Analytical Studies Branch Research Paper Series
Pay Transparency and the Gender Gap

by Michael Baker, Yosh Halberstam, Kory Kroft, Alexandre Mas and Derek Messacar
11F0019M No. 430
Release date: September 16, 2019

Abstract

This paper examines the impact of public sector salary disclosure laws on university faculty salaries in Canada. These laws, which give the public access to the salaries of individual faculty members if they exceed specified thresholds, were introduced in different provinces at different points in time. Using detailed administrative data covering the universe of university faculty in Canada and an event-study research design, this study documented three key findings. First, the disclosure laws reduced salaries on average. Second, the laws reduced the gender wage gap between men and women. Third, the gender wage gap narrowed primarily in universities where faculty members are unionized.

Keywords: Salary disclosure laws; pay transparency; gender wage gap; event study; University and College Academic Staff System (UCASS)

Executive summary

One of the most persistent and salient features of labour markets around the world is that women earn less than men. A hypothesis recently gaining traction among academic researchers and policy makers is that the gender earnings gap persists in part because it is hidden. There have also been calls in the private sector for more transparency on pay discrepancies between male and female workers.

As a result, transparency laws are increasingly being considered as a policy to reduce the gender wage gap. However, despite the increase in transparency legislation in Canada and other countries, there has been limited academic research that sheds light on whether pay transparency systematically reduces the gender wage gap. This is a significant shortcoming because transparency laws presumably impose costs on employers and increase the administrative burden. Whether these laws meet the standard of a cost-benefit analysis depends on whether they create more equality between genders, given the efficiency cost.

This paper provides new evidence on the causal effect of pay transparency laws on salaries. Specifically, the impact of the (staggered) introduction of pay disclosure laws on university faculty salaries across Canadian provinces is examined. In 1996, British Columbia, Manitoba and Ontario were the first to introduce disclosure laws, and several other provinces have passed such legislation more recently.

To evaluate the effect of these laws, administrative data on the salaries of full-time academic employees at Canadian postsecondary institutions from 1970 onwards are used, based on an analysis of the University and College Academic Staff System. The research design uses the variation across Canadian provinces in the rollout of the disclosure laws, as well as within-province variation in exposed departments.

The following three key results were established:

  1. Transparency laws reduce faculty salaries by approximately 1 to 3 percentage points on average.
  2. These laws also reduce the gender wage gap by approximately 2.2 to 2.4 percentage points. This represents a roughly 30% reduction in the gap, from a base of 7% to 8%, which was the gender wage gap that prevailed at the time of the first series of transparency reforms in Canada. This effect primarily reflects a slowing in the growth of salaries for male faculty members. There is also some evidence to suggest that the salaries of female faculty members have increased, although the estimates are smaller in magnitude.
  3. The effects of salary disclosure on average wages and the gender wage gap are more pronounced in unionized workplaces. For example, female wages increased by roughly 1 percentage point in response to the introduction of a disclosure law in unionized universities, whereas that change was close to zero in non-unionized universities. Unions may play an important role in the response to disclosure, since universities must participate in—and respond to—the formal grievance procedures of unionized workplaces. The existence of a formal grievance procedure might particularly benefit women in an environment where the majority of chairs and senior faculty members are men.

The results of this study are informative about the partial equilibrium impacts of pay transparency laws. However, it is possible that such laws have spillover effects that lead to broader changes in social norms and, as a result, the general equilibrium effects of these laws may be different.

1 Introduction

One of the most persistent and salient features of labour markets around the world is that women earn less than men. For example, in the United States, a woman typically earns roughly 77 dollars for every 100 dollars earned by a man (Goldin 2014). A hypothesis recently gaining traction among academic researchers and policy makers is that the gender earnings gap persists in part because it is hidden. This is buttressed by a series of policy reforms that mandate the disclosure of salaries broken down by gender.Note In the United States, President Obama passed legislation requiring firms with government contracts to disclose the average wages of employees by gender, although this was subsequently rolled back by President Trump.Note There have also been calls in the private sector for more transparency on pay differences between male and female workers; for example, technology firms are facing public pressure to disclose salaries broken down by gender.Note

Outside the United States, transparency laws are increasingly being considered as a policy to reduce the gender earnings gap. Denmark introduced legislation in 2006 requiring large firms to report wage statistics broken down by gender (Bennedsen et al. 2019). Starting in 2017, firms in the United Kingdom with more than 250 employees are required to report pay and bonuses broken down by gender (The Equality Act 2010 (Gender Pay Gap Information) (S.I. 2017/172)).Note Similar reforms are underway in Australia, France and Germany. In Canada, the Pay Transparency Act (2018, S.O. 2018, C.5 – Bill 3), which was introduced recently in Ontario, requires all publicly advertised job postings to include a salary range, bars employers from asking about past compensation and mandates employers to report gender earnings gaps to the province.Note

Despite the increase in transparency legislation, there has been limited academic research that sheds light on whether pay transparency systematically reduces the gender wage gap. This is a significant shortcoming because transparency laws presumably impose costs on employers and increase the administrative burden. Whether these laws meet the standard of a cost-benefit analysis depends on whether they create more equality between genders, given the efficiency cost.

This paper provides new evidence on the causal effect of pay transparency laws on salaries. The impact of the (staggered) introduction of pay disclosure laws on university faculty salaries across Canadian provinces is examined. In 1996, British Columbia, Manitoba and Ontario were the first to introduce disclosure laws that require universities to report the salaries of each employee earning over $50,000 (British Columbia and Manitoba) and over $100,000 (Ontario). Disclosure laws in other provinces have passed more recently, and only four provinces currently lack the explicit legal means to publicize university faculty salaries.

To evaluate the effect of these laws, Statistics Canada administrative data on the salaries of full-time academic employees at Canadian colleges and universities from 1970 onwards are used. These data have nearly 100% coverage of full-time faculty members at Canadian universities, and almost all universities in Canada are in the public sector. This dataset contains a wide range of demographic and job-related variables relevant to identifying and explaining the evolution of the gender wage gap over time. With these variables, the salaries that are “exposed” by these laws can be determined at a very detailed level. This is one of the few datasets that jointly provides information on earnings and demographic characteristics for a comprehensive set of employers within a sector.

The research design uses variation across Canadian provinces in the rollout of the disclosure laws, as well as within-province variation in exposed departments. Since the laws apply only to faculty members with salaries above thresholds, lower-paying departments were not affected by the disclosure. However, higher-paying departments were affected, which provides an additional source of within-province variation. Therefore, treatment and control groups can be defined at the academic unit level, and time-varying trends at the provincial level can be controlled for in a flexible manner.

