andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-32 knowledge-graph by maker-knowledge-mining
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Introduction: Aaron Edlin points me to this issue of the Journal of Economic Perspectives that focuses on statistical methods for causal inference in economics. (Michael Bishop’s page provides some links .) To quickly summarize my reactions to Angrist and Pischke’s book: I pretty much agree with them that the potential-outcomes or natural-experiment approach is the most useful way to think about causality in economics and related fields. My main amendments to Angrist and Pischke would be to recognize that: 1. Modeling is important, especially modeling of interactions . It’s unfortunate to see a debate between experimentalists and modelers. Some experimenters (not Angrist and Pischke) make the mistake of avoiding models: Once they have their experimental data, they check their brains at the door and do nothing but simple differences, not realizing how much more can be learned. Conversely, some modelers are unduly dismissive of experiments and formal observational studies, forgetting t
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1 We then turn to the key factors we see contributing to improved empirical work, including the availability of more and better data, along with advances in theoretical econometric understanding, but especially the fact that research design has moved front and center in much of empirical micro. [sent-19, score-0.596]
2 While Angrist and Pischke extol the successes of empirical work that estimate “treatment effects” based on actual or quasi-experiments, they are much less sanguine about structural analysis and hold industrial organization up as an example where “progress is less dramatic. [sent-58, score-0.554]
3 This seems to us a very narrow and dogmatic approach to empirical work; credible analysis can come in many guises, both structural and nonstructural, and for some questions structural analysis offers important advantages. [sent-60, score-0.624]
4 Sydnor The causes and consequences of gender disparities in standardized test scores — especially in the high tails of achievement — have been a topic of heated debate. [sent-71, score-0.67]
5 The existing evidence on standardized test scores largely confirms the prevailing stereotypes that more men than women excel in math and science while more women than men excel in tests of language and reading. [sent-72, score-0.512]
6 We provide a new perspective on this gender gap in test scores by analyzing the variation in these disparities across geographic areas. [sent-73, score-0.679]
7 In particular, states where males are highly overrepresented in the top math and science scores also tend to be states where women are highly overrepresented in the top reading scores. [sent-76, score-0.396]
8 There is a large gender gap that widens dramatically at percentiles above those that can be examined using standard data sources. [sent-81, score-0.382]
9 Explaining the Gender Gap in Math Test Scores: The Role of Competition Muriel Niederle and Lise Vesterlund The mean and standard deviation in performance on math test scores are only slightly larger for males than for females. [sent-86, score-0.498]
10 This gender gap has been documented for a series of math tests including the AP calculus test, the mathematics SAT, and the quantitative portion of the Graduate Record Exam (GRE). [sent-88, score-0.569]
11 Rather we argue that the reported test scores do not necessarily match the gender differences in math skills. [sent-90, score-0.711]
12 We will present results that suggest that the evidence of a large gender gap in mathematics performance at high percentiles in part may be explained by the differential manner in which men and women respond to competitive test-taking environments. [sent-91, score-0.687]
13 Of particular concern is that the distortion is likely to vary by gender and that it may cause gender differences in performance to be particularly large in mathematics and for the right tail of the performance distribution. [sent-95, score-0.778]
14 Thus the gender gap in math test scores may exaggerate the math advantage of males over females. [sent-96, score-0.91]
15 Empirical Industrial Organization: A Progress Report Liran Einav and Jonathan Levin The field of industrial organization has made dramatic advances over the last few decades in developing empirical methods for analyzing imperfect competition and the organization of markets. [sent-97, score-0.655]
16 The auditing profession is based on what looks like a structural infirmity: auditors are paid by the companies they audit. [sent-109, score-0.451]
17 This paper begins with an overview of the practice of audits, the auditing profession, and the problems that auditors continue to face in terms not only of providing audits of high quality, but also in providing audits that investors feel comfortable trusting to be of high quality. [sent-112, score-0.574]
18 It then turns t o a number of reforms that have been proposed, including ways of building reputation, liability reform, capitalizing or insuring auditing firms, and greater competition in the auditing profession. [sent-113, score-0.384]
19 White This paper will explore how the financial regulatory structure propelled three credit rating agencies — Moody’s, Standard & Poor’s (S&P;), and Fitch — to the center of the U. [sent-118, score-0.46]
20 bond markets — and thereby virtually guaranteed that when these rating agencies did make mistakes, these mistakes would have serious consequences for the financial sector. [sent-120, score-0.451]
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Introduction: Aaron Edlin points me to this issue of the Journal of Economic Perspectives that focuses on statistical methods for causal inference in economics. (Michael Bishop’s page provides some links .) To quickly summarize my reactions to Angrist and Pischke’s book: I pretty much agree with them that the potential-outcomes or natural-experiment approach is the most useful way to think about causality in economics and related fields. My main amendments to Angrist and Pischke would be to recognize that: 1. Modeling is important, especially modeling of interactions . It’s unfortunate to see a debate between experimentalists and modelers. Some experimenters (not Angrist and Pischke) make the mistake of avoiding models: Once they have their experimental data, they check their brains at the door and do nothing but simple differences, not realizing how much more can be learned. Conversely, some modelers are unduly dismissive of experiments and formal observational studies, forgetting t
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Introduction: John Horton points to Sims ‘s comment on Angrist and Pischke : Top of page 8—he criticizes economists for using clustered standard errors—suggests using multilevel models instead. Awesome! So now there are at least two Nobel prize winners in economics who’ve expressed skepticism about controlled experiments. (I wonder if Sims is such a danger in a parking lot.) P.S. I’m still miffed that this journal didn’t invite me to comment on that article!
