Gender Wage Gap

(by /u/mrregmonkey)

Gender Wage Gap FAQ

1. Overview of Economic Theory of Labor Market

2. Bad Controls

3. Audit Studies

4. Review of Claudia Goldin’s work

Theoretical Overview

Does a free market prevent wage gaps from forming, due to competition?

Let's assume perfectly competitive labor markets which have the following important properties.

i. Free Entry/Exit – No barriers to entry/exit (including entry/exit costs)

ii. Homogeneous work environments (so firms only compete on wages offered)

iii. Perfect information- All parties have the same information

iv. A large amount of buyers and sellers- no one can have a monopoly or significant market share

Under these conditions, 'taste based' discrimination cannot cause a wage gap. If marginalized workers work for discriminatory firms, nondiscriminatory firms will hire them away. This will put pressure to equalize wages between these groups, potentially running discriminatory firms out of business, though not necessarily. If discriminatory firms can hire enough of their preferred workers without causing a wage gap, they will stay in business. This is the fleshed out argument that free markets prevent discrimination, first formulated by Becker (1957)

What happens if we relax these perfectly competitive assumptions? For instance:

What if there are not enough nondiscriminatory firms to employ marginalized groups?

What if search costs are higher for marginalized groups?

There are significant barriers to entry or exit?

Then wage gaps due to taste-based discrimination is fully possible.

For example, if we allow for job search, marginalized groups will face harsher job searches and their employers will know they have less outside options, giving them financial incentives to create a wage gap, even if they have no taste for discrimination.

The important takeaway is that cannot assume economic outcomes from a deductive approach alone - the assumption we make could change our results. Instead, we need to look at empirical data. We also need to be clear about what assumptions we are making, because they matter quite a bit. By tweaking a single assumption (for instance, the cost of job search), we go from a model where discriminators suffer competitive disadvantages, to one where firms face financial incentives to discriminate. The need to look at data bring us to the most common way to do that, looking at the "raw" gender wage gap or the "controlled" gender wage gap, which is the next section.

Bad Controls

(or "Being Paid 77 cents on the Dollar and Controlling for Education: The Omitted Variable's Edition")

Many of the gender wage gap arguments on reddit boil down to one side asserting that the 77 cents to the dollar wage gap is pure discrimination and the other asserting that other things like education, hours worked, etc. have to be controlled for as they cause earnings to be higher. They are arguing that the 77 cents on a dollar claim isn’t looking at all relevant variables.

However, as Goldin (2014) notes, first that the gender wage gap has shrunk across time

The mantra of the women’s movement in the 1970s was “59 cents on the dollar” and a more recent crusade for pay equality has adopted “77 cents on the dollar.”

Then notes some potential reasons why.

As women have increased their productivity enhancing characteristics and as they “look” more like men, the human capital part of the wage difference has been squeezed out.

Something caused women to change their human capital decisions (decision affecting educational attainment etc.), causing the gender wage gap to shrink. These factors motivating these human capital decisions could very much matter too! For instance, increased work ethic would affect on the job performance and increase educational attainment. However, that is almost going to be impossible to control for. There is really no limit to this omitted variable problem. Even if you think of everything you could have missed something. So both sides of this argument suffer the same problems, namely there are omitted variables they didn't control for.

Furthermore, once you do not have every variable controlled for, you do not have a causal interpretation. It is possible that leaving out a control variable puts you closer to the unknown “true” effect. On the other hand, it is possible that leaving out an important variable puts you further away from the “true” effect. Both of these arguments suffer serious methodological problems.

There are two basic strategies to deal with this. One is to find a way to run experiments and try to measure what you can. An advantage of this is that there is no doubt you have gotten cause and effect right. A downside of this is you do not know how sensitive your findings are or if they scale to outside situations. You also might not know exactly how your cause and effect happens. These make it hard to know what policy to use. More on this is discussed in the Audit Studies section.

The other option, is to build a model and see how well this explains the data. This is very handy because it lets you easily know what to do from a policy perspective. You can easily see where factors are important and where they are not. The downside is, you could have assumed the wrong model. These models are testable because they ASSUME what variables are relevant and what ones aren't. So they limit the amount of variables you need to look at. However, you could leave a factor out. An example of this is Claudia Goldin’s work, which is discussed below.

Audit Studies

Given these flaws in just looking at wages directly and what limited information these provide, what can we know about the gender wage gap and other gaps? The answer is we need to use a different research methodology. First among these are audit studies.