The university sector is a good setting for studying the impact of transparency laws on the gender wage gap for several reasons. First, the gender gap was pervasive at all academic ranks and across all academic institutions in Canada over the period of study.Note Second, there is consensus on the output of academic faculty—classes taught, research publications, administrative service—and it is relatively easy to observe. Therefore, there is logic to the potential arguments in favour of salary redress under a disclosure law. Third, the well-established and widely adopted divisions of faculty by department and rank allow for precise definitions of reference groups. Fourth, given the way salaries are determined in the university sector, earnings differentials reflect wage differentials rather than differences in hours worked. Lastly, the ease of access to the information revealed by some disclosure laws studied depends on Internet access, and universities were at the forefront of providing Internet access to their employees over the study period.

Three key results were established. First, transparency laws reduce faculty salaries on average. In particular, transparency laws lead to a statistically significant 1- to 3-percentage-point reduction in salaries. Second, transparency laws reduce the gender wage gap: there is a statistically significant reduction of 2.2 to 2.4 percentage points. This represents a roughly 30% reduction in the gap, from a base of 7% to 8%, which was the gender wage gap that prevailed at the time of the first series of transparency reforms in Canada. The estimates indicate that the reduction in the gender wage gap reflects a slowing in the growth of salaries for male faculty members in the treatment group relative to the control group. There is also some evidence to suggest that the salaries of female faculty members have increased, although the estimates are smaller in magnitude. Third, the effects of salary disclosure on average wages and the gender wage gap are more pronounced in unionized workplaces.

This paper contributes to several strands of the literature on pay transparency. Several studies have examined the effects of transparency on wages. Gomez and Wald (2010) evaluated the impact of pay disclosure in the province of Ontario and found that the salaries of university presidents in the province increased relative to the average public sector salary and also led to higher growth in average professorial salaries in Ontario relative to other provinces.Note Mas (2017) looked at the effects of a law change in California that mandated the online disclosure of municipal salaries and found salary compression.

Closer to the present context, Bennedsen et al. (2019) examined the impact of a law in Denmark that required firms of more than 35 employees to provide salary data by gender to employees through their employee representative. The data are reported for groups that are large enough to protect the anonymity of individuals.Note Using a difference-in-differences design that compares firms with 35 to 50 employees to firms with 20 to 34 employees, Bennedsen et al. (2019) found that the disclosure law led to a reduction in the gender wage gap in treated firms primarily because of a slowing in men’s wage growth.Note

There are a couple of differences between the present study and that of Bennedsen et al. (2019). First, the nature of the transparency law is very different between the two contexts. In Denmark, either salary gaps are disclosed by firms to an employee representative or the firms draft an internal report on pay equity, whereas in the present context, all salaries above a certain threshold are disclosed and accessible directly by all workers. Second, unlike Bennedsen et al. (2019), who focused on private sector workers, this paper studies public sector workers. Therefore, the two papers are complementary in this respect. Nevertheless, the results of the two studies are quite similar—a reduction in the gender wage gap driven in part by lower salaries among men was also found in the present study.

Other studies have examined the impacts of pay transparency on other outcomes. Cullen and Perez-Truglia (2018) conducted a field experiment at a large corporation that revealed the salaries of peers and managers. They found that a higher perceived peer salary lowers effort, output and retention, whereas a higher perceived manager salary increases these outcomes. Card et al. (2012) used a randomized information experiment to show that pay transparency reduced the well-being of university faculty members in departments where they earned below-median pay in California. Breza, Kaur and Shamdasani (2018) showed that the productivity of Indian manufacturing workers decreased when they were able to find out the salaries of their peers. Perez-Truglia (2019) looked at how transparency affects well-being by evaluating a reform in Norway that led to online tax records for the whole population being disclosed and also found a reduction in well-being.

The rest of the paper is organized as follows: Section 2 discusses the mechanisms by which transparency laws might affect the gender wage gap. Section 3 provides an overview of public sector disclosure laws in Canada. Section 4 discusses the data. Section 5 provides evidence of the gender wage gap for all workers in Canada and for professional occupations within the educational services sector. Section 6 describes the event-study specification. Section 7 contains the empirical results and Section 8 concludes.

2 Why might pay transparency affect the gender wage gap?

One effect of the disclosure of information on gender-based salary disparities within an organization is that it may lead individuals to privately demand higher pay from their employer. The case of Lilly Ledbetter illustrates this. Ledbetter, a supervisor at Goodyear Tire, an American manufacturing company, was unaware that her male counterparts—in similar positions—were being paid more than she was. The revelation of this fact through an anonymous letter led her to file an employment discrimination lawsuit against her employer. This case went all the way to the U.S. Supreme Court and subsequently led to the Lilly Ledbetter Fair Pay Act of 2009 (Pub. L. No. 111-2, 123 Stat. 5 (2009)), which eased the burden of filing a discrimination lawsuit.Note

The Ledbetter case emphasizes individual action by employees. It is also possible that broad salary disclosure reduces the gender wage gap as a result of an institutional response to wider public attention to pay disparities. In particular, organizations may take institutional action to make salary adjustments, in part to maintain public relations. For example, Mas (2017) found that the disclosure of city manager salaries in California led to a reduction in average salaries, which has been interpreted as an institutional response to public outcry over high levels of compensation.

On the other hand, it is possible that the gender wage gap is unaffected by transparency laws. For example, if there is taste-based discrimination or if the gender wage gap is the result of monopsony, transparency may have no impact. Similarly, while learning about co-workers’ wages might reveal something about the nature of firm-specific rents, if men and women use this information in a symmetric fashion when bargaining, one should not expect to see any impact on the gender wage gap. However, if men—but not women—use this information when bargaining, it could exacerbate the gap.Note In the present study of university faculty members, both individual and institutional action can lead to redress.

3 Public sector pay disclosure laws in Canada

As noted in the introduction, the first public sector salary disclosure laws were passed in 1996 in the provinces of British Columbia, Manitoba and Ontario. In each case, the government mandated the disclosure of all university salaries exceeding a certain threshold—$50,000 in British Columbia and Manitoba and $100,000 in Ontario.

Table 1 outlines the year of implementation and disclosure thresholds of the disclosure laws and legislation in provinces that provide access to public salaries, as well as whether these governments publish these salaries online.Note These laws contain a number of noteworthy additional features.

