Tag Archives: economics

“Does International Child Sponsorship Work? A Six-Country Study of Impacts on Adult Life Outcomes”

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New paper at the Journal of Political Economy:

Child sponsorship is a leading form of direct aid from wealthy country households to children in developing countries. Over 9 million children are supported through international sponsorship organizations. Using data from six countries, we estimate impacts on several outcomes from sponsorship through Compassion International, a leading child sponsorship organization. To identify program effects, we utilize an age-eligibility rule implemented when programs began in new villages. We find large, statistically significant impacts on years of schooling; primary, secondary, and tertiary school completion; and the probability and quality of employment. Early evidence suggests that these impacts are due, in part, to increases in children’s aspirations.

Appear to be a few ungated copies floating around the web. Science Daily claims that this is the first study to show that such programs actually work:

Despite the billions of dollars that flow to child sponsorship each year and the millions of American families who sponsor overseas children, this is the first published study to investigate whether such programs actually benefit the children they intend to help. Evidence from the study points to the positive effects of child sponsorship on the adult life outcomes of these children.

Do small businesses (really) create most jobs?

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Something I’ve wondered about for a while, given that it’s a staple of political rhetoric. Now a forthcoming paper in the Review of Economics and Statistics looks at this issue systematically:

The view that small businesses create the most jobs remains appealing to policymakers and small business advocates. Using data from the Census Bureau Business Dynamics Statistics and Longitudinal Business Database, we explore the many issues at the core of this ongoing debate. We find that the relationship between firm size and employment growth is sensitive to these issues. However, our main finding is that once we control for firm age, there is no systematic relationship between firm size and growth. Our findings highlight the important role of business start-ups and young businesses in U.S. job creation.

The authors story is apparently a straightforward spurious relationship: “Importantly, because new firms tend to be small, the finding of a systematic inverse relationship between firm size and net growth rates in prior analyses is entirely attributable to most new firms being classified in small size classes” (first page of the unnumbered PDF at the link).

Does that invalidate the “descriptive” claim that small businesses create more jobs? For a similar issue(?), see here.

The study is apparently gated, but NBER did a press release about the study a few years ago, suggesting an ungated copy may be out there.

But apparently there’s been lots of popular commentary about whether this claim is true as well.

A third simple alternative explanation

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In spring 2012 I was lucky to serve as teaching assistant for an undergraduate research methods course in the MIT political science department. Like many such courses, we give a broad overview of linear regression, internal and external validity, and the difference between observational and experimental studies. Whenever you read an observational study, we taught students, you should always be thinking about two possible alternative explanations for a statistical relationship: an omitted third variable, or reverse causality.

I’m realizing that we probably left out an important third explanation: the relationship is an artifact, either a technical error or, perhaps, willful manipulation of the data. (Well, we did always tell students to always look at their own data, but we probably could have emphasized this more in how they evaluate research by others.) Recent events make me think that technical errors are probably more common than I previously thought.

The particular event I have in mind is the Reinhart-Rogoff fiasco. The (perhaps overblown — the 2010s are still young after all) money quote from one observer of the situation:

one of the core empirical points providing the intellectual foundation for the global move to austerity in the early 2010s was based on someone accidentally not updating a row formula in Excel.

But this follow-up on Andrew Gelman’s blog, which details all the seemingly common and easy-to-make mistakes that occur even using more sophisticated statistical software. I have to say I think point #5 at that post is the best, and again is actually just another version of the advice we gave to our undergraduates: always show your data!

Anyway, see this post by another UMass Amherst economist for what seems like a terrific explanation of why reverse causality is actually the more serious problem for Rogoff-Reinhart.

Randomly assigning health insurance in Oregon

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Count me among those who didn’t know realize there was a debate about this, but apparently people have been arguing about whether Medicaid does any good for people. As described in a recent New York Times article, the state of Oregon held a lottery where they randomly assigned Medicaid coverage to 10,000 out of the 90,000 who applied. See also this post at the Monkey Cage.

I like this description of the design from the New York Times:

By assigning coverage randomly, Oregon gave researchers more confidence that they had teased out the true effects of insurance, and had not been fooled by other differences between the insured and the uninsured.

The Times apparently decided the study was not good enough to stand on its own, and decided to interview “17 insured and uninsured participants.” At least we know the treatment was random there, though!

Value-added assessments

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I posted about value-added assessments when the front page story in the New York Times came out early this year. In recent weeks I’ve come across a couple interesting commentaries on these scores.

At the Washington Post, Jay Mathews wrote a column titled “Devaluing value-added assessments.” I read it closely, but couldn’t understand what Mathews is saying is wrong with these scores. He begins by saying he will relate “the best argument against value-added I have seen in some time.”

Point #1:

“I have seen this sham firsthand over many years,” Wiggins writes. “Lots of so-called good N.J. and N.Y. suburban districts are truly awful when you look firsthand (as I have for three decades) at the pedagogy, assignments and local assessments; but those kids outscore the kids from Trenton and New York City, even though both city systems have a number of outstanding schools and teachers.”

I don’t get this–don’t value added scores only measure changes within a single district? Aren’t we only using them to assess teachers within districts?

Point #2:

Also, Wiggins wrote, valid research on value-added exposes “hidden truths,” such as “it IS true that models accurately predict over a three-year period, performance at the extremes. Thus, the really effective teachers stay so and the really ineffective ones are really ineffective.”

I don’t understand this at all. What is the hidden truth here exactly? That teachers matter?

Point #3:

Schools with high test scores discover through value-added analysis that they need more than that. One outstanding prep school, Wiggins said, gave a professionally designed test of critical thinking to freshmen and seniors. There was no improvement. Similar results have come from colleges giving the Collegiate Learning Assessment of analytical skills, given to freshmen and seniors.

Huh? It sounds like Mathews is saying here that value added scores help schools identify bigger problems. Isn’t that a good thing?

Point #4:

Our mistake was thinking this valuable long-term research tool would work as a one-year teacher rating system. “It becomes like a sick game of telephone: What starts out as a reasonable idea, when whispered down the line to people who don’t really get the details — or don’t want to get them — becomes an abomination,” Wiggins wrote. “By looking at individual teachers, over only one year (instead of the minimum three years as the psychometricians and VAM [valued-added model] designers stress), we now demand more from the tests than can be obtained with sufficient precision.”

I’m not sure what to make of this. It sounds like the critique is that the VA measure only uses change over one year. I suppose that would be problematic if true, but I’m not sure it is true. Even if it is, the paper by Chetty et al. (subject of the NYT article linked above) offers evidence that VA measures are an unbiased measure of quality.

A second commentary comes from Andrew Gelman’s blog. This is more of a technical discussion about whether VA measures make the right modeling assumptions.

Do gas prices matter for election outcomes?

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Pretty good article in the New York Times today takes up this question, with quotes from political scientsts Alan Abramowitz, James Gimpel, Andrew Reeves, John Sides, and an unnamed “political scientist [who] estimated that the impact of changes in unemployment was 27 times greater than the impact of equivalent changes in gas prices.” The basic message: gas prices aren’t important!

Does food aