To help my students understand the impact of race and class upon life chances, I show excerpts from the People Like Us documentary. Of the clips that I usually show, the one that has grabbed my students’ attention the most is the story of Tammy Crabtree and her two sons living in Ohio. Viewers of the documentary may remember that Tammy walked several miles to reach her workplace, a minimum wage job at Burger King, and that her teenage son Matt voiced both shame about his family’s trailer-home poverty and his high hopes about his future.
Today, when answering an email inquiry by a school teacher about how to teach difficult issues to his students, I stumbled upon a recent update to Tammy and her family’s story. Tammy is still working at Burger King, although she has a shorter commute than before – a 20 minute walk from her house. Matt did not finish high school or attend college, contrary to what he had envisioned for himself, so that he could work to support his own child. Now, he exhibits greater compassion about his mother’s circumstances, showing a degree of introspection that most may not realize until very late in life. Both he and his brother emphasize family as a priority, as does Tammy.
Have a look at the family back in the late 1990s and now:
In a story full of neglect and willful ignorance, there are a few heroes. One is Mona Hanna-Attisha, the Flint pediatrician and Michigan State professor who raised the alarm with data on kids’ blood-lead levels from the local hospital. Another is Marc Edwards, the Virginia Tech environmental engineer who took on the Michigan Department of Environmental Quality after a Flint resident sent him a lead-rich water sample for testing.
Hanna-Attisha and Edwards provide shining examples of how academics can use science to hold the powers-that-be accountable and make meaningful change.
Taking on the status quo is hard. But as Edwards discusses in the Chronicle, it’s becoming ever-harder to do that from within universities:
I am very concerned about the culture of academia in this country and the perverse incentives that are given to young faculty. The pressures to get funding are just extraordinary. We’re all on this hedonistic treadmill — pursuing funding, pursuing fame, pursuing h-index — and the idea of science as a public good is being lost….What faculty person out there is going to take on their state, the Michigan Department of Environmental Quality, and the U.S. Environmental Protection Agency?…When was the last time you heard anyone in academia publicly criticize a funding agency, no matter how outrageous their behavior? We just don’t do these things….Everyone’s invested in just cranking out more crap papers.
When faculty defend academic freedom, tenure is often the focus. And certainly tenure provides one kind of protection for scientists like Hanna-Attisha (though she doesn’t yet have it) or Edwards who want to piss off the powerful.
But as this interview — and you should really read the whole thing — makes clear, tenure isn’t the only element of the academic ecosystem that allows people to speak out. Scientists can’t do their work without research funding, or access to data. When funders have interests — whether directly economic, as when oil and gas companies fund research on the environmental impacts of fracking, or more organizational, as when environmental agencies just don’t want to rock the boat — that affects what scientists can do.
So in addition to tenure, a funding ecosystem that includes multiple potential sources and that excludes the most egregiously self-interested will encourage independent science.
But beyond that, we need to defend strong professional cultures. Hanna-Attisha emphasizes how the values of medicine both motivated her (“[T]his is what matters. This is what we do … This is why we’re here”) and prompted her boss’s support (“Kids’ health comes first”), despite the “politically messy situation” that might have encouraged the hospital’s silence. Edwards lectures his colleagues about “their obligation as civil engineers to protect the public” and says, “I didn’t get in this field to stand by and let science be used to poison little kids.”
Intense economic pressures, though, make it hard to protect such this kind of idealism. As market and financial logics come to dominate institutions like hospitals and universities, professional values gradually erode. It takes a concerted effort to defend them when everything else encourages you to keep your head down and leave well enough alone.
Promoting academic independence isn’t without its downsides. Scientists can become solipsistic, valuing internal status over real-world impact and complacently expecting government support as their due. The balance between preserving a robust and independent academic sector and ensuring scientists remain accountable to the public is a delicate one.
But if I have to choose between two risks—that science might be a bit insular and too focused on internal incentives, or that the only supporters of science have a one-sided interest in how the results turn out—I’ll take the first one every time.
A few weeks ago, I spoke about open borders at Wellesley College as a guest of the Freedom Project. My talk summarized the view that open borders is a “common grounds” position. People who are liberal and conservative should support it. It is trans-ideological and bipartisan in nature. The liberal argument for open borders is very easy to defend. The best way to end poverty and lessen inequality is simply letting people move to places where they are more economically productive. For libertarians, the issue is equally straightforward. Migration restriction is nothing but a barrier to trade and personal freedom.