First, most provinces with salary disclosure laws publish salary data online.Note The first time the governments of Ontario, Nova Scotia, Alberta, and Newfoundland and Labrador published salary information online, it was widely covered in the media. However, in other provinces, disclosure laws do not require the province to make these data accessible online. In British Columbia, online access to faculty salaries was given only after a freedom of information request by journalists from the Vancouver Sun—a provincial newspaper—in 2008. The newspaper maintained an online, searchable databank of public sector salaries (including faculty salaries) from 2008 to 2015.Note


Table 1
Provincial salary disclosure laws
Table summary
This table displays the results of Provincial salary disclosure laws Year of
implementation, Disclosure
threshold (dollars) and Online government publication (appearing as column headers).
Year of
implementation
Disclosure
threshold (dollars)
Online government publication
British Columbia 1996 50,000 no
Manitoba 1996 50,000 no
Ontario 1996 100,000 yes
Nova Scotia 2012 100,000 yes
Alberta 2015 125,000 yes
Newfoundland and Labrador 2016 100,000 yes

Second, the initial reporting threshold for disclosure has remained fixed throughout time in most provinces, but has been adjusted for inflation in others. For example, several years following the adoption of legislation on government employee salary disclosure in Alberta, a separate act that applied more broadly to the public sector (including university faculty) was passed in 2012, with a threshold of $125,000 adjusted annually to the Alberta Consumer Price Index.

Lastly, in some provinces, legislation affecting salary disclosure was passed prior to the legislation cited in the table, but did not require the salaries of the university faculty being studied to be disclosed publicly. For example, before the adoption of the legislation in Ontario, the salaries of government employees earning over $40,000 were published in the Public Accounts (Ontario. Ministry of Finance 1990). However, this disclosure did not extend to university faculty and access was limited, as it required obtaining a hard copy of the Public Accounts.Note

4 Data

This study is based on an analysis of Statistics Canada’s University and College Academic Staff System (UCASS) dataset from 1970 to 2017. This is an annual national survey that collects data on full-time teaching staff at degree-granting Canadian universities and their affiliated colleges, as of October 1 of each year. The survey includes all teachers within faculties, academic staff in teaching hospitals, visiting academic staff, and research staff who have an academic rank and salary similar to teaching staff, all of whom have terms of appointment not less than twelve months. It excludes administrative and support staff, librarians, and research and teaching assistants.

UCASS is administered directly to institutions and participation is mandatory. The unit of observation in the data is the individual, but the survey unit is the institution, and information on the socioeconomic characteristics of staff—including pay—is obtained directly from payroll records. Statistics Canada works closely with institutions to maintain consistent reporting each year and to ensure that the data are comparable across institutions. A limitation of this dataset is that it was discontinued from 2011 to 2015. During this period, data were collected independently by participating institutions in association with the National Vice-Presidents Academic Council, leading to the creation of the National Faculty Data Pool (NFDP) consortium, with the goal of emulating UCASS as closely as possible for longitudinal consistency. A recent collaborative effort between Statistics Canada and the university consortium has led to the NFDP being integrated into UCASS to fill in the missing years.

The NFDP has two limitations that are important to note. First, participation in the survey was voluntary. From 2010 to 2012, the sample size decreased from approximately 35,450 workers to 27,000, and the number of institutions observed decreased from 113 to 56. The loss of institutions is proportionately larger, as the withdrawal of a university from the survey also led to the loss of all of its (smaller) satellite colleges. Second, for confidentiality reasons or ease of reporting, several institutions did not maintain consistent reporting of their employees’ personal identifiers when moving from UCASS to the NFDP in 2011 or back to UCASS in 2016. To overcome this issue, individuals were matched on observables to generate longitudinally consistent identifiers for institutions where a break was observed. This was done by matching individuals within institutions and departments based on year of birth, gender, year appointed to the institution and year of highest degree. Placebo checks for institutions and years where no break occurred indicate that the success rate exceeds 99%.

The following sample restrictions were imposed throughout this analysis. Individuals were included only if they held appointments at the rank of assistant, associate or full professor; they were not employed in the faculties of medicine or dentistry; and they were assigned to a specific department. These restrictions were put in place because there is a clearer understanding of the salary determination for the included faculties. For example, salary determination for medicine and dentistry may be affected by activities beyond research and teaching, including medical practice. The analysis was restricted to faculty members with a non-missing department, since the empirical specification below requires assigning a peer group based on department and this is not possible for those not assigned to a department.Note Lastly, the sample was restricted to institutions that were observed in the 2012 wave of the NFDP and that finalized their data with or submitted information to Statistics Canada. This restriction on institutions was in place to balance the panel around the years that the survey was discontinued.

In Table 2, descriptive statistics for the full sample used in this study and separately for men and women are presented. The sample includes 101,103 individual university employees across Canada. On balance, individuals are approximately 48 years old and one-quarter of them are women. This masks the fact that, in the 1970s, less than 15% of faculty members were women. However, this figure has climbed to about 40% in recent years and approximately 45% of new hires in the 2010s were women. In addition, about 80% of faculty members hold a doctorate and 70% belong to unionized institutions. Interestingly, women are nearly 10% more likely to be unionized than men, although this may be driven by two factors: (1) women are more likely to work at institutions represented by unions or faculty associations, and (2) the proportion of women in the industry has risen over time alongside a gradual increase in unionization from the 1970s to 1990s.


Table 2
Descriptive statistics of university employees across Canada
Table summary
This table displays the results of Descriptive statistics of university employees across Canada Full sample, Men and Women, calculated using mean and standard deviation units of measure (appearing as column headers).
Full sample Men Women
mean standard deviation mean standard deviation mean standard deviation
Demographics
Age (years) 47.5 9.7 47.7 9.8 47.1 9.4
Female (percent) 24.5 43.0 0.0 0.0 100.0 0.0
Highest degree (percent)
Doctorate 81.6 38.7 83.0 37.5 77.3 41.9
Professional 0.5 7.4 0.5 7.2 0.6 7.9
Master’s 14.2 34.9 13.0 33.7 18.0 38.4
Below master’s 3.6 18.6 3.4 18.2 4.1 19.8
Rank (percent)
Assistant professor 24.0 42.7 20.4 40.3 35.2 47.8
Associate professor 39.7 48.9 38.4 48.6 43.7 49.6
Full professor 36.3 48.1 41.2 49.2 21.1 40.8
Other job traits (percent)
Unionized 69.6 46.0 67.4 46.9 76.6 42.3
Has responsibilities 11.6 32.0 12.1 32.6 10.1 30.2
Compensation
Salary (2017 constant dollars)
Full sample 116,750 29,750 118,750 29,750 110,700 28,850
Assistant professor 89,350 19,000 89,200 19,050 89,600 18,900
Associate professor 111,900 21,100 111,350 20,650 113,350 22,250
Full professor 140,250 25,150 140,250 24,950 140,300 26,350
Salary growth (percent)
Full sample 2.7 5.6 2.5 5.6 3.3 5.5
Assistant professor 3.4 4.8 3.3 4.8 3.7 4.8
Associate professor 2.8 5.3 2.6 5.3 3.3 5.3
Full professor 2.2 6.1 2.0 6.0 3.0 6.6

