Category: Data Source
Private Schools in Developing Countries
Tina Rosenberg has an excellent piece on private schooling in developing countries at the NYTimes blog:
In the United States, private school is generally a privilege of the rich. But in poorer nations, particularly in Africa and South Asia, families of all social classes send their children to private school….
BRAC used to be an acronym for Bangladesh Rural Advancement Committee, but now the letters stand alone. It was founded in 1972 to provide relief after Bangladesh’s war of liberation. Although you’ve probably never heard of it, BRAC is the largest nongovernmental organization in the world, with some 100,000 employees, and it services reach 110 million people.
…And since 1985, it has run schools… BRAC has more than 1.25 million children in its schools in Bangladesh and six other countries, and it is expanding.
BRAC students, in fact, do better than their public-school counterparts….BRAC students are more likely to complete fifth grade — in 2004, 94 percent did, as opposed to 67 percent of public school students. (The BRAC number is now about 99 percent.) On government tests, BRAC students do about 10 percent better than public school students — impressive, given that their population is the most marginalized. (emphasis added).
In my own work on private schools in India I also found suggestive evidence that private schools–mostly very small, urban slum schools–produced better outcomes than their public counterparts (paper (pdf), video).
Is the Fed able to offset “austerity”?
David Beckworth serves up another very good blog post and directs us to this graph of nominal gdp; it seems aggregate demand has been recovering steadily:
Scott Sumner directs us to Marcus Nunes, but here is a quotation from Scott:
In 1937 real government purchases recoiled 4.2% and the economy tanked. In 2012 real government purchases were 4.8% below the 2010 level and the recovery is slow!
Surely something is going on that´s making comparable ‘fiscal austerity’ so much less damning in 2012 than in 1937.
And that ‘something’ is monetary policy.
Here are further remarks from Scott.
The decline in public investment at the state and local levels
Matt Yglesias has the scoop, and here is his chart:
You can think of that as another way of viewing the lost economic decade of the oughties.
American Austerity (and Growth)
The red line in the chart above is Paul Krugman’s preferred measure of austerity, the ratio of overall government expenditure to potential GDP. The idea of potential GDP has plenty of problems and biases but I want to be more than fair. In his post on American Austerity Krugman warns:
the truth is that federal stimulus is years behind us, while state and local governments have cut back, so the overall story is one of fiscal contraction that’s smaller than in Europe, but not by that much.
…Spending is down to what it was before the recession, and also significantly lower than it was under Reagan. Bear in mind that in the years since the recession began we’ve seen a significant number of boomers reach retirement age, which would ordinarily have led to rising spending, not to mention the effects of rising health care costs. Bear in mind also that the private sector is still deleveraging, which means that government should be spending more to help sustain the economy. So this is actually a picture of very bad policy. (emphasis added)
I assume that by very bad policy what Krugman means is a policy that is likely to have very bad effects. Hence, I have added to Krugman’s graph the growth rate of real gdp (annual rate). I don’t see the very bad effects. In the 1990s growth was strong even while “austerity” was increasing (falling red line) [as this sentence appears to be driving people mad do note that it is a factual description of the data from which I do not draw a conclusion]. More recently, we have seen a big increase in austerity according to Krugman and his measure but although there has been no boom, growth has remained modest. As Justin Wolfers tweeted this morning with the strong jobs report, “the recovery has been remarkably persistent, and resilient,” albeit not rapid. Scott Sumner argues that this is bye, bye Keynesian multiplier as monetary policy stands triumphant (also here) which is one possible interpretation.
“Investigating America’s elite”
That is a new paper by Jonathan Wai, from the latest issue of Intelligence, with the subtitle “Cognitive ability, education, and sex differences,” and here is the abstract:
Are the American elite drawn from the cognitive elite? To address this, five groups of America’s elite (total N = 2254) were examined: Fortune 500 CEOs, federal judges, billionaires, Senators, and members of the House of Representatives. Within each of these groups, nearly all had attended college with the majority having attended either a highly selective undergraduate institution or graduate school of some kind. High average test scores required for admission to these institutions indicated those who rise to or are selected for these positions are highly filtered for ability. Ability and education level differences were found across various sectors in which the billionaires earned their wealth (e.g., technology vs. fashion and retail); even within billionaires and CEOs wealth was found to be connected to ability and education. Within the Senate and House, Democrats had a higher level of ability and education than Republicans. Females were underrepresented among all groups, but to a lesser degree among federal judges and Democrats and to a larger degree among Republicans and CEOs. America’s elite are largely drawn from the intellectually gifted, with many in the top 1% of ability.
