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Information and the persistence of the gender wage gap: Early evidence from California's Salary History Ban (Hansen & McNichols, 2020)

Review Guidelines

Absence of conflict of interest.

Citation

Hansen, B., & McNichols, D. (2020). Information and the persistence of the gender wage gap: Early evidence from California's Salary History Ban (Working Paper No. w27054). Cambridge, MA: National Bureau of Economic Research. http://dx.doi.org/10.2139/ssrn.3586186

Highlights

  • The study's objective was to examine the impact of state-wide implementation of Salary History Bans (SHBs) on gender earnings gap ratios.  

  • The study used a nonexperimental design to estimate the difference in outcomes between states that passed a SHB to states without SHBs using multiple data sources.  

  • The study did not find a significant difference in gender earnings gap ratios between states that passed a SHB to states without SHBs. 

  • This study receives a moderate evidence rating. This means we are somewhat confident that any estimated effects are attributable to the intervention, but other factors might also have contributed. However, the study did not find statistically significant effects. 

Intervention Examined

Salary History Bans (SHBs)

Features of the Intervention

Salary History Bans (SHBs) are laws that ban employers from asking jobseekers questions about their salary history. Various states have implemented SHBs in recent years to address potential discrimination in labor markets, particularly aimed at combatting the gender wage gap. The state of California implemented their SHB law on January 1, 2018. Under this law, employers are prevented from asking applicants about their compensation history or seeking this information through an agent. Applicants may voluntarily reveal their salary history, but the law restricts employers from basing salary solely on the grounds of prior salary. In addition, most SHBs require the employers to provide a salary range for the applicant. The SHB laws are aimed at protecting any individual applying for employment.  

Features of the Study

The study used a nonexperimental design to estimate the difference in outcomes between the states that implemented SHBs (treatment) to states that did not (comparison). The authors used multiple data sources including the Basic Monthly Current Population Survey (CPS), American Community Survey, the Quality Workforce Indicators, and the Current Employer Statistics. The study sample included data from 2006 to the end of 2019 and is restricted to prime working age individuals between the ages of 25 and 54. To create the treatment group, the authors create earnings ratios by gender, state, age, and industry of employment. The treatment group includes all data gathered from places after SHB laws were implemented and includes 36 states and The District of Columbia. The authors used statistical methods to create a comparison group similar in outcomes to entities experiencing a discrete change in policy and included data prior to the enactment of SHB laws. The authors used statistical models to compare the gender earnings gap ratio for treatment and comparison groups.  

Findings

Earnings and wages

  • Overall, the study findings suggest that female earnings increased relative to male earnings in the states that adopted SHBs. However, this was not a statistically significant finding. 

Considerations for Interpreting the Findings

The authors use advanced statistical models to create their comparison group of untreated states where matching on covariates is not necessary when the match is made on a long set of pre-treatment outcomes. Therefore, the matches used pre-treatment outcomes only and do not include the typical variables for these type of studies (e.g., gender, race, etc.).  

Causal Evidence Rating

The quality of causal evidence presented in this report is moderate because it was based on a well-implemented nonexperimental design. This means we are somewhat confident that the estimated effects are attributable to the implementation of SHBs, but other factors might also have contributed.  

Reviewed by CLEAR

February 2023

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