Gender Pay Gap in Top Jobs

What is the ratio of women s to men s earnings on average in Canada?

What is the ratio of women s to men s earnings on average in Canada? Between 1984-2008, the gender wage gap narrowed by 9.6 percentage points or in relative terms, by 13% Source: Marie Drolet, 2010.

Does the gap vary across the distribution? Upper end: women earn 25% less than men == Glass Ceiling Lower end: women earn 18% less than men == Sticky Floors Source: Drolet, 2001.

Among MBAs, the gender pay gap grows with experience Source: Bertrand, Goldin and Katz, 2010.

Executive gap varies by industry

Dominic Barton: Key findings from our latest research on women executives Who is Dominic Barton? Dominic Barton is the global managing director of McKinsey. He is based in London and leads the firm s focus on the future of capitalism and the role business leadership can play in creating long-term social and economic value. Dominic Barton also wrote the following Honours Thesis at UBC 217. 1984 An Examination of the Vulnerability of the International Financial System to Crisis Dominic Barton Look on YouTube for McKinsey Addressing Unconscious Bias

Executive gender gap remains, Catalyst report says Canadian companies making slow progress in promoting women CBC News Posted: Mar 3, 2011 11:46 AM ET Growth in the number of women who advance to the executive ranks at Canada's largest companies has slowed to a crawl in the past two years, a major study has found. The percentage of women holding senior officer positions increased less than one percentage point over two years, from 16.9 per cent in 2008 to 17.7 per cent in 2010. Further, female senior officers held 6.2 per cent of top earner positions in 2010 up less than one percentage point from 5.6 per cent in 2008. In both years, a full 30 per cent of the largest companies in Canada did not have a single woman in their executive ranks.

Women hold 8.5 per cent of the highest-paid positions in Canada s top 100 listed companies, according a report by global executive search firm Rosenzweig & Company. That s almost double the 4.6 per cent recorded in 2006, the first year Rosenzweig studied women in executive positions, and up from 7.4 per cent in 2011. But in real terms, it represents 45 women in top jobs such as chief executive, chief financial officer or vice-president of a company, including eight female CEOs.

Top 10% Income Share Over the past 20 years, top incomes in the US have soared 50% 45% 40% 35% 30% 25% Top 10% Pre-tax Income Share in the US, 1917-2013 1917 1922 1927 1932 1937 1942 1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007 2012 Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash market income including realized capital gains and excluding government transfers.

Mostly among the top 1% 25% Decomposing Top 10% into 3 Groups, 1913-2013 Share of total income for each group 20% 15% 10% 5% 0% Top 1% (incomes above $392,000 in 2013) Top 5-1% (incomes between $165,500 and $392,000) Top 10-5% (incomes between $116,500 and $165,500) 1913 1918 1923 1928 1933 1938 1943 1948 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash market income including

Average Income Real Top 1% Incomes Real Bottom 99% Incomes growth (or loss) Growth Growth Real Growth captured by top 1% p (1) (2) (3) (4) 1993-2013 15.1% 62.4% 7.3% 59% Clinton Expansion 1993-2000 31.5% 98.7% 20.3% 45% 2001 Recession 2000-2002 -11.7% -30.8% -6.5% 57% Bush Expansion 2002-2007 16.1% 61.8% 6.8% 65% Great Recession 2007-2009 -17.4% -36.3% -11.6% 49% Recovery 2009-2012 6.9% 34.7% 0.8% 91% Top tax increase 2012-2013 -3.2% -14.9% 0.2% 106% Computations based on family market income including realized capital gains (before individual taxes). Incomes exclude government transfers (such as unemployment insurance and social security) and non-taxable fringe benefits. Incomes are deflated using the Consumer Price Index. Table 1. Real Income Growth by Groups Column (4) reports the fraction of total real family income growth (or loss) captured by the top 1%. For example, from 2002 to 2007, average real family incomes grew by 16.1% but 65% of that growth accrued to the top 1% while only 35% of that growth accrued to the bottom 99% of US families. Source: Piketty and Saez (2003), series updated to 2013.

Increases in top incomes in other countries too!

Source: Lemieux and Riddell (2014)

But not all countries!

Governance and Social Norms Under very tax rates (over 50%), boards may find that higher CEO compensation is not the most effective use of firm revenues Perhaps stronger governance could bring discipline to increases in top income, and have more compensation send lower down the hierarchy The average worker would likely benefit more than a higher salary than a meager tax relief Different countries/firms have different views on the appropriate CEO/average worker salary ratio from 67 to 1 in Japan, to 206 to 1 in Canada, to in the United States

Increases in salaries are an important part of the picture 12% 10% 8% US Top 0.1% Pre-Tax Income Share and Composition Capital Gains Capital Income Business Income Salaries 6% 4% 2% 0% 1916 1921 1926 1931 1936 1941 1946 1951 1956 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 Source: Piketty and Saez, 2003 updated to 2013. Series based on pre-tax cash market income including or excluding realized capital gains, and always excluding government

Source: http://go.bloomberg.com/multimedia/ceo-pay-ratio/

Quotas for corporate boards advance?