An audit study is using secret shoppers to see if there is a difference between how the genders are treated. First otherwise identical resumes are used, with only the names varying, to allow gender to vary. An example could be one employer is randomly sent a resume with John Smith, while another is randomly sent a resume with Jane Smith. As employers are selected at random, their unique features are controlled for, provided the sample size is large enough. What these studies allow us to see are the average discrimination that an individual faces. These do not answer if a specific firm is a discriminator or not, but that in aggregate an identical woman gets more or less calls than an identical man. Sometimes these audit studies also hire actors to pretend to be the individuals on the resume. This allows us more information on if a woman is less likely to get hired, even after receiving a call back. Unfortunately, these studies are not perfect. The actors are not double blind (they know it’s a study) and may act different either consciously or unconsciously. Fortunately, the sending of resumes is not biased by this (as it predates the involvement of actors). This allows us to see if there is any discrimination on the average firm towards a particular gender. This is very important, as it allows us to know if there is discrimination in a given labor market or not.

However, even here faces drawbacks. Although this tell us the average discrimination a gender faces, it does not tell us the marginal discrimination a gender faces. This is because it does not take into account an agent’s ability to dodge discrimination. An agent is not going to take it’s average offer, but it’s best offer. For this reason, these studies identify the if a group faces discrimination or not, but not how much the discrimination lowers their wages.

Finally, a drawback is that these can only really study small labor markets. For example, this can study all of the tech postings on a job's website. Or all the job postings in a newspaper for a given time period. It is impossible to do an audit study on all available jobs. So these studies also can identify discrimination only within a narrow specific labor market, and even then, only really for call backs.

In sum, these studies are useful to change discourse from “is there discrimination or not” to “how much does discrimination matter.” They cannot identify how much discrimination changes labor market behavior on their own or identify the scope of discrimination on their own. They cannot even identify why discrimination happens. Is it because employers genuinely do not like women? Is it because women have less outside options? Is it for some other reason? These questions left unanswered make it difficult to write policy to solve this problem. Here is an example of an audit study, in practice.

Claudia Goldin’s Work

Claudia Goldin is a professor at Harvard and a leading researcher on the GWG. Her work (Goldin 2014) while important, is often misrepresented. For this reason is deserves it own section.

Claudia Goldin wanted to explore how due to gender roles, women value work flexibility more than men, affects the gender wage gap. So she constructs a model where some industries need people to work long continuous hours. An example is a MBA working as a consultant needs to work long hours on a project, as they can not have someone else take over. It’s just not feasible to get the other person up to speed, compared to paying more to one MBA and having them work really long hours. This causes wages to increase as hours increase, in industries like this. This means, someone with an MBA who works 30 hours a week might make $40 dollars an hour, but an MBA who works 70 hours a week might make $70. So they make more per hour in addition to working more hours. However, not all industries have increasing returns in hours. Some, like pharmacy, have constant returns in hours.

She looks at a few professions, with specific subsamples. Specifically she looks at J.D.s from University of Michigan, MBAs from University of Chicago and National Pharmacist Workforce Survey. This allowed her more precise information then what is usually available from the census survey. She empirically looks at the explanatory power of the model outlined above.

She finds that initially the earnings differences are small, but increase between the genders over time. She also finds that hours worked is correlated with family needs. For example, 15 years out of the program, MBA grads begin to have a large gender wage gap. This is correlated with women in the program having children, particularly if they also have a high income spouse. JD’s also have similar patterns. In short, it looks like women who have less labor supply see reduced earnings, especially when accounting for the various reductions in labor supply. This leads to large gender wage gaps in these industries, due to the increasing returns for hours worked.

Of particular interest is the occupation of pharmacy. This is because as she notes

Most pharmacists today work for non-independent retailers, mainly large chains, or in hospitals—about 75 percent do. But four decades ago around 25 percent were employed in these sectors. Self-ownership and employment by independent pharmacies declined greatly in the interim.

Pharmacy today, and pharmacy in the past are the same with how working less hours would affect your earnings. Now, with standardization of making drugs and electronic records, it is much easier for one pharmacist to finish up a job started by another pharmacist. As such, we would expect the gender wage gap to shrink in this industry and it has. This also allows female pharmacists to take time off without having to exit the labor force. This results in a very small gender wage gap, after controlling for hours.

In sum, Goldin’s work suggests another policy avenue to reduce the gender wage gap, to have more professions allow for workplace flexibility.

It is important to note that although this work is excellent, it can be taken out of context. First of all Goldin assumed a specific model of the labor market, namely one where women suffer gender wage gaps due to different valuations of leisure. That’s perfectly fine, because it helps her think about this specific mechanism that generates the generate wage gap. However, it could be that this model is misspecified in some way. She could have assumed the wrong model. The people citing her aren't aware of the assumptions she's making in her model (which brings us back to section 1)

Also, her data is not experimental like the data generated by audit studies, so we do not know if we are making correlation = causation type mistake here. So, despite what is often said, she did not out and out disprove discrimination. That said, it is really good work, and the fact it can explain so much of the data suggests there is certainly something to this idea about hours worked.