I don’t yet see this paper on-line, but here is some summary coverage.
Interpreting Statistical Evidence
Betsey Stevenson & Justin Wolfers offer six principles to separate lies from statistics:
1. Focus on how robust a finding is, meaning that different ways of looking at the evidence point to the same conclusion.
In Why Most Published Research Findings are False I offered a slightly different version of the same idea
Evaluate literatures not individual papers.
SWs second principle:
2. Data mavens often make a big deal of their results being statistically significant, which is a statement that it’s unlikely their findings simply reflect chance. Don’t confuse this with something actually mattering. With huge data sets, almost everything is statistically significant. On the flip side, tests of statistical significance sometimes tell us that the evidence is weak, rather than that an effect is nonexistent.
That’s correct but there is another point worth making. Tests of statistical significance are all conditional on the estimated model being the correct model. Results that should happen only 5% of the time by chance can happen much more often once we take into account model uncertainty not just parameter uncertainty.
3. Be wary of scholars using high-powered statistical techniques as a bludgeon to silence critics who are not specialists. If the author can’t explain what they’re doing in terms you can understand, then you shouldn’t be convinced.
I am mostly in agreement but SW and I are partial to natural experiments and similar methods which generally can be explained to the lay public while other econometricians (say of the Heckman school) do work that is much more difficult to follow without significant background and while being wary I also wouldn’t reject that kind of work out of hand.
4. Don’t fall into the trap of thinking about an empirical finding as “right” or “wrong.” At best, data provide an imperfect guide. Evidence should always shift your thinking on an issue; the question is how far.
Yes, be Bayesian. See Bryan Caplan’s post on the Card-Krueger minimum wage study for a nice example.
5. Don’t mistake correlation for causation.
Does anyone still do this? I know the answer is yes. I often find, however, that the opposite problem is more common among relatively sophisticated readers–they know that correlation isn’t causation but they don’t always appreciate that economists know this and have developed sophisticated approaches to disentangling the two. Most of the effort in a typical empirical paper in economics is spent on this issue.
6. Always ask “so what?” …The “so what” question is about moving beyond the internal validity of a finding to asking about its external usefulness.
Good advice although I also run across the opposite problem frequently, thinking that a study done in 2001 doesn’t tell us anything about 2013, for example.
Here, from my earlier post, are my rules for evaluating statistical studies:
1) In evaluating any study try to take into account the amount of background noise. That is, remember that the more hypotheses which are tested and the less selection which goes into choosing hypotheses the more likely it is that you are looking at noise.
2) Bigger samples are better. (But note that even big samples won’t help to solve the problems of observational studies which is a whole other problem).
3) Small effects are to be distrusted.
4) Multiple sources and types of evidence are desirable.
5) Evaluate literatures not individual papers.
6) Trust empirical papers which test other people’s theories more than empirical papers which test the author’s theory.
7) As an editor or referee, don’t reject papers that fail to reject the null.
The follow-up study on Medicaid coverage in Oregon
Here is some overview coverage from Annie Lowrey, an important issue of course with some striking results. Here is coverage from Sarah Kliff. Here is commentary from Justin Wolfers, and here. After the R&R saga, I say it’s time for someone to stand up and admit “We have some egg on our face with this one.”
Addendum: Reading more carefully through the quotations from Finkelstein and Holahan in the Lowrey piece, I find it amazing, and I suppose even embarrassing, what those commentators are claiming as a positive result. Of course it is worth comparing the program to simply giving people the cash.
Are public sector employees paid more?