5 Context

Female workers in Canada earn less than their male counterparts, as is the case in most developed economies. In Chart A.1 of the Appendix, the female-to-male hourly wage ratio for full-time workers over the period of this study is documented (Baker and Drolet [2010] and Morissette, Picot and Lu [2013]). The ratio for all workers and professional occupations within the educational services sector is reported. The ratio for all workers rises from a low of just over 0.82 to almost 0.89 over this period. The ratio for education workers is more volatile because of smaller sample sizes—it begins the period at just over 0.88 and rises above 0.90, except for an abrupt decline in 2018. Throughout almost the entire period, female education professionals faced a smaller wage gap than their counterparts in the wider labour market.

While it has become commonplace to measure gender pay disparities with hourly wages in Canada, earnings are the norm in many other countries, and this study focuses on the annual earnings of the faculty members in this analysis. Using earnings to document gender differences may conflate both the differences in hours worked (e.g., part time versus full time) and the differences in hourly wages. This is less of a concern in the present context, as the sample is restricted to full-time appointments and faculty salaries in Canada are typically a fixed amount paid over 12 months.

The gender earnings gap in this sample of faculty members is reported in Chart 1. The gap is presented over time both unconditionally and conditional on controls (institution, department, year of birth and highest degree attained). The conditional gap was around 15% at the beginning of the period and has narrowed to roughly 4% to 5% in recent years. This is consistent with the findings of Warman, Woolley and Worswick (2010), who used similar data to document a narrowing in male-to-female earnings differentials between 1970 and 2001.

Chart 1

Data table for Chart 1 
Data table for chart 1
Table summary
This table displays the results of Data table for chart 1 Without controls and With controls, calculated using log (salary) units of measure (appearing as column headers).
Without controls With controls
log (salary)
1971 -0.14 -0.14
1972 -0.15 -0.14
1973 -0.15 -0.14
1974 -0.16 -0.14
1975 -0.15 -0.13
1976 -0.16 -0.12
1977 -0.16 -0.12
1978 -0.16 -0.12
1979 -0.15 -0.10
1980 -0.15 -0.10
1981 -0.16 -0.10
1982 -0.16 -0.10
1983 -0.16 -0.09
1984 -0.16 -0.09
1985 -0.16 -0.09
1986 -0.16 -0.09
1987 -0.17 -0.09
1988 -0.17 -0.09
1989 -0.18 -0.09
1990 -0.18 -0.09
1991 -0.17 -0.08
1992 -0.17 -0.08
1993 -0.17 -0.08
1994 -0.17 -0.08
1995 -0.17 -0.08
1996 -0.16 -0.07
1997 -0.15 -0.07
1998 -0.15 -0.07
1999 -0.14 -0.06
2000 -0.14 -0.06
2001 -0.13 -0.06
2002 -0.13 -0.06
2003 -0.12 -0.06
2004 -0.12 -0.06
2005 -0.11 -0.05
2006 -0.10 -0.05
2007 -0.10 -0.05
2008 -0.10 -0.04
2009 -0.09 -0.04
2010 -0.09 -0.04
2011 -0.09 -0.04
2012 -0.09 -0.04
2013 -0.09 -0.03
2014 -0.09 -0.03
2015 -0.08 -0.03
2016 -0.08 -0.03
2017 -0.08 -0.03

A potential concern in using university sector pay is that salaries may be set according to a statutory formula; for example, they may be determined entirely on the basis of institution, department and rank. To gauge whether there is discretion in pay and scope for transparency laws to impact the gender wage gap, salaries are predicted by regressing them on the interaction of institution, department, rank, tenure and year fixed effects; age fixed effects; and highest degree obtained fixed effects. If salaries are set in a formulaic way, then there should be very little residual variance between actual salaries and predicted salaries. Chart A.2 in the Appendix shows that this is not the case, as substantial residual variation was observed for both men and women. The R-squared for both models is roughly 70%. Furthermore, the fact that the conditional gender earnings gap was roughly 7% to 8% at the time the first disclosure laws were introduced suggests that there is scope for disclosure to affect the gap.

6 Econometric specification

The Canadian setting is unique for evaluating the causal effect of transparency, as there are three separate sources of variation in transparency: province, year and baseline salary. For example, as discussed above, salary disclosure in Ontario was introduced in 1996, but only individuals with salaries above the $100,000 threshold were included.Note The baseline definition of treatment takes advantage of all of these sources of variation. Specifically, an individual is defined as treated in a given year if, during that year, they worked in a province with salary disclosure legislation in place and worked in a department where a faculty member’s salary was revealed by the disclosure policy in the year of the reformNote . The main definition of peer group consists of all faculty in the same institution and department. Results are also reported from another definition based on institution, department and rank. The two definitions of treatment are conceptually distinct: the former may capture vertical comparisons, whereas the latter is limited to horizontal comparisons (see Cullen and Perez-Truglia 2018).

