Results for “gender pay” 83 found
Why are Gender Pay Gaps so Large in Japan and South Korea?
From Alice Evans:
Singapore, Hong Kong and Taiwan are closing gender gaps in pay, seniority and parliamentary representation. Japan and South Korea, meanwhile, have the largest gender pay gaps in the OECD. Management remains 85% male. Female graduates are treated like secretaries, expected to pour the tea and run errands.
In Japan, a female graduate earns the same as a man who has only completed school. For Korean women aged 25-39, gender gaps in wages are indistinguishable between those with children and those without. In Europe, by contrast, there is a major penalty for motherhood.
Japanese businesses have lobbied again legislative change, even refusing sexual harassment training. Courts routinely deny systematic discrimination. Employers cannot even be sued for sexual harassment. Employees can only ask the Ministry of Labour for mediation. Accusations of abuse are mostly ignored.
At least part of the explanation stems from the lifetime full employment norms of Japan and South Korea, not present in Taiwan or Singapore, where those wage gaps are lower.
To summarise, Japan and South Korea have enshrined a system of lifetime employment and seniority pay for both blue collar and white collar workers. Firms are extremely sexist: men are treated as future managers, women are their subordinates. These inequalities are largest amongst low status regular workers. Fed up and frustrated, wives quit regular work to spend more time with their children and undertake non-regular, low paid work.
That is only part of the argument, here is the entire piece.
Gender pay gap observation
…according to a new analysis of 2,000 communities by a market research company, in 147 out of 150 of the biggest cities in the U.S., the median full-time salaries of young women are 8% higher than those of the guys in their peer group. In two cities, Atlanta and Memphis, those women are making about 20% more. This squares with earlier research from Queens College, New York, that had suggested that this was happening in major metropolises. But the new study suggests that the gap is bigger than previously thought, with young women in New York City, Los Angeles and San Diego making 17%, 12% and 15% more than their male peers, respectively. And it also holds true even in reasonably small areas like the Raleigh-Durham region and Charlotte in North Carolina (both 14% more), and Jacksonville, Fla. (6%).
The figures come from James Chung of Reach Advisors, who has spent more than a year analyzing data from the Census Bureau’s American Community Survey. He attributes the earnings reversal overwhelmingly to one factor: education. For every two guys who graduate from college or get a higher degree, three women do. This is almost the exact opposite of the graduation ratio that existed when the baby boomers entered college. Studies have consistently shown that a college degree pays off in much higher wages over a lifetime, and even in many cases for entry-level positions. “These women haven’t just caught up with the guys,” says Chung. “In many cities, they’re clocking them.”
Chung also claims that, as far as women’s pay is concerned, not all cities are created equal. Having pulled data on 2,000 communities and cross-referenced the demographic information with the wage-gap figures, he found that the cities where women earned more than men had at least one of three characteristics. Some, like New York City or Los Angeles, had primary local industries that were knowledge-based. Others were manufacturing towns whose industries had shrunk, especially smaller ones like Erie, Pa., or Terre Haute, Ind. Still others, like Miami or Monroe, La., had a majority minority population. (Hispanic and black women are twice as likely to graduate from college as their male peers.)
That is not the final word, but here is more from Time magazine.
The art sale gender pay gap
A Georgia O’Keeffe painting just sold for over $44 million, setting a new record for a painting by a woman; the previous record was for a Joan Mitchell painting auctioned for $11.9 million. A Francis Bacon once auctioned for $142.4 million, and so:
Despite the huge O’Keeffe sale, the cavern between the men’s and women’s records remains yawning. The gender pay gap is something like 84 cents to the dollar. The art sale “record gap” is now about 31 cents to the dollar. Before Thursday, it was 8 cents.
That is by Oliver Roeder, the full article is here.
