Absence of conflict of interest.
The study’s objective was to examine the impact of Salary History Bans (SHBs) on employment and earnings outcomes by sex and age.
The author conducted a difference-in-differences analysis to compare outcomes in a state that passed a SHB to a states without SHBs using a data source containing employment and earnings outcomes for approximately 60,000 households.
The study found a significant relationship between SHBs and an increase in overall female earnings and average weekly hours worked. It also found a significant relationship between SHBs and an increase in female earnings for populations older than 35 and an increase in hours worked for females under 35, but a decrease in hours worked for males under 35.
This study receives a low evidence rating. This means that we are not confident that the estimated effects are attributable to Salary History Bans; other factors are likely to have contributed.
Salary History Bans (SHBs)
Features of the Intervention
Salary History Bans (SHBs) prohibit employers from asking about applicant earnings history. Historically, the intent of these laws has been to reduce wage discrimination across genders by avoiding perpetuation of previous/current salaries. However, SHBs have previously been difficult to establish for all employers within a state, and usually only apply to specific employee populations, such as state/local employees. The study used California as the primary setting because the state implemented a SHB in 2018 that applied to all employers within the state. On January 1,2018, all employers in the state were restricted from seeking compensation history and restricted employers from basing salaries solely on previous salaries. The SHB law also required employers to provide salary ranges to the applicant if requested.
Features of the Study
The author used a nonexperimental design to compare the outcomes of participants in California with a SHB to a matched comparison group from states without SHBs. The treatment group consisted of data collected from the California sample, due to their adoption of a SHB in 2018. A comparison group was created with data from seven other states that did not have a comprehensive SHB law in place, creating a “synthetic California” to simulate outcomes if the SHB was not adopted. The comparison group included, Nevada, Arizona, the District of Columbia, North Carolina, Mississippi, Hawaii, Florida, and Oregon. The primary source of data contained monthly labor force statistics for approximately 60,000 households from 2006 to 2018. The author used statistical models to compare differences between treatment and comparison groups in earnings ratios, weekly earnings, hours worked, and hourly wages by sex and age.
Earnings and wage
The study found a significant relationship between SHBs and a decrease in the female to male state earnings ratio, suggesting an increase in overall female earnings.
The study found a significant relationship between SHBs and a decrease in female to male earnings ratio for populations older than 35 but no significant relationship for populations younger than 35.
The study did not find a significant relationship between SHBs and weekly earnings.
The study found a significant relationship between SHBs and an increase in average weekly hours worked across the state for female populations but not for male populations.
For populations under 35, the study found a significant relationship between SHBs and an increase in average weekly hours worked for females but a decrease in weekly hours worked for males.
The study did not find a significant relationship between SHBs and average weekly hours worked for male or female populations older than 35.
Considerations for Interpreting the Findings
Although the author matched groups according to pre-treatment outcomes within sex and age, the author did not examine or control for race/ethnicity, as required in the topic area protocol. Also, it is impossible to disentangle the effect of SHBs from the effect of the state itself because the analysis considered a policy operating in only one state; this is known as a confounding factor. We cannot attribute the estimated effects with confidence to SHBs, and not to other factors. Therefore, the study is not eligible for a moderate causal evidence rating, the highest rating available for nonexperimental designs.
Causal Evidence Rating
The quality of causal evidence presented in this report is low because the author did not account for other factors that could have affected the difference between the treatment and comparison groups. This means we are not confident that the estimated effects are attributable to Salary History Bans; other factors are likely to have contributed.