To formalize the approach, consider a panel of i=1,,N MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGPbGaeyypa0JaaGymaiaacYcacqGHMacVcaGGSaGaamOtaaaa @3C87@  individuals in which salary   Y it MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaGGGcGaamywa8aadaWgaaWcbaWdbiaadMgacaWG0baapaqabaaa aa@3A59@  is observed for t=1,,T MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWG0bGaeyypa0JaaGymaiaacYcacqGHMacVcaGGSaGaamivaaaa @3C98@  years or, for some, a subset thereof. A binary treatment variable is also observed, D it { 0,1 }:  D it =0  MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGebWdamaaBaaaleaapeGaamyAaiaadshaa8aabeaak8qacqGH iiIZdaGadaWdaeaapeGaaGimaiaacYcacaaIXaaacaGL7bGaayzFaa GaaiOoaiaacckacaWGebWdamaaBaaaleaapeGaamyAaiaadshaa8aa beaak8qacqGH9aqpcaaIWaGaaeiOaaaa@471D@  if i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGPbaaaa@3705@  has not been treated by year t MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWG0baaaa@370F@  and D it =1 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGebWdamaaBaaaleaapeGaamyAaiaadshaa8aabeaak8qacqGH 9aqpcaaIXaaaaa@3AFC@  if i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGPbaaaa@3705@  has been treated by year t MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWG0baaaa@370F@ . In the present setting, treatment is an absorbing state and the treatment path D (i,t) (t=0) T MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGebWdamaaBaaaleaapeGaaiikaiaadMgacaGGSaGaamiDaiaa cMcaa8aabeaakmaaDaaaleaapeGaaiikaiaadshacqGH9aqpcaaIWa GaaiykaaWdaeaapeGaamivaaaaaaa@407B@  is a sequence of zeros and ones. In this case, the treatment path is uniquely characterized by the time period of the initial treatment, which is denoted by E i = min { t: D i,t =1 } MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGfbWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabg2da98aa daWfqaqaa8qaciGGTbGaaiyAaiaac6gaaSWdaeaaaeqaaOWdbmaacm aapaqaa8qacaWG0bGaaiOoaiaadseapaWaaSbaaSqaa8qacaWGPbGa aiilaiaadshaa8aabeaak8qacqGH9aqpcaaIXaaacaGL7bGaayzFaa aaaa@4638@ . This is typically referred to as the “event time,” and K it =t E i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGlbWdamaaBaaaleaapeGaamyAaiaadshaa8aabeaak8qacqGH 9aqpcaWG0bGaeyOeI0Iaamyra8aadaWgaaWcbaWdbiaadMgaa8aabe aaaaa@3E40@  is denoted as the “relative time.” Let F i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGgbWdamaaBaaaleaapeGaamyAaaWdaeqaaaaa@382A@  be an indicator variable that takes on a value of 1 if individual i MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGPbaaaa@3705@  is female. The following standard dynamic specification is considered:

log( Y it )= α i + β t M + β t F + k = A B1 γ k 1{ K it =k }+ γ B+ 1{ K it B } MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGSbGaam4BaiaadEgadaqadaWdaeaapeGaamywa8aadaWgaaWc baWdbiaadMgacaWG0baapaqabaaak8qacaGLOaGaayzkaaGaeyypa0 JaeqySde2damaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgUcaRiab ek7aI9aadaqhaaWcbaWdbiaadshaa8aabaWdbiaad2eaaaGccqGHRa WkcqaHYoGypaWaa0baaSqaa8qacaWG0baapaqaa8qacaWGgbaaaOGa ey4kaSYaaybCaeqal8aabaWdbiaadUgacaGGGcGaeyypa0JaaiiOai abgkHiTiaadgeaa8aabaWdbiaadkeacqGHsislcaaIXaaan8aabaWd biabggHiLdaakiabeo7aN9aadaWgaaWcbaWdbiaadUgaa8aabeaak8 qacaaIXaWaaiWaa8aabaWdbiaadUeapaWaaSbaaSqaa8qacaWGPbGa amiDaaWdaeqaaOWdbiabg2da9iaadUgaaiaawUhacaGL9baacqGHRa WkcqaHZoWzpaWaaSbaaSqaa8qacaWGcbGaey4kaScapaqabaGcpeGa aGymamaacmaapaqaa8qacaWGlbWdamaaBaaaleaapeGaamyAaiaads haa8aabeaak8qacqGHLjYScaWGcbaacaGL7bGaayzFaaaaaa@6FE2@ + k = A B1 δ k 1{ K it =k }× F i + δ B+ 1{ K it B }× F i + ε it MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqGHRaWkdaGfWbqabSWdaeaapeGaam4AaiaacckacqGH9aqpcaGG GcGaeyOeI0IaamyqaaWdaeaapeGaamOqaiabgkHiTiaaigdaa0Wdae aapeGaeyyeIuoaaOGaeqiTdq2damaaBaaaleaapeGaam4AaaWdaeqa aOWdbiaaigdadaGadaWdaeaapeGaam4sa8aadaWgaaWcbaWdbiaadM gacaWG0baapaqabaGcpeGaeyypa0Jaam4AaaGaay5Eaiaaw2haaiab gEna0kaadAeapaWaaSbaaSqaa8qacaWGPbaapaqabaGcpeGaey4kaS IaeqiTdq2damaaBaaaleaapeGaamOqaiabgUcaRaWdaeqaaOWdbiaa igdadaGadaWdaeaapeGaam4sa8aadaWgaaWcbaWdbiaadMgacaWG0b aapaqabaGcpeGaeyyzImRaamOqaaGaay5Eaiaaw2haaiabgEna0kaa dAeapaWaaSbaaSqaa8qacaWGPbaapaqabaGcpeGaey4kaSIaeqyTdu 2damaaBaaaleaapeGaamyAaiaadshaa8aabeaaaaa@67F8@

Where A0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGbbGaeyyzImRaaGimaaaa@395D@  leads of the treatment are included together with B0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGcbGaeyyzImRaaGimaaaa@395E@  terms that capture the short-run effects and a single parameter to capture longer-run effects. In the present specification, A=10 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGbbGaeyypa0JaaGymaiaaicdaaaa@3958@  and B=6 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGcbGaeyypa0JaaGOnaaaa@38A4@ . Therefore, the model controls for an individual fixed effect ( α i ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaqadaWdaeaapeGaeqySde2damaaBaaaleaapeGaamyAaaWdaeqa aaGcpeGaayjkaiaawMcaaaaa@3AC0@  and gender-specific year effects ( β t M , β t F ) MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaqadaWdaeaapeGaeqOSdi2damaaDaaaleaapeGaamiDaaWdaeaa peGaamytaaaakiaacYcacqaHYoGypaWaa0baaSqaa8qacaWG0baapa qaa8qacaWGgbaaaaGccaGLOaGaayzkaaaaaa@402A@  (M = male, F = female). Some specifications also control flexibly for year-by-province-by-gender fixed effects. Therefore, this controls for time-varying, province-specific shocks that might differentially affect the salaries of men and women and that are correlated with the event time. The identifying assumption is that there are no shocks correlated with the introduction of transparency laws that differentially affect the salaries of men and women within peer groups. The coefficients of interest are the parameters { δ k } k = A B1 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaGadaWdaeaapeGaeqiTdq2damaaBaaaleaapeGaam4AaaWdaeqa aaGcpeGaay5Eaiaaw2haa8aadaqhaaWcbaWdbiaadUgacaGGGcGaey ypa0JaaiiOaiabgkHiTiaadgeaa8aabaWdbiaadkeacqGHsislcaaI Xaaaaaaa@443B@  and δ B+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaH0oazpaWaaSbaaSqaa8qacaWGcbGaey4kaScapaqabaaaaa@39BF@ . These indicate the causal effect of transparency on the gender wage gap in the short run and long run, respectively. The presence of pre-trends can also be tested for by plotting the δ ^ k MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacuaH0oazpaGbaKaadaWgaaWcbaWdbiaadUgaa8aabeaaaaa@3916@  for k<0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGRbGaeyipaWJaaGimaaaa@38C5@  and examining whether δ ^ k =0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacuaH0oazpaGbaKaadaWgaaWcbaWdbiaadUgaa8aabeaak8qacqGH 9aqpcaaIWaaaaa@3AF0@ .

