4) | 3.3 (0.5) | 0.9 (0.3) | 0.05 (0.1) | 1.2 (0.3) |
Source. Authors’ own.
Note. The figures in tables represent Pearson’s chi-square test with Cramer’s V in parentheses;.
As countries, managers’ gender, and size of the firm as well as productivity impacts are nominal variables, their association is established using Pearson’s chi-square test, with Cramer’s V showing the strength of association. For example, managers reporting employees “arriving late by an hour” is associated with countries (chi-square = 8.2, p < .05). This implies a statistically significant difference is observed across countries about this productivity impact.
p < .01. ** p < .05. * p < .1.