Bryan Caplan has studied the literature and he quotes the summary of Philipp Bewerunge and Harvey Rosen:
The literature on wage differentials between public and private sector employees spans roughly four decades, originating with Smith’s [1976a, 1976b, 1977] seminal series of papers. The core of her analysis is the estimation of conventional human capital earnings functions. For example, in Smith [1976b] she uses 1973 Current Population Survey (CPS) data to estimate for each gender a regression of the logarithm of the wage on various worker characteristics such as years of schooling and race, including a series of dichotomous variables indicating whether each individual worked in the federal, state, or local government sectors (the private sector is the omitted category). For males, she finds wage differentials relative to the private sector of 19 percent in federal government and -4.9 percent in local government. The coefficient on the state government variable is statistically insignificant. The differentials for female workers are 31 percent in federal government, 12 percent in state government, and 3.6 percent in local government…
Papers subsequent to Smith’s have modified her approach by trying to correct for self-selection of workers into various sectors, by using panel data to estimate fixed effects models, and by estimating models on a state-by-state basis to allow for the possibility that labor market institutions, and hence public sector wage differentials, vary across states. A fair way to summarize the findings in this literature is as follows: a robust result, found in almost all the research from Smith’s early papers on, is that there is a substantial positive wage differential for federal employees, even after controlling for worker characteristics in the standard way.
I recall this characterization not receiving wide circulation during the recent disputes over Wisconsin and the like, so I thought I would pass it along.
Who shares data?
Perhaps I’ve linked to this before, I am not sure, but it is worth another look:
We provide evidence for the status quo in economics with respect to data sharing using a unique data set with 488 hand-collected observations randomly taken from researchers’ academic webpages. Out of the sample, 435 researchers (89.14%) neither have a data&code section nor indicate whether and where their data is available. We find that 8.81% of researchers share some of their data whereas only 2.05% fully share. We run an ordered probit regression to relate the decision of researchers to share to their observable characteristics. We find that three predictors are positive and significant across specifications: being full professor, working at a higher-ranked institution and personal attitudes towards sharing as indicated by sharing other material such as lecture slides.
That is from Patrick Andreoli Versbach and Frank-Müller Lange, with a thanks to Florens Sauerbruch for the pointer.
Here (via @autismcrisis) is a new paper by John Ioannidis and Chris Doucouliagos, “What’s to Know About the Credibility of Empirical Economics?”, possibly gated for you, here is the abstract:
The scientific credibility of economics is itself a scientific question that can be addressed with both theoretical speculations and empirical data. In this review, we examine the major parameters that are expected to affect the credibility of empirical economics: sample size, magnitude of pursued effects, number and pre-selection of tested relationships, flexibility and lack of standardization in designs, definitions, outcomes and analyses, financial and other interests and prejudices, and the multiplicity and fragmentation of efforts. We summarize and discuss the empirical evidence on the lack of a robust reproducibility culture in economics and business research, the prevalence of potential publication and other selective reporting biases, and other failures and biases in the market of scientific information. Overall, the credibility of the economics literature is likely to be modest or even low.
An update on the Reinhart and Rogoff critique and some observations
My previous post presented this:
Rortybomb summarizes it here, Matt Yglesias here, and the original paper is here (pdf), by Thomas Herndon, Michael Ash, and Robert Pollin. I will read the paper soon.
I’ve now had some time to look at the paper, and here are a few observations:
1. I am of course open to publishing a rebuttal from R&R, but on a first read the authors make a strong case for their claim that the core Reinhart-Rogoff result — concerning the growth slowdown at debt at 90% of gdp — is based on a coding error and some data exclusion issues. Please reread my earlier post on “the smell test.”
2. That said, as Ray Lopez mentions, including in the data the postwar bouncebacks of some Anglo countries (NZ, Australia, and Canada), as recommended by the critics, is not obviously going to improve the quality of the answer. For instance the Kiwis have postwar growth rates of 7.7, 11.9, -9.9, and 10.8 percent, across the late 1940s. Are those numbers — which were combined with high postwar levels of debt — relevant to current fiscal policy issues? I say no, while admitting this may lead us to throw out other data points as well. I don’t know what is the non-cherry-pick answer here or if there even is one.
3. It is perhaps unfortunate in this age of the internet that rebuttals must be presented so quickly, but so be it. It will be interesting to hear from R&R.
4. Not too long ago I reread R&R to ascertain whether they actually present the 90% level as an emergency cliff of sorts. I concluded they did not, although there were some sentences that a reader could take out of context toward confirming such an interpretation.
5. In the paper by the critics, the pp.7-9 discussion of “weighting by country” vs. “weighting by country-year” is very interesting, but the fact that it matters as much as it does makes me more skeptical about the entire enterprise. Whether you should weight by population is important too.