Lastly, to quantify the magnitude of the effect and increase the precision of the estimates, the “static” or canonical specification is adapted by setting A=B=0 MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGbbGaeyypa0JaamOqaiabg2da9iaaicdaaaa@3A6A@ :

log( Y it )= α i + β t M + β t F + γ 0+ D it + δ 0+ D it × F i + ε it MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGSbGaam4BaiaadEgadaqadaWdaeaapeGaamywa8aadaWgaaWc baWdbiaadMgacaWG0baapaqabaaak8qacaGLOaGaayzkaaGaeyypa0 JaeqySde2damaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgUcaRiab ek7aI9aadaqhaaWcbaWdbiaadshaa8aabaWdbiaad2eaaaGccqGHRa WkcqaHYoGypaWaa0baaSqaa8qacaWG0baapaqaa8qacaWGgbaaaOGa ey4kaSIaeq4SdC2damaaBaaaleaapeGaaGimaiabgUcaRaWdaeqaaO WdbiaadseapaWaaSbaaSqaa8qacaWGPbGaamiDaaWdaeqaaOWdbiab gUcaRiabes7aK9aadaWgaaWcbaWdbiaaicdacqGHRaWka8aabeaak8 qacaWGebWdamaaBaaaleaapeGaamyAaiaadshaa8aabeaak8qacqGH xdaTcaWGgbWdamaaBaaaleaapeGaamyAaaWdaeqaaOWdbiabgUcaRi abew7aL9aadaWgaaWcbaWdbiaadMgacaWG0baapaqabaaaaa@63D5@

Where γ 0+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaaIWaGaey4kaScapaqabaaaaa@39B4@ is the causal effect of transparency on average wages for male faculty members and γ 0+ + δ 0+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaaIWaGaey4kaScapaqabaGcpeGa ey4kaSIaeqiTdq2damaaBaaaleaapeGaaGimaiabgUcaRaWdaeqaaa aa@3E4B@ is the causal effect for female faculty members. Compared with the dynamic model, this specification imposes no pre-trends and assumes constant treatment effects for all k MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGRbaaaa@3706@ . The standard errors are clustered at the level of institution and department, as this is the level at which the treatment is defined.

7 Empirical results

This section begins by presenting a series of non-parametric event-study plots to visually examine the effects of transparency on the gender wage gap. Next, it turns to regression models to quantify the precise impact.

Chart 2 contains the main event study showing the impact of pay disclosure laws on the gender wage gap.Note Panel A splits the sample into male and female faculty members. The red circles represent women’s log salaries, and the blue squares represent men’s log salaries. The blue squares correspond to γ k MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaWGRbaapaqabaaaaa@3908@  while the red circles correspond to γ k + δ k MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaWGRbaapaqabaGcpeGaey4kaSIa eqiTdq2damaaBaaaleaapeGaam4AaaWdaeqaaaaa@3CF3@ . Year 0 is the year of the reform. The chart shows that, prior to the reform, the blue squares were above the red circles. However, after the reform, the reverse is true, indicating that the disclosure laws reduced the gender wage gap. The chart shows that men’s salaries fell on average, while women’s salaries increased. This can also be seen in Panel B, which graphs the gender wage gap δ k MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaH0oazpaWaaSbaaSqaa8qacaWGRbaapaqabaaaaa@3906@ . In terms of pre-trends, while a slight increase is seen in the gender wage gap in the years prior to the reforms, the visual evidence indicates a clear and noticeable jump around the event year, which provides some degree of confidence that it is not just differential pre-trends that are being detected. The chart also shows that salaries for both men and women tend to drop in the long run (e.g., γ 0+ + δ 0+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaaIWaGaey4kaScapaqabaGcpeGa ey4kaSIaeqiTdq2damaaBaaaleaapeGaaGimaiabgUcaRaWdaeqaaa aa@3E4B@  and γ 0+ MathType@MTEF@5@5@+= feaagKart1ev2aqatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacqaHZoWzpaWaaSbaaSqaa8qacaaIWaGaey4kaScapaqabaaaaa@39B4@  are quite low relative to their short-run effects).

Chart 2

Data table for Chart 2 
Data table for Chart 2
Table summary
This table displays the results of Data table for Chart 2. The information is grouped by Year relative to reform interacted with peer salary disclosed (appearing as row headers), Panel A — Average wages of men and women, Panel B — Gender gap, Men, 95% confidence interval, Women, Gender wage gap, Upper bound and Lower bound, calculated using log (salary) units of measure (appearing as column headers).
Year relative to reform interacted with peer salary disclosed Panel A — Average wages of men and women Panel B — Gender gap
Men 95% confidence interval Women 95% confidence interval Gender wage gap 95% confidence interval
Upper bound Lower bound Upper bound Lower bound Upper bound Lower bound
log (salary)
-10 0.049 0.055 0.043 0.020 0.030 0.011 -0.028 -0.019 -0.038
-9 0.052 0.059 0.045 0.019 0.029 0.008 -0.033 -0.023 -0.044
-8 0.054 0.062 0.047 0.022 0.034 0.010 -0.033 -0.021 -0.045
-7 0.055 0.063 0.047 0.021 0.033 0.008 -0.034 -0.022 -0.046
-6 0.054 0.061 0.046 0.018 0.031 0.005 -0.036 -0.023 -0.049
-5 0.050 0.059 0.042 0.014 0.029 -0.001 -0.036 -0.021 -0.051
-4 0.046 0.056 0.036 0.017 0.033 0.001 -0.029 -0.013 -0.045
-3 0.043 0.054 0.032 0.017 0.034 0.001 -0.026 -0.009 -0.042
-2 0.048 0.059 0.037 0.020 0.038 0.003 -0.027 -0.010 -0.045
-1 0.040 0.052 0.029 0.019 0.036 0.002 -0.021 -0.004 -0.038
0 0.024 0.037 0.011 0.028 0.047 0.010 0.004 0.023 -0.014
1 0.033 0.048 0.017 0.031 0.051 0.011 -0.002 0.018 -0.021
2 0.039 0.056 0.023 0.041 0.060 0.021 0.001 0.021 -0.018
3 0.045 0.061 0.029 0.047 0.066 0.028 0.002 0.021 -0.017
4 0.043 0.057 0.029 0.051 0.070 0.032 0.008 0.027 -0.011
5 0.041 0.056 0.027 0.052 0.071 0.032 0.010 0.030 -0.009
6+ -0.004 0.012 -0.019 0.005 0.026 -0.015 0.009 0.029 -0.011