Claudia Goldin on the gender pay gap
The pdf of her Philadelphia paper is here. This is from the concluding section:
The reasoning of this essay is as follows. A gender gap in earnings exists today that greatly expands with age, to some point, and differs significantly by occupation. The gap is much lower than it had once been and the decline has been largely due to an increase in the productive human capital of women relative to men. Education at all levels increased for women relative to men and the fields that women pursue in college and beyond shifted to the more remunerative and career-oriented ones. Job experience of women also expanded with increased labor force participation. The portion of the difference in earnings by gender that was once due to differences in productive characteristics has largely been eliminated.
What, then, is the cause of the remaining pay gap? Quite simply the gap exists because hours of work in many occupations are worth more when given at particular moments and when the hours are more continuous. That is, in many occupations earnings have a nonlinear relationship with respect to hours. A flexible schedule comes at a high price, particularly in the corporate, finance and legal worlds.
A compensating differentials model explains wage differences by the costs of flexibility. The framework developed here shows why there are higher or lower costs of time flexibility and the underlying causes of nonlinearity of earnings with respect to time worked. Much has to do with the presence of good substitutes for individual workers when there are sufficiently low transactions costs of relaying information. Evidence from O*Net on occupational characteristics demonstrates that certain features of occupations that create time demands and reduce the degree of substitution across workers are associated with larger gender gaps.
Data for MBAs and JDs shows large increases in gender pay gaps with time since degree and also reveals the relationship between the increasing gender pay gap and the desire for time flexibility due to the arrival of children. Lower hours mean lower earnings in a nonlinear fashion. Lower potential earnings, particularly among those with higher-earning spouses, often means lower labor force participation. Pharmacists, on the other hand, have pay that is more linear with respect to hours of work. Female pharmacists with children often work part-time and remain in the labor force rather than exiting.
The paper is interesting throughout.
Addendum: Mary Ann Bronson, a job candidate from UCLA, has a new and interesting paper (pdf) on the gender gap across college majors and related issues. Here is another UCLA job market paper, by Gabriela Rubio, on why arranged marriages decline in frequency. This year, at Duke University, there are more female entering students in the Ph.d. program than male.
Do pay transparency laws raise wages?
It seems not:
Labour advocates champion pay-transparency laws on the grounds that they will narrow pay disparities. But research suggests that this is achieved not by boosting the wages of lower-paid workers but by curbing the wages of higher-paid ones. A forthcoming paper by economists at the University of Toronto and Princeton University estimates that Canadian salary-disclosure laws implemented between 1996 and 2016 narrowed the gender pay gap of university professors by 20-30%. But there is also evidence that they lower salaries, on average. Another paper by professors at Chapel Hill, Cornell and Columbia University found that a Danish pay-transparency law adopted in 2006 shrank the gender pay gap by 13%, but only because it curbed the wages of male employees. Studies of Britain’s gender-pay-gap law, which was implemented in 2018, have reached similar conclusions.
Another misconception about pay-transparency laws is that they strengthen the bargaining power of workers. A recent paper by Zoe Cullen of Harvard Business School and Bobby Pakzad-Hurson of Brown University analysed the effects of 13 state laws passed between 2004 and 2016 that were designed to protect the right of workers to ask about the salaries of their co-workers. The authors found that the laws were associated with a 2% drop in wages, an outcome which the authors attribute to reduced bargaining power. “Although the idea of pay transparency is to give workers the ability to renegotiate away pay discrepancies, it actually shifts the bargaining power from the workers to the employer,” says Mr Pakzad-Hurson. “So wages are more equal,” explains Ms Cullen, “but they’re also lower.”
Here is more from The Economist.
The anatomy of gender discrimination
That is the topic of my latest Bloomberg column, here is one excerpt:
Maybe the men, on average, did have greater ambition and thus promotion potential. One reason could be that women, on average, spend more time at home raising children than men. For very demanding executive jobs, even a small difference in time and travel availability could make a big difference in job performance.
And yet even if that’s the case, there could still be a discrimination problem. Even if women and men differ on average, there is a probability distribution for each group, and those distributions usually will overlap. That is, there will be many women who are willing and able to meet any workplace standard thrown at them, and many men with limited ambition.
