Social Science

Subtle ways colleges discriminate against poor students Vox 9-11-17

Vox: "Subtle ways colleges discriminate against poor students"  

Educational AttainmentAmerica is not a meritocracy. The figure on left demonstrates that it is the father’s level of education that is the primary factor in determining what level of education the child will receive. “College is a finishing school for affluent families and a glass ceiling for everyone else.” Richard Reeves in his book The Dream Hoarders argues this is structural - the affluent use their wealth to gain favors unavailable to the wiring class. Think of structures like legacy admissions or zero-sum situations like using your county club connections to get your child that coveted internship that now cannot go to anyone else. Socioeconomic class controls the way we think, the way we see the world and the way we parent. Affluent parents stress the teaching of independent values like expressing yourself, questioning authority, solving your own problems. Low income parents stress interdependent values like working well with others and following authority (see Annette Lareau’s Unequal Childhoods). College admissions officers select for applicants with strong independent skill sets, not strong interdependent ones. This is a subtle but effective way that colleges discriminate against low income students.

College CompletionOnce in college, low income students suffer from lower completion rates. This connects back to socioeconomic status. Poor people don’t have a safety net, thus they are risk averse. They avoid speaking out. In college, such students are not going to advocate for themselves, not going to question a grade, not going to ask to speak to the professor. This behavior reinforces itself. As students respond to setbacks with isolation, it becomes ever more difficult to change their situations. Psychologists have a concept - “stereotype threat” - as low income students begin to struggle, they begin to believe that stereotypes about poor people (lazy, ignorant etc.) may actually be true. Finally, the motivations for going to college differ between low income students (I'm going to college to help my family) versus high income students (I'm going to colleg to expand my knowledge of the world). From the article: "In short, we just don't have a great idea of how advantaged or disadvantaged we are. But this means that, when lower-class students begin struggling in college, they blame themselves for their struggles. Gibbons says most of them were held to high standards in their hometowns and by their families, so asking for help feels like failure. So they feel they are failing because they aren't as capable. It reiterates the fear that they are the stereotype of the undereducated lower-class kid."

A study on first-generation college students

A study on college retention and graduation rates

Polling is getting more accurate, despite new challenges 6-17-17

Source: "Democracy's whipping boys"  

Three major polling failures in the last three years: first, British pollsters preducted a "remain" victory in June of 2016. Then, in November of 2016, some American pollsters showed Clinton with a 99% chance of victory. Finally, in June of 2018, the Conservatives were predicted to maintain control of Parliament. What happened? If we look at long-term results (see chart), polls have been getting better and better each year. 

Polls ImprovingPoll aggregation or averaging allowed Nate Silver to give Trump a 1-in-3 chance of winning in 2016. And it was a close election - change 78,000 votes in PA, MI and WI and Clinton wins the Electoral College. Her 2.1% win in the popular vote was within 1% of the outcome predicted by most polls. For the 2017 Parliament result, a witch of just 75 voters from Labour to Conservative in the districts with the narrowest Labour margins would have allowed the Conservatives to maintain their majority. As for Brexit, there were polls showing a toss-up but they were discounted by the media.

Current challenges: first, no one answers their phones. 72% of those called by phone in 1980 agreed to take part in a poll versus 8% today. Next, it's hard to get a representative sample. Some demographic groups are far more reluctant to give their opinion. It takes about 20 calls to find an elderly white woman who will participatre versus almost 350 calls to find a young Latino male. Online pollsters like YouGov assemble large, stable panels of each major demographic and ten weight the results based on how large or small that group is in the overall electorate. Weighting is also used by telephone pollsters to reduce non-response bias (that is, those groups with high non-response bias are given higher weights than those who readily participate). But compicated weighing schemes allow pollsters to adjust results to reduce extreme results. This can result in "herding" behavior. Finally, polls have a hard time accounting for voter turnout. Groups with traditionally ow turnout like uneducated whites in the US or the youth in the UK can have their preferences doscounted because of the low turnout. When their turnout is actually high, the polls can miss the mark.

 

Motivated reasoning 6/10/17

Source: "Free exchange: How to be wrong"           

Human thought is not always (or even usually) rational. Humans don’t use new information to update their beliefs - instead, they cling to their beliefs in the face of contradictory evidence. Jean Tirole and Roland Benabou wrote a landmark paper in 2016 that looks at beliefs as consumption goods. This new perspective yields interesting insights and predictions.

Beliefs are goods, in the sense that people spend resources acquiring them and developing them and they derive value and benefits from their beliefs. A typical value involves signaling membership in a group. A less common belief value would be the way believing in a religion can shape your behavior (if I believe I am a good salesman, I can use the confidence generated by my belief to close more sales; if I accept an ascetic religion, my beliefs can help me avoid unhealthy behaviors).

Benabou and Tirole believe people engage in motivated reasoning to protect their hard-won beliefs from contradictory evidence. The first stage of motivated reasoning is strategic ignorance: here, the believer simply ignores the evidence. In stage two, reality denial, the believer acknowledges the new evidence but refuses to accept it as coming from a credible source. Finally, in self-signalling, the evidence is accepted as credible but the believer interprets it as actually strengthening their belief, not contradicting it (example: an unhealthy person might decide that their ability to still run a few miles is proof that they are indeed healthy).

Other work by Benabou suggests that “groupthink” is highest when people within a group share the same fate if their briefs are discredited. If a politicians fortunes are linked to his party’s performance, they will have little incentive to speak out against the group. Such groups become partisan and polarized. Such groups try to delegitimize independent voices (like research groups or watchdog groups).

Book review: Everybody Lies 5/27/17

Article (book review): Everybody Lies  

 The book Everybody Lies by Seth Stephens-Davidowitz looks at the use of search data as a way to find previously invisible correlations and connections. Example: the prevalence of the term “n*gger” in search results was the best variable in predicting whether or not the voters in that region would vote for Trump in the 2016 GOP primaries. Search data is a game-changer because it gets at what people actually believe, not what they are willing to admit to a stranger with a clipboard.

From the review: ‘Modern microeconomics, sociology, political science and quantitative psychology all depend to a large extent on surveys of at most a few thousand respondents. In contrast, he says, there are “four unique powers of Big Data”: it provides new sources of information, such as pornographic searches; it captures what people actually do or think, rather than what they choose to tell pollsters; it enables researchers to home in on and compare demographic or geographic subsets; and it allows for speedy randomized controlled trials that demonstrate not just correlation but causality. As a result, he predicts, “the days of academics devoting months to recruiting a small number of undergraduates to perform a single test will come to an end.” In their place, “the social and behavioural sciences are most definitely going to scale,” and the conclusions researchers will be able to reach are “the stuff of science, not pseudoscience”.’