6. I am seeing a large number of tweets which both misrepresent R&R or misrepresent their influence on current policies of “austerity.”
7. My own view, as you can read in The Great Stagnation, is that the primary mechanism is slow growth causing high debt/gdp ratios, not vice versa. In any case this is by far the most important issue, whether or not you agree with my take on it.
8. The “case for austerity” didn’t rest much on R&R in the first place, rather on the notion that the bills have to be paid, dawdling on adjustment is not always so easy, and the feasible sum of international redistribution is quite low. For this reason the UK should be relatively uninterested in immediate austerity and many nations in the eurozone periphery more interested.
9. In the blogosphere, the ratio of blog posts “attacking austerity” to “proposing constructive alternatives to austerity” is at least ten to one. That too tells you something. Many of the alternatives proposed would indeed pass a Benthamite cost-benefit test, at least if implemented as desired, but they are simply inconsistent with incentives and the relatively selfish nature of individual behavior.
10. The most interesting question to me is a rather squirrelly and subjective one: how should this episode change the relative ratios of what I read? Should I in fact read fewer quantitative economics papers, instead (at the margin, of course) preferring more narrative history? This is not the first time that an extremely influential major empirical result has been overturned or at least thrown into serious doubt.
Addendum: FT Alphaville weighs in. And Annie Lowrey is tweeting some responses from R&R.
A critique of Reinhart and Rogoff
Rortybomb summarizes it here, Matt Yglesias here, and the original paper is here (pdf), by Thomas Herndon, Michael Ash, and Robert Pollin. I will read the paper soon.
How are American parents different?
The biggest difference between American parents and their counterparts in Europe might be that they are far more relaxed about enrichment than we are, according to a study released this week by Sara Harkness and Charles M. Super at the School of Family Studies at the University of Connecticut.
Not only are Americans far more likely to focus on their children’s intelligence and cognitive skills, they are also far less likely to describe them as “happy” or “easy” children to parent.
“The U.S.’s almost obsession with cognitive development in the early years overlooks so much else,” Harkness told Slate .
For part of their research, the authors focused just on parents in the United States and the Netherlands. The differences are stark: American parents emphasized setting aside “special time” with each of their children, while Dutch parents spent a few hours each day together with their kids as an entire family.
…American parents were the only ones to consistently mention their children’s advanced intellect, while other countries focused on qualities like “happiness,” being “easy” to manage, or the even more zen-like “well-balanced,” in Italy. (Italians also used the word simpatico, a group of characteristics suggesting social and emotional competence).
The article, by Olga Khazan, is interesting throughout and for the pointer I thank an excellent and loyal MR reader.
Levitt and Fryer on race and IQ
From their April 2013 AER piece:
Analyzing these data, we find extremely small racial differences in mental functioning of children age 8 to 12 months. Absent controls, the mean white infant outscores the mean black infant by 0.055 standard deviation units — only a sliver of the one-standard-deviation racial gap typically observed at older ages. The raw scores for blacks are indistinguishable from Hispanics and Asians, who also slightly underperform whites. Adding interviewer fixed effects and controls for the child’s age, gender, socioeconomic status (SES) and prenatal circumstances further compresses the observed racial differences. With these covariates, we cannot reject equality in test scores across any of the racial/ethnic groups examined.
The piece is titled “Testing for Racial Differences in the Mental Ability of Young Children.” Versions of the piece are here, but I believe the final version is not yet in jstor.
Note that to the extent you treat parental IQ as affecting the IQ of the child through environment, these results are consistent with a wide variety of accounts of racial gaps in IQ. Still, there is no serious evidence, from these results, against the claim that the measured racial IQ gap is due to environment and environment alone.
Sentences about household wealth (Cyprus fact of the day)
On average, the wealthiest households are in Luxembourg, but Cyprus, which last month came close to a complete financial meltdown, was second.
That is from the eurozone. And this:
Median net wealth is the lowest in the bloc’s paymaster, Germany (51,400 euros), less than a third of that in Italy (173,500 euros) or Spain (182,700 euros), due to the relatively low level of home ownership in Germany.
As I’ve said many times in the past, much about the future will depend on whether wealth taxation turns out to be politically feasible to a greater degree than at present.
Undesirable trends in the labor force participation rate
From Cardiff Garcia, here is more.