If you think men and women are different on average, the unfairness can become all the more severe for the potential top performers. In this context, employers will look at the most talented women and, for reasons of stereotyping, dramatically underestimate their potential, including for leadership positions.
Economic reasoning suggests another subtle effect at play. Promotion to the top involves a series of steps along a career ladder, often many steps. If there is a discrimination “tax” at each step, even if only a small one, those taxes can produce a discouraging effect. It resembles the old problem of the medieval river that has too many tolls on it, levied by too many independent principalities. The net effect can be to make the river too costly to traverse, even if each prince is taking only a small amount.
With a citation to Zaua further below!
“Potential” and the gender promotion gap
We show that widely-used subjective assessments of employee “potential” contribute to gender gaps in promotion and pay. Using data on 30,000 management-track employees from a large retail chain, we find that women receive substantially lower potential ratings despite receiving higher job performance ratings. Differences in potential ratings account for 30-50% of the gender promotion gap. Women’s lower potential ratings do not appear to be based on accurate forecasts of future performance: women outperform male colleagues with the same potential ratings, both on average and on the margin of promotion. Yet, even when women outperform their previously forecasted potential, their subsequent potential ratings remain low, suggesting that firms persistently underestimate the potential of their female employees.
Here is the paper by Benson, Li, and Shue. Via the excellent Samir Varma.
Politically Incorrect Paper of the Day: The Persistence of Pay Inequality
Gender wage gaps appear even in markets where workplace discrimination is impossible or unlikely. Uber driver’s for example are assigned trips using a gender-blind algorithm and earn according to a known formula based on time and distance of trip. Yet, a small but persistent gender gap of about 7% exists which appears to be due mostly to the fact that male drivers drive a little bit faster, choose to work in more congested areas, and have a bit more experience. Litman et al. (2020) show that the same kind of difference also show up in earnings on Mechanical Turk
In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.
The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%.
In this case, however, neither experience nor task choice nor demographics appears to explain the difference. One interesting finding is that women are more likely to choose tasks with a lower advertised pay–perhaps men are just a bit lazier. Who knows? People are different.
N.B. The authors go out of their way to plead that they are not in fact politically incorrect.
Girls’ comparative advantage in reading can largely explain the gender gap in math-related fields
In an earlier post, Do Boys Have a Comparative Advantage in Math and Science? I pointed to evidence showing that boys have a comparative advantage in math because they are much worse than girls at reading. (Boys do not have a large absolute advantage in math.) If people specialize in their personal comparative advantage this can easily lead to more boys than girls entering math training even if girls are equally or more talented. As I wrote earlier:
[C]onsider what happens when students are told: Do what you are good at! Loosely speaking the situation will be something like this: females will say I got As in history and English and B’s in Science and Math, therefore, I should follow my strengthens and specialize in drawing on the same skills as history and English. Boys will say I got B’s in Science and Math and C’s in history and English, therefore, I should follow my strengths and do something involving Science and Math.
A new paper in PNAS by Breda and Napp finds more evidence for the comparative advantage hypothesis. Breda and Napp look at intention to study math in ~300,000 students worldwide taking the PISA.
PISA2012 includes questions related to intentions to pursue math-intensive studies and careers. These intentions are measured through a series of five questions that ask students if they are willing (i) to study harder in math versus English/reading courses, (ii) to take additional math versus English/reading courses after school finishes, (iii) to take a math major versus a science major in college, (iv) to take a maximum number of math versus science classes, and (v) to pursue a career that involves math versus science. Our main measure of math intentions is an index constructed from these five questions and available for more than 300,000 students. It captures the desire to do math versus both reading and other sciences.
What they find is that comparative advantage (math ability relative to reading ability) explains math intentions better than actual math or reading ability. Comparative advantage is also a better predictor of math intentions than perceptions of math ability (women do perceive lower math ability relative to true ability than do men but the effect is less important than comparative advantage). In another data set the authors show that math intentions predict math education.