The case for conservatives is a little more subtle because there is no single intuition that motivates conservative critiques of migration. In my talk at Wellesley, I broke it down this way. Each bullet point merits a longer discussion, but I present the summary here:
- “Retail conservatives:” The rank and file conservative might oppose migration because immigrants reduce employment for natives, increase crime, or create undue stress on social services. In these cases, research either shows that there is simply no evidence to back it up or that negative effects are way, way overblown. Additionally, retail conservatives who promote family values and self-reliance should applaud immigrants because they improve their economic situation through hard work, not hand outs.
- “Philosophical conservatives:” There is a strand of more sophisticated, philosophical conservatives that are motivated by the writings of folks like Burke and Oakeshott. One might summarize their view as a suspicion of radical change and social engineering. If so, the they should vehemently oppose closed borders. What is more radical than drawing a line and proclaiming that people on one side can’t move to the other? Aren’t migration controls an attempt at social engineering by legislators? Don’t borders violate the organic social order of communities?
- “Cultural conservatives:” Some conservative migration critics are worried that migration might undermine the valuable things about Western culture. I think there are a few sensible responses. First, Western culture has survived socialism, fascism, communism and a whole lot more. America is much tougher than waves of low skilled labor. Second, in public opinion research, one often finds that migrants aren’t terribly different than natives in terms of political opinion. Third, Western societies tend to “chill out” migrants. If you want to decrease the anti-Western sentiment in the world, let people migrate to the West and their kids will be much less hostile than those back in the home country.
To sum up, there are a number of conservative criticisms of open borders and there are a lot of very intuitive and strong responses.
The Obama strategy in 2008 had a plan A and a plan B. Plan A was to knock out Hillary with big victories in Iowa and New Hampshire. Didn’t work. Plan B was to pad the delegate lead by exploiting small state caucuses and minimizing the damage in Hillary friendly places like New York. That worked, especially since the Hillary campaign was simply incompetent.
Sanders has a similar plan. His Plan A, the early knock out, almost worked. I suspect that Bernie might have even won the popular vote in Iowa, given that the Iowa Democratic Party is refusing to release vote tallies as they did in previous years. So Bernie is on to Plan B. That means he has to accomplish two things:
- Max out caucus states.
- Minimize losses in large primary states.
This is the list of remaining states in February and Super Tuesday and delegate totals for Democrats according to US election central:
- Alabama 60
- American Samoa caucus 10
- Arkansas 37
- Colorado caucus 79
- Georgia 116
- Massachusetts 116
- Minnesota caucus 93
- Nevada 43
- Oklahoma 42
- South Carolina 59
- Tennessee 76
- Texas 252
- Vermont 26
- Virginia 110
You will notice that Bernie has at least three easy states: Vermont, Massachusetts, and probably Minnesota. Then, it gets really hard, really fast. This is not because Hillary will magically become a great campaigner, but the fundamentals favor Hillary.
There are two reasons. First, you win Southern states in the Democratic primary by doing well among Black voters. South Carolina (Feb 27) will be the first test of how well Bernie can move these voters. If he comes up short in South Carolina, it’s bad news because you have more Southern states coming up real fast such as Alabama and Georgia on Super Tuesday and other Southern states soon after that. Second, in March, you will see the types of big states that Hillary dominated in 2008 because of superior name recognition, such as Texas (51% for HRC in 2008), New York (57%), California (51%), Ohio (53%), and Pennsylvania (54%).
Is it impossible for Bernie to win the nomination? Of course not, but he needs to really dominate outside of the establishment friendly mega-states like Ohio and California. That means an immediate and massive turn around in the Black vote, a wipe out in the caucus states, and some strategy for containing the losses from the big states, which even challenged Obama. That sounds really hard to me.
Ever since the publication of Piketty’s Capital in the 21st Century, there’s been a lot of debate about the theory and empirical work. One strand of the discussion focuses on how Piketty handles the data. A number of critics have argued that the main results are sensitive to choices made in the data analysis (e.g., see this working paper). The trends in inequality reported by Piketty are amplified by how he handles the data.