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Introduction: Matthew Bogard writes: Regarding the book Mostly Harmless Econometrics, you state : A casual reader of the book might be left with the unfortunate impression that matching is a competitor to regression rather than a tool for making regression more effective. But in fact isn’t that what they are arguing, that, in a ‘mostly harmless way’ regression is in fact a matching estimator itself? “Our view is that regression can be motivated as a particular sort of weighted matching estimator, and therefore the differences between regression and matching estimates are unlikely to be of major empirical importance” (Chapter 3 p. 70) They seem to be distinguishing regression (without prior matching) from all other types of matching techniques, and therefore implying that regression can be a ‘mostly harmless’ substitute or competitor to matching. My previous understanding, before starting this book was as you say, that matching is a tool that makes regression more effective. I have n
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Introduction: Wayne Folta writes, “In light of your recent blog post on women in higher education, here’s one I just read about on a techie website regarding elementary education”: Why do girls get better grades in elementary school than boys—even when they perform worse on standardized tests? New research . . . suggests that it’s because of their classroom behavior, which may lead teachers to assign girls higher grades than their male counterparts. . . . The study, co-authored by [Christopher] Cornwell and David Mustard at UGA and Jessica Van Parys at Columbia, analyzed data on more than 5,800 students from kindergarten through fifth grade. It examined students’ performance on standardized tests in three categories—reading, math and science-linking test scores to teachers’ assessments of their students’ progress, both academically and more broadly. The data show, for the first time, that gender disparities in teacher grades start early and uniformly favor girls. In every subject area, bo
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Introduction: Blogger Echidne pointed me to a recent article , “The Distance Between Mars and Venus: Measuring Global Sex Differences in Personality,” by Marco Del Giudice, Tom Booth, and Paul Irwing, who find: Sex differences in personality are believed to be comparatively small. However, research in this area has suffered from significant methodological limitations. We advance a set of guidelines for overcoming those limitations: (a) measure personality with a higher resolution than that afforded by the Big Five; (b) estimate sex differences on latent factors; and (c) assess global sex differences with multivariate effect sizes. . . . We found a global effect size D = 2.71, corresponding to an overlap of only 10% between the male and female distributions. Even excluding the factor showing the largest univariate ES [effect size], the global effect size was D = 1.71 (24% overlap). Echidne quotes a news article in which one of the study’s authors going overboard: “Psychologically, men a
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Introduction: Aaron Edlin points me to this issue of the Journal of Economic Perspectives that focuses on statistical methods for causal inference in economics. (Michael Bishop’s page provides some links .) To quickly summarize my reactions to Angrist and Pischke’s book: I pretty much agree with them that the potential-outcomes or natural-experiment approach is the most useful way to think about causality in economics and related fields. My main amendments to Angrist and Pischke would be to recognize that: 1. Modeling is important, especially modeling of interactions . It’s unfortunate to see a debate between experimentalists and modelers. Some experimenters (not Angrist and Pischke) make the mistake of avoiding models: Once they have their experimental data, they check their brains at the door and do nothing but simple differences, not realizing how much more can be learned. Conversely, some modelers are unduly dismissive of experiments and formal observational studies, forgetting t
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Introduction: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when you change something, it is necessary to change it.” At the same time, in my capacity as a social scientist, I’ve published many applied research papers, almost none of which have used experimental data. In the present article, I’ll address the following questions: 1. Why do I agree with the consensus characterization of randomized experimentation as a gold standard? 2. Given point 1 above, why does almost all my research use observational data? In confronting these issues, we must consider some general issues in the strategy of social science research. We also take from the psychology methods literature a more nuanced perspective that considers several differen