Thus, accumulating evidence shows that over-representation of males in STEM fields is perhaps better framed as under-representation of males in reading fields and the latter is driven by relatively low reading achievement among males.
As the gender gap in reading performance is much larger than that in math performance, policymakers may want to focus primarily on the reduction of the former. Systematic tutoring for low reading achievers, who are predominantly males, would be a way, for example, to improve boys’ performance in reading. A limitation of this approach, however, is that it will lower the gender gap in math-intensive fields mostly by pushing more boys in humanities, hence reducing the share of students choosing math.
The authors don’t put it quite so bluntly but another approach is to stop telling people to do what they are good at and instead tell them to do what pays! STEM fields pay more than the humanities so if people were to follow this advice, more women would enter STEM fields. I believe that education spillovers are largest in the STEM fields so this would also benefit society. It is less clear whether it would benefit the women.
Hat tip: Mary Clare Peate.
Google decides it is underpaying its men
When Google conducted a study recently to determine whether the company was underpaying women and members of minority groups, it found that more men than women were receiving less money for doing similar work.
The surprising conclusion to the latest version of the annual study contrasted sharply with the experience of women working in Silicon Valley and in many other industries.
In response to the finding, Google gave $9.7 million in additional compensation to 10,677 employees for this year. Men account for about 69 percent of the company’s work force, but they received a disproportionately higher percentage of the money. The exact number of men who got raises is unclear. [TC: I don’t fully understand the metric here.]
But the study did not tell the whole story of women at Google or in the technology industry more broadly, something that company officials acknowledged.
The Uber Pay Gap
Using data on over one million Uber drivers and millions of trips, Cody Cook, Rebecca Diamond, Jonathan Hall, John A. List, and Paul Oyer show that female Uber drivers earn 7% less than male drivers. What makes this paper new, however, is that UBER’s extensive data lets the authors understand in great detail why the pay gap exists. It’s not discrimination:
Uber uses a gender-blind algorithm and drivers earn according to a transparent formula based on the time and distance of trips. There are no negotiated pay rates or convex returns to long hours worked, factors that have been shown to open a gender earnings gap in other settings. Our research also finds that both average rider ratings of drivers and cancellation rates are roughly equivalent between genders and we find no evidence that outright discrimination, either by the app or by riders, is driving the gender earnings gap.
The authors find that three factors explain the gap; driving speed, experience, and choices about where to drive.
First, driving speed alone can explain nearly half of the gender pay gap. Second, over a third of the gap
can be explained by returns to experience, a factor which is often almost impossible to evaluate
in other contexts that lack high frequency data on pay, labor supply, and output. The remaining
∼20% of the gender pay gap can be explained by choices over where to drive.
Male Uber drivers, like other males, drive a bit faster than female drivers, about 2.2% faster after controlling for experience and location. Since Uber pays by time as well as by distance the returns to speed are not very high and the difference in speed is small but overall this results in an increase in pay for males of about 50 cents an hour.
Drivers learn by doing and more men than women have driven for Uber for years:
A driver with more than 2,500 lifetime trips completed earns 14% more per hour than a driver who
has completed fewer than 100 trips in her time on the platform, in part because she learn where
to drive, when to drive, and how to strategically cancel and accept trips. Male drivers accumulate
more experience than women by driving more each week and being less likely to stop driving with
Uber.
Overall, female and male Uber drivers behave remarkably similarly but small differences aggregated over large samples produce a small but systematic gender gap in wages of about 7%. The gap, however, is an artifact, a social construct that has no implications for “social justice,” drivers are treated equally.
The author’s conclude:
Overall, our results suggest that, even in the gender-blind, transactional, flexible environment
of the gig economy, gender-based preferences (especially the value of time not spent at paid work
and, for drivers, preferences for driving speed) can open gender earnings gaps. The preference
differences that contribute to pay differences in professional markets for lawyers and MBA’s also
lead to earnings gaps for drivers on Uber, suggesting they are pervasive across the skill distribution
and whether in the traditional or gig workplace.