The regression results are presented in Table 3. Panel A reports the results for the full sample of both men and women. The first and third columns include individual fixed effects and province-by-year fixed effects, while the second and fourth columns control additionally for the number of years since appointment to the institution, the number of years since the highest degree was obtained and an indicator for having senior administrative responsibilities. The first and second columns consider the peer group to be the institution and department, while the third and fourth columns consider the peer group to be the institution, department and rank.Note Appendix Table A.1 reproduces Table 3 clustering on the institution.


Table 3
Effect of pay transparency on the average wage and on the gender wage gap, university faculty members
Table summary
This table displays the results of Effect of pay transparency on the average wage and on the gender wage gap Peer group specification, Institution and department and Institution, department and rank, calculated using coefficient estimates, statistics and indicators units of measure (appearing as column headers).
Peer group specification
Institution and department Institution, department and rank
coefficient estimates
Panel A: Effect on the average wage
Treated -0.014Note ** -0.015Note ** -0.025Note *** -0.026Note ***
statistics
R-squared 0.923 0.926 0.923 0.926
Number of observations 982,543 948,691 982,543 948,691
Number of clusters 1,262 1,239 1,262 1,239
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes
Additional controls no yes no yes
coefficient estimates
Panel B: Effect on the gender wage gap
Treated -0.014Note ** -0.017Note ** -0.025Note *** -0.026Note ***
Female-treated interaction 0.022Note ** 0.023Note *** 0.023Note *** 0.024Note ***
statistics
R-squared 0.924 0.927 0.925 0.927
Number of observations 982,543 948,691 982,543 948,691
Number of clusters 1,262 1,239 1,262 1,239
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes
Additional controls no yes no yes

Across all of these specifications, transparency laws were consistently found to reduce average salaries. All estimates are statistically significant at the 1% level.Note The point estimates range between 1.4 and 1.5 percentage points in the institution and department peer group specification and between 2.5 and 2.6 percentage points in the institution, department and rank peer group specification, where conditioning on the additional controls in the second and fourth columns increases the magnitude of the estimates by 0.1 percentage point.

Panel B presents the estimates for the gender wage gap, breaking down the impact of the treatment by gender. In all specifications, year-by-province-by-gender fixed effects were controlled for. Across the specifications, the estimates indicate a statistically significant reduction in the gender wage gap by 2.2 to 2.4 percentage points. Relative to a mean gender wage gap of 7% to 8% at the time of the initial reforms in 1996 (see Chart 1), this represents an effect of roughly 30%. In the first and second columns, the narrowing of the gender wage gap stems from both a decline in the growth of men’s wages and an increase in women’s wages, consistent with the evidence in the event study in Chart 2. However, in the third and fourth columns, the change in the gap stems mostly from changes in men’s wages.

The fact that the growth in men’s salaries fell in the treatment group relative to the control group suggests that there may have been, in part, an institutional response to disclosure. Unions are an important institutional mediator in the Canadian higher education sector, as a large share of faculty members are unionized (see Table 2). Unions may play an important role in the response to disclosure, since universities must participate in—and respond to—the formal grievance procedures of unionized workplaces.Note In contrast, the request for higher pay in a non-unionized environment is more likely to occur through an informal meeting with a department chair, which may be difficult without an external competing offer from a peer institution. The existence of a formal grievance procedure might particularly benefit women in an environment where the majority of chairs and senior faculty members are men.

Table 4 presents estimates of the effect of the treatment separately based on whether faculty members were unionized or non-unionized that year. In Panel B, the estimates by gender reveal that the primary effect of the law on the gender wage gap was observed in unionized workplaces. Women’s wages increased by roughly 1 percentage point in response to the introduction of a disclosure law. In non-unionized universities, the change in women’s wages was close to zero. While it is not possible to be certain that this is the result of the union mechanisms discussed above, this does suggest that the efficacy of the transparency laws turns on something that is different across—rather than common among—unionized and non-unionized universities.


Table 4
Effects of pay transparency by union status, university faculty members
Table summary
This table displays the results of Effects of pay transparency by union status Peer group specification, Institution and department, Institution, department and rank, Unionized and Not unionized, calculated using coefficient estimates, statistics and indicators units of measure (appearing as column headers).
Peer group specification
Institution and department Institution, department and rank
Unionized Not unionized Unionized Not unionized
coefficient estimates
Panel A: Effect on the average wage
Treated -0.009 -0.008 -0.017Note ** -0.030Note ***
statistics
R-squared 0.926 0.936 0.926 0.936
Number of observations 686,692 294,003 686,692 294,003
Number of clusters 943 781 943 781
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes
coefficient estimates
Panel B: Effect on the gender wage gap
Treated -0.013Table 4 Note  -0.008 -0.021Note *** -0.027Note **
Female-treated interaction 0.025Note ** 0.013 0.032Note *** 0.007
statistics
R-squared 0.928 0.938 0.928 0.938
Number of observations 686,692 293,992 686,692 293,992
Number of clusters 943 781 943 781
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes

Lastly, a number of the universities in the sample undertook campus-wide studies of gender differences in compensation over the sample period. While there is no direct evidence that these studies were in response to transparency laws, they appear to have all taken place within provinces after a law has come into effect. The analysis in these studies typically involves the use of regression analysis to estimate the gender wage gap, controlling for factors such as field and experience (years since the highest degree and years at the institution). In many of these cases, the studies revealed evidence of a gender wage gap, which has led the university to make a one-time across-the-board adjustment to female faculty members’ salaries. In other cases, a pool of money has been established to grant anomalies to faculty members who fall below the regression line. A list of these initiatives, their relevant dates, and the amount and timing of any resulting salary adjustments is presented in Table A.2 of the Appendix. These studies may be a mechanism by which disclosure affected compensation at the institutional level.

