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- What is the War Powers Act of 1973, and why does it matter? | Thinking about libertarian foreign policy
- American and Russian soldiers are shooting at each other in Syria | Why care about Syrians?
- State decay and “patchwork” | Laws, Juridification, and the Administrative State
- Conservatives and their contempt for detail in governance | Fascism Explained
- No, fascism can’t happen here (in the US) | The Gradual, Eventual Triumph of Liberty
A few days ago, this study of gender pay differences for Uber drivers came out. The key finding, that women earned 7% less than men, was stunning because Uber uses a gender-blind algorithm. The figure below was the most interesting one from the study as it summarized the differences in pay quite well.
To explain this, the authors highlight a few explanations borne out by the data: men drive faster allowing them to have more clients; men have spent more time working for Uber and have more experience that may be unobserved; choices of where and when to drive matters. It is this latter point that I find fascinating because it speaks to an issue that I keep underlining regarding pay gaps when I teach.
For reasons that may be sociological or biological (I am agnostic on that), men tend to occupy jobs that have high rates of occupational mortality (see notably this British study on the topic) in the forms of accidents (think construction, firemen) or diseases (think miners and trashmen). They also tend to take the jobs in further removed areas in order to gain access to a distance premium (which is a form of risk in the sense that it affects family life etc.). The premiums to taking risky jobs are well documented (see notably the work of Kip Viscusi who measured the wage premium accruing to workers who were employed in bars where smoking was permitted). If these premiums are non-negligible but tend to be preferred by men (who are willing to incur the risk to be injured or fall sick), then risk preferences matter to the gender wage gap.
However, there are hard to properly measure in order to assess the share of the wage gap truly explained by discrimination. Here with the case of Uber, we can get an idea of the amplitude of the differences. Male Uber drivers prefer riskier hours (more risks of having an inebriated and potentially aggressive client), riskier places (high traffic with more risks of accidents) and riskier behavior (driving faster to get more clients per hour). The return to taking these risks is greater earnings. According to the study, 20% of the gap stems from this series of choices or roughly 1.4 percentage points.
I think that this is significantly large to warrant further consideration in the future in the debate. More often than not, the emphasis is on education, experience, marital status, and industry codes (NAICS code) to explain wage differences. The use of industry codes has never convinced me. There is wide variance within industries regarding work accidents and diseases. The NAICS codes industries by wide sectors and then by sub-sectors of activities (see for example the six-digits codes to agriculture, forestry, fishing and hunting here). This does not allow to take account of the risks associated with a job. There are a few study that try to account for this problem, but there are … well … few in numbers. And rarely are they considered in public discussions.
Here, the Uber case shows the necessity to bring back this subtopic in order to properly explain the wage gap.
A few years ago, I was teaching at HEC Montréal and I explained that putting people in prison – by statistical definition – did reduce unemployment. My students were shocked that I would say that. I told them that it was important to know definitions like that because you can then analyze the BS that politicians and pundits can spew.
And the case of Black-Americans is the best example, especially with regards to the wage gap. In recent years, I have seen pundits (left and right) use the slightly increasing ratio of black-to-white wages as a tool to promote their favored political narrative (i.e. the BS that I am referring to).
But, at the same time, the incarceration rates of Blacks has increased dramatically. Tell me, do you think that the socio-economic features of blacks in jail are distributed the same way as the socio-economic features of blacks not in jail? Of course not, criminals tend to be clustered disproportionately at the bottom of the income ladder. However, when its time to collect the wage statistics for blacks and whites, you are basically considering only the wages of blacks not in jail (i.e. blacks who are in the top centiles of the wage distribution). So, you’re basically committing a sin of statistical composition.
Some bloggers have caught on to that – the wage ratio is going up at the same pace as the incarceration rate for blacks. But they caught on after the work of scholars like Becky Pettit and Bruce Western came along (here and here and see graph below that illustrates the effect of correcting for incarceration on the employment rate of blacks).
When I look at this evidence, I understand why some people are pissed off at the conditions of Black Americans. It throws in doubt the contention that there has been racial convergence in America. At the same time, I wonder if the lack of recognition given to this statistical issue is a form of cognitive dissonance. If you claim that the convergence is mostly an artifice of composition fallacies, then what does it say about the policies of the last 30-40 years?
In a twitter-debate with Tariq Nasheed, I pointed out that the wages rates did converge between the 1940s and 1990s. Recently, Robert Margo of the University of Chicago extended this to per capita incomes since 1870. It is fascinating to see that there was convergence between 1870 and 1940 in spite of Jim Crow laws (it tells you how much more blacks could have achieved had the laws not existed – see notably the work of Bob Higgs on this).
Each time I see this evidence, I am bemused. You see, I often debate colleagues on particular features of social policy in order to assess policy reforms or the effects of past reforms. But, its always good to take a step back and look at the long-view of history. It puts things in perspective. The Margo graph does just that. It tells me the story of what could have been. And just for the sake of remembering properly (infer whatever conclusions you like), it is worth showing racial differences in unemployment rates since 1890. What strikes me is how similar the rates are until the 1950s. What happened at that point? When you ask yourself this question, you’re forced to put everything in perspective. And it becomes harder to have “generic” answers in the lazy-form of “its racism”. Why would racism explain the difference after 1950, but not before?
Maybe, just maybe, people like Tariq Nasheed should stop proving that H.L. Mencken was right in saying that “for every complex problem, there is an answer that is simple, clear and plainly wrong”.