The Gender Gap in STEM is NOT What You Think
In a new NBER working paper David Card and Abigail Payne have a stunning new explanation of the gender gap in STEM at universities. The conventional wisdom is that the gender gap is about women and the forces–discrimination, sexism, parenting, aptitudes, choices; take your pick–that make women less likely to study in STEM fields. Card and Payne are saying that the great bulk of the gap is actually about men and their problems. At least that is my interpretation of their results, the authors, to my mind, don’t clearly state just how much their results run against the conventional wisdom. (Have I misunderstood their paper? We shall see.)
The authors are using a large data set on Canadian high school students that includes data on grade 12 (level 4) high school classes and grades and initial university program. Using this data, the authors find that females are STEM ready:
…At the end of high school, females have nearly the same overall rate of STEM readiness as males, and
slightly higher average grades in the prerequisite math and science courses. The mix of STEM related courses taken by men and women is different, however, with a higher concentration of women in biology and chemistry and a lower concentration in physics and calculus.
Since females are STEM-ready when leaving high school you are probably thinking that the gender gap must be a result either of different entry choices conditional on STEM-readiness or different attrition rates. No. Card and Payne say that entry rates and attrition rates are similar for males and females. So what explains why males are more likely to take a STEM degree than females?
The main driver of the gender gap is the fact that many more females (44%) than males (32%) enter university. Simply assuming that non‐STEM ready females had the same university entry rate as non‐STEM ready males would
narrow the gender gap in the fraction of university entrants who are STEM ready from 14
percentage points to less than 2 percentage points.
Moreover:
On average, females have about the same average grades in UP (“University Preparation”, AT) math and sciences courses as males, but higher grades in English/French and other qualifying courses that count toward the top 6 scores that determine their university rankings. This comparative advantage explains a substantial share of the gender difference in the probability of pursing a STEM major, conditional on being STEM ready at the end of high school.
Put (too) simply the only men who are good enough to get into university are men who are good at STEM. Women are good enough to get into non-STEM and STEM fields. Thus, among university students, women dominate in the non-STEM fields and men survive in the STEM fields. (The former is mathematically certain while the latter is true only given current absolute numbers of male students. If fewer men went to college, women would dominate both fields). I don’t know whether this story will hold up but one attractive feature, as a theory, is that it is consistent with the worrying exit from the labor market of men at the bottom.
If we accept these results, the gender gap industry is focused on the wrong thing. The real gender gap is that men are having trouble competing everywhere except in STEM.
Hat tip: Scott Cunningham.
Pay equality and treatment inequality within the corporation
One question is how much firms pay men and women, relative to their marginal products; here are some previous MR posts on that topic. A second question, neglected somewhat by economists (but not ignored more generally), is how men and women (and other genders) treat each other within the firm.
To go down this purely hypothetical path, let’s say a firm pays women their full and fair market value, but the firm is embedded in a city where men lord it over women, perhaps because of income inequality and unequal ratios in the dating market. Within that company, men may treat women in unfair ways. They may harass them, or simply listen to them less, or perhaps refuse to serve as mentors. The surrounding urban culture makes this a stable equilibrium, because the men in this company do not need the women so much for their preferred “total life portfolio” of gender relations. For purposes of contrast, if men need their particular workplace to date and marry suitable women, or even just have them as friends or mentors, those men will treat these women more courteously. (NB: is there an equilibrium where this attention leads to worse treatment? Maybe sometimes the women simply prefer to be ignored. I have heard that male tourists in Bangkok do not hit on the female tourists they meet there, for instance.)
The (fair) firm will fire egregious male offenders of the company’s norms, but some of these offenses are neither observable nor contractible. So imagine the firm setting the wage first, and then the men within that company claw back some of the employee surplus of the women by harassing those women.