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Introduction: Ole Rogeberg points me to a discussion of a discussion of a paper: Did pre-release of my [Rogeberg's] PNAS paper on methodological problems with Meier et al’s 2012 paper on cannabis and IQ reduce the chances that it will have its intended effect? In my case, serious methodological issues related to causal inference from non-random observational data became framed as a conflict over conclusions, forcing the original research team to respond rapidly and insufficiently to my concerns, and prompting them to defend their conclusions and original paper in a way that makes a later, more comprehensive reanalysis of their data less likely. This fits with a recurring theme on this blog: the defensiveness of researchers who don’t want to admit they were wrong. Setting aside cases of outright fraud and plagiarism, I think the worst case remains that of psychologists Neil Anderson and Deniz Ones, who denied any problems even in the presence of a smoking gun of a graph revealing their data
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Introduction: Michael Gilligan, Eric Mvukiyehe, and Cyrus Samii write : We [Gilligan, Mvukiyehe, and Samii] use original survey data, collected in Burundi in the summer of 2007, to show that a World Bank ex-combatant reintegration program implemented after Burundi’s civil war caused significant economic reintegration for its beneficiaries but that this economic reintegration did not translate into greater political and social reintegration. Previous studies of reintegration programs have found them to be ineffective, but these studies have suffered from selection bias: only ex-combatants who self selected into those programs were studied. We avoid such bias with a quasi-experimental research design made possible by an exogenous bureaucratic failure in the implementation of program. One of the World Bank’s implementing partners delayed implementation by almost a year due to an unforeseen contract dispute. As a result, roughly a third of ex-combatants had their program benefits withheld for reas
Introduction: Paul Alper writes: Unless I missed it, you haven’t commented on the recent article of Michael Bang Peterson [with Daniel Sznycer, Aaron Sell, Leda Cosmides, and John Tooby]. It seems to have been reviewed extensively in the lay press. A typical example is here . This review begins with “If you are physically strong, social science scholars believe they can predict whether or not you are more conservative than other men…Men’s upper-body strength predicts their political opinions on economic redistribution, they write, and they believe that the link may reflect psychological traits that evolved in response to our early ancestral environments and continue to influence behavior today. . . . they surveyed hundreds of people in America, Denmark and Argentina about bicep size, socioeconomic status, and support for economic redistribution.” Further, “Despite the fact that the United States, Denmark and Argentina have very different welfare systems, we still see that — at the psychol
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Introduction: Dave Berri posted the following at the Freakonomics blog: The “best” picture of 2012 was Argo. At least that’s the film that won the Oscar for best picture. According to the Oscars, the decision to give this award to Argo was made by the nearly 6,000 voting members of the Academy of Motion Picture Arts and Sciences. . . . In other words, this choice is made by the “experts.” There is, though, another group that we could have listened to on Sunday night. That group would be the people who actually spend money to go to the movies. . . . According to that group, Marvel’s the Avengers was the “best” picture in 2012. With domestic revenues in excess of $600 million, this filmed earned nearly $200 million more than any other picture. And when we look at world-wide revenues, this film brought in more than $1.5 billion. . . . Despite what seems like a clear endorsement by the customers of this industry, the Avengers was ignored by the Oscars. Perhaps this is just because I am an econo
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Introduction: Sandeep Baliga writes : [In a recent study , Gilles Duranton and Matthew Turner write:] For interstate highways in metropolitan areas we [Duranton and Turner] find that VKT (vehicle kilometers traveled) increases one for one with interstate highways, confirming the fundamental law of highway congestion.’ Provision of public transit also simply leads to the people taking public transport being replaced by drivers on the road. Therefore: These findings suggest that both road capacity expansions and extensions to public transit are not appropriate policies with which to combat traffic congestion. This leaves congestion pricing as the main candidate tool to curb traffic congestion. To which I reply: Sure, if your goal is to curb traffic congestion . But what sort of goal is that? Thinking like a microeconomist, my policy goal is to increase people’s utility. Sure, traffic congestion is annoying, but there must be some advantages to driving on that crowded road or pe
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