2014 Catalyst Census: Women Board Directors

OSC proposes rules for women on boards, but stops short of quotas Barbara Shecter Financial Post January 16, 2014 10:19 AM ET Companies listed on the Toronto Stock Exchange will have to disclose how many women they have on their boards and in their executive ranks as well as set targets for the future, the Ontario Securities Commission said Thursday. But the proposals fall short of quotas, which some groups argued is the only way to ensure Canadian companies put more women on their boards. Under the proposals, which are subject to a 90-day comment period before being finalized, all companies listed on the TSX would have to disclose each year the number of women on their board and in executive positions, and their targets regarding the representation of women. They would also have to explain their corporate policies related to the issue, and disclose the board s or nominating committee s consideration of the representation of women in identifying and selecting directors. Our proposed amendments are intended to encourage more effective boards and better corporate decision making, which will benefit investors and the capital markets, said Howard Wetston, chair of the OSC. This is about helping TSX-listed issuers tap into a pool of talented and capable resources currently under-represented on today s boards and senior management.

OSC rebukes firms for lack of action on gender-diversity rules Janet McFarland Wednesday, Jun. 10, 2015 4:23PM EDT Many companies have shown bare technical compliance with new gender-diversity reporting rules introduced this year and it is simply not good enough, Ontario Securities Commission chair Howard Wetston says. Mr. Wetston said on Wednesday that the OSC has begun reviewing proxy circulars filed this year to see how companies are responding to the rules requiring them to disclose details about their policies to bolster women in senior roles, or else explain why they do not have policies in place.

Do quotas for corporate boards help women advance?

Differences-in-Differences Before reform After reform Treated Control Step 1: Simple Difference Outcome (example): LFP (female labor force participation) Two groups: Treatment group (T) which faces a change and control group (C) which does not Simple Difference estimate: D = LFP T - LFP C (P 2 P 1 ) captures treatment effect, if in the absence of treatment, LFP equal across 2 groups Note: this assumption always holds when T and C status is randomly assigned. To test for this assumption, we can compare LFP before the treatment: DD BB = LLLLLL BB TT LLLLLL BB cc (P 1 S 1 )

Differences-in-Differences Treated Control Step 2: Difference-in-Difference (DD) If DD BB 0, we can estimate DD: DDDD = DD AA DD BB = [LLLLLL TT AA LLLLLL cc AA ] [LLLLLL TT BB LLLLLL cc BB ] where A = after reform, B = before reform DD= (P 1 S 1 ) (P 2 S 2 ) Under the parallel trend assumption (P 1 S 1 ) = (Q S 2 ) the treatment effect is DD=(P 2 Q) Before reform After reform

Differences-in-Differences DD is unbiased if the parallel trend assumption holds: absent the treatment, the difference across T and C would have stayed the same before and after

Differences-in-Differences DD can be estimated by OLS to control for additional covariates LLLLLL iiii = ββ 0 After + ββ 1 Treat+γAfter Treat+ββ xx XX iiii +εε iiii It is easy to show that γ = DDDD. The assumption of parallel trends is reflected in the model by the fact that ββ 0 is the same for the treatment and control group DD most convincing when groups are very similar to start with [closer to randomized experiment]

Differences-in-Differences Should always test DD using data from more periods and plot the two time series to check parallel trend assumption [e.g. Imbens et al.] Use alternative control groups [not as convincing as potential control groups are many] In principle, can create a DDD as the difference between actual DD and DD Placebo (DD between 2 control groups)

Event Study Abnormal Returns Event studies are extremely common in finance research projects! They represent an attempt to gauge the effect of an identifiable event on a financial variable, usually stock returns

Event Study Abnormal Returns Abnormal returns, denoted AR it are calculated by subtracting an expected return from the actual return: AR it = R it E(R it ) The expected returns can be calculated in numerous ways; the market model is a common way to construct expected returns It uses a regression of the return to stock i on a constant and the return to the market portfolio: R it = α i + β i R mt + u it The abnormal returns are computed as uu iitt = R it [ αα ii + ββ ii RR mmmm ] or AAAA iiii = R it EE[RR iitt XX ii ] where uu ii, R it are the abnormal, actual returns respectively and EE[RR iiii XX ii ] are the normal returns, conditioning on other X s The cumulative average return (CAR) between T 1 and T 2 are calculated by averaging across firms first and then cumulating over time: where

Event Study Abnormal Returns In DD context, one further approach is to set up a control portfolio of firms that have characteristics as close as possible to those of the event firm for example, matching on firm size, beta, industry, book-to-market ratio, etc. and then using the returns on this portfolio as the expected returns