The more fairly the company pays women, the more men within that company can harass the women, if only because the women are less likely to leave the company (the participation constraint).
So some of the benefit of paying women their fair share ends up distributed to those men, within the company, who wish to harass or otherwise mistreat those women. And of course the unfair treatment of women does not have to come from harassers, or even from men. It also could come from other women, or from relatively impersonal processes, such as inquiries and tribunals, which perhaps in some manner, possibly unintentionally, are less well geared to represents the interests of women. So you should interpret that word “harassment” in the broadest possible sense.
One prediction of this model is a good deal of harassment in sectors that have relatively strong pay equity norms. Furthermore, men bent on harassment or mistreatment of women may be among the biggest supporters of pay equity within their institutions.
The greater you think is the scope for potential male mistreatment of women, the weaker is the pecuniary case for pay equity norms, at least in a short-run, partial equilibrium setting (you might think in the longer run you can shift all the norms with tough, across-the-board enforcement).
Conversely, imagine you can shift the norms within a company so the male employees treat the female employees better. That weakens the pressures for the company to pay the women their full marginal products. Even a company bent on “being fair” may find it hard to spot the underpaid women, because they are not always leaving and revealing that better pay should be offered.
A broader point is that the ethos of a company can only deviate from the ethos of its geographic location by so much.
Of course that is all just in the model, the real world is quite different. In the real world, selection and clustering effects overwhelm the logic of compensating differentials, so there are good institutions and bad institutions. The good institutions pay the women what they deserve, and have stronger norms against harassment and bad treatment. All goes well there, and for that reason it is not hard to tell which are the good institutions and the bad institutions.
Publishing pays in economics
Here is a new paper by Suzanne O’Keefe and Ta-Chen Wang:
We study salaries of economics faculty at the University of California to determine how publications affect salary. We find that each publication in a top 10 journal has a positive and significant effect on annual base salary of 1.5%, or $2,053. Unlike previous research, our analysis specifies the impact of publications in specific journals. Publications in American Economic Review, Econometrica, and Review of Economics and Statistics have an independent positive effect on salary. Compensation is also affected by faculty rank, seniority, university of employment, and teaching awards. Base salary does not significantly differ by gender, however, gross salary is about 9% lower for women. After controlling for migration and faculty rank, seniority has a negative impact on salary.
Here is a sentence of interest:
Full-time tenure-track economics faculty members in the UC system have gross salaries ranging from about $70,000 to $378,000.
Against my expectations, UCLA economics professors are paid more than 13k more, on average, than UC Berkeley economics professors. The pay gap for women is larger in economics than in these universities as a whole.
The possibly gated article is here, and for the pointer I thank Michelle Dawson.
Labor market outcomes for transgendered individuals
Yes economists study this too:
We use the workplace experiences of transgender people – individuals
who change their gender typically with hormone therapy and surgery – to
provide new insights into the long-standing question of what role
gender plays in shaping workplace outcomes. Using an original survey of
male-to-female and female-to-male transgender people, we document the
earnings and employment experiences of transgender people before and
after their gender transitions. We find that while transgender people
have the same human capital after their transitions, their workplace
experiences often change radically. We estimate that average earnings
for female-to-male transgender workers increase slightly following
their gender transitions, while average earnings for male-to-female
transgender workers fall by nearly 1/3. This finding is consistent with
qualitative evidence that for many male-to-female workers, becoming a
woman often brings a loss of authority, harassment, and termination,
but that for many female-to-male workers, becoming a man often brings
an increase in respect and authority. These findings challenge the
omitted variables explanations for the gender pay gap and illustrate
the often hidden and subtle processes that produce gender inequality in
workplace outcomes.
Here is the article. I’m not so sure this solves the identification problem, since it ends up looking at atypical individuals (those who switch to female may not be the same personality types as those who switch to male). But, on this topic, what do I know?
I thank Zuzanna for the pointer.