8 Conclusion

This paper examines the effect of transparency laws on the gender wage gap. While it focuses on public sector salaries, the ongoing efforts of governments around the world to increase the transparency of wages in the private sector may allow researchers to determine whether the effects documented hold in other sectors of the economy.

There are several directions for future research. First, the estimates are informative about the partial equilibrium impacts of transparency. It is possible that transparency laws have spillover effects that lead to broader changes in social norms and, as a result, the general equilibrium effects of these laws may be different. Second, transparency laws are complex and varied by nature. There is a difference between active disclosure—whereby salaries are easily accessible online—and passive disclosure—whereby salaries are available only upon request. These two forms of disclosure may not have the same equilibrium effects on salaries. For example, salaries that are accessible online may garner significantly more media attention and public pressure for adjustment. Additionally, the lower cost of access means that they are more likely to be used in bargaining with employers.

Appendix

Chart A1

Data table for Chart A1 
Data table for chart A.1
Table summary
This table displays the results of Data table for chart A.1 All workers and Professional occupations in education, calculated using log (salary) units of measure (appearing as column headers).
All workers Professional occupations in education
log (salary)
1997 82.95 88.01
1998 82.65 88.75
1999 82.57 89.18
2000 82.04 89.15
2001 82.16 88.41
2002 83.36 89.29
2003 84.05 89.31
2004 84.94 88.89
2005 85.54 92.41
2006 85.44 89.00
2007 85.43 90.03
2008 85.38 90.84
2009 86.57 91.93
2010 87.12 94.77
2011 87.89 91.98
2012 87.79 93.11
2013 87.80 92.47
2014 88.01 91.76
2015 87.84 92.54
2016 88.35 91.42
2017 88.52 90.57
2018 88.62 87.67

Chart A2

Data table for Chart A2 
Data table for Chart A.2
Table summary
This table displays the results of Data table for Chart A.2. The information is grouped by Log (salary) residual bin (appearing as row headers), Men and Women, calculated using percent units of measure (appearing as column headers).
Log (salary) residual bin Men Women
percent
-0.50 0.0021 0.0012
-0.48 0.0050 0.0012
-0.46 0.0050 0.0012
-0.44 0.0052 0.0050
-0.42 0.0057 0.0062
-0.40 0.0068 0.0112
-0.38 0.0125 0.0100
-0.36 0.0152 0.0175
-0.34 0.0201 0.0175
-0.32 0.0300 0.0287
-0.30 0.0457 0.0237
-0.28 0.0742 0.0549
-0.26 0.1191 0.0761
-0.24 0.1654 0.1309
-0.22 0.2822 0.1821
-0.20 0.4321 0.2843
-0.18 0.6788 0.4127
-0.16 1.0364 0.7183
-0.14 1.6290 1.1422
-0.12 2.4643 2.0550
-0.10 3.7497 3.2721
-0.08 5.5005 5.3819
-0.06 8.0836 7.8497
-0.04 11.1890 11.5969
-0.02 14.7846 17.1584
0.00 15.0135 17.3878
0.02 11.0250 11.0682
0.04 7.6557 7.3322
0.06 5.1522 4.9941
0.08 3.5287 3.1511
0.10 2.3757 2.0637
0.12 1.5692 1.2370
0.14 1.0396 0.7706
0.16 0.7099 0.5063
0.18 0.5097 0.3167
0.20 0.3470 0.2506
0.22 0.2210 0.1446
0.24 0.1711 0.1122
0.26 0.1139 0.0873
0.28 0.0799 0.0337
0.30 0.0525 0.0262
0.32 0.0306 0.0224
0.34 0.0225 0.0137
0.36 0.0144 0.0112
0.38 0.0073 0.0100
0.40 0.0068 0.0087
0.42 0.0029 0.0062
0.44 0.0031 0.0050
0.46 0.0034 0.0012
0.48 0.0024 0.0000
0.50 0.0000 0.0000

Table A.1
Effects of pay transparency with standard errors clustered by institution, university faculty members
Table summary
This table displays the results of Effects of pay transparency with standard errors clustered by institution Peer group specification, Institution and department and Institution, department and rank, calculated using coefficient estimates, statistics and indicators units of measure (appearing as column headers).
Peer group specification
Institution and department Institution, department and rank
coefficient estimates
Panel A: Effect on the average wage
Treated -0.014 -0.015 -0.025Note ** -0.026Note **
statistics
R-squared 0.923 0.926 0.923 0.926
Number of observations 982,543 948,691 982,543 948,691
Number of clusters 56 55 56 55
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes
Additional controls no yes no yes
coefficient estimates
Panel B: Effect on the gender wage gap
Treated -0.014 -0.017Table A.1 Note  -0.025Note ** -0.026Note **
Female-treated interaction 0.022Note ** 0.023Note ** 0.023Note ** 0.024Note ***
statistics
R-squared 0.924 0.927 0.925 0.927
Number of observations 982,543 948,691 982,543 948,691
Number of clusters 56 55 56 55
indicators
Fixed effects
Individual yes yes yes yes
Province–year yes yes yes yes
Additional controls no yes no yes

Table A.2
Known examples of institutional studies on gender pay equity and women’s pay adjustments, university faculty members
Table summary
This table displays the results of Known examples of institutional studies on gender pay equity and women’s pay adjustments. The information is grouped by University (appearing as row headers), Year of study, Date of pay adjustment and Size of adjustment (appearing as column headers).
University Year of study Date of pay adjustment Size of adjustment
Western University 2005, 2009 Note ...: not applicable Note ...: not applicable
University of British Columbia 2010 February 28, 2013 2.00%
University of Victoria 2014 Unknown Unknown
McMaster University 2015 July 1, 2015 $3,515
Simon Fraser University 2015 September 3, 2016 1.70%
University of Waterloo 2016 September 1, 2016 $2,905
Wilfrid Laurier University 2017 June 22, 2017 3.00%; 3.90%
Guelph University 2018 June 1, 2018 $2,050
University of Toronto 2019 July 1, 2019 1.30%

References

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