Perhaps the strongest criticism in this vein is made by UC Riverside’s Richard Sutch, who has a working paper claiming that some of Piketty’s major empirical points are simply unreliable. The abstract:
Here I examine only Piketty’s U.S. data for the period 1810 to 2010 for the top ten percent and the top one percent of the wealth distribution. I conclude that Piketty’s data for the wealth share of the top ten percent for the period 1870-1970 are unreliable. The values he reported are manufactured from the observations for the top one percent inflated by a constant 36 percentage points. Piketty’s data for the top one percent of the distribution for the nineteenth century (1810-1910) are also unreliable. They are based on a single mid-century observation that provides no guidance about the antebellum trend and only very tenuous information about trends in inequality during the Gilded Age. The values Piketty reported for the twentieth-century (1910-2010) are based on more solid ground, but have the disadvantage of muting the marked rise of inequality during the Roaring Twenties and the decline associated with the Great Depression. The reversal of the decline in inequality during the 1960s and 1970s and subsequent sharp rise in the 1980s is hidden by a fifteen-year straight-line interpolation. This neglect of the shorter-run changes is unfortunate because it makes it difficult to discern the impact of policy changes (income and estate tax rates) and shifts in the structure and performance of the economy (depression, inflation, executive compensation) on changes in wealth inequality.
From inside the working paper, an attempt to replicate Piketty’s estimate of intergenerational wealth transfer among the wealthy:
The first available data point based on an SCF survey is for 1962. As reported by Wolff the top one percent of the wealth distribution held 33.4 percent of total wealth that year [Wolff 1994: Table 4, 153; and Wolff 2014: Table 2, 50]. Without explanation Piketty adjusted this downward to 31.4 by subtracting 2 percentage points. Piketty’s adjusted number is represented by the cross plotted for 1962 in Figure 1. Chris Giles, a reporter for the Financial Times, described this procedure as “seemingly arbitrary” [Giles 2014].9 In a follow-up response to Giles, Piketty failed to explain this adjustment [Piketty 2014c “Addendum”].
There is a bit of a mystery as to where the 1.2 and 1.25 multipliers used to adjust the Kopczuk-Saez estimates upward came from. The spreadsheet that generated the data (TS10.1DetailsUS) suggests that Piketty was influenced in this choice by the inflation factor that would be required to bring the solid line up to reach his adjusted SCF estimate for 1962. Piketty did not explain why the adjustment multiplier jumps from 1.2 to 1.25 in 1930.
This comes up quite a bit, according to Sutch. There is reasonable data and then Piketty makes adjustments that are odd or simply unexplained. It is also important to note that Sutch is not trying to make inequality in the data go away. He notes that Piketty is likely under-reporting early 20th century inequality while over-reporting the more recent increase in inequality.
A lot of Piketty’s argument comes from international comparisons and longitudinal studies with historical data. I have a lot of sympathy for Piketty. Data is imperfect, collected irregularly, and prone to error. So I am slow to criticize. Still, given that Piketty’s theory is now one of the major contenders in the study of global inequality, we want the answer to be robust.
Over at Statistical Modelling, Andrew Gelman makes a very sensible point about peer review: it is as only as good as your peers. Why do psychologists worship p-values? Because they approve it in peer review. A few choice quotes:
In short, if an entire group of peers has a misconception, peer review can simply perpetuate error. We’ve seen this a lot in recent years, for example that paper on ovulation and voting was reviewed by peers who didn’t realize the implausibility of 20-percentage-point vote swings during the campaign, peers who also didn’t know about the garden of forking paths. That paper on beauty and sex ratio was reviewed by peers who didn’t know much about the determinants of sex ratio and didn’t know much about the difficulties of estimating tiny effects from small sample sizes.
To put it another way, peer review is conditional. Papers in the Journal of Freudian Studies will give you a good sense of what Freudians believe, papers in the Journal of Marxian Studies will give you a good sense of what Marxians believe, and so forth. This can serve a useful role. If you’re already working in one of these frameworks, or if you’re interested in how these fields operate, it can make sense to get the inside view. I’ve published (and reviewed papers for) the journal Bayesian Analysis. If you’re anti-Bayesian (not so many of theseanymore), you’ll probably think all these papers are a crock of poop and you can ignore them, and that’s fine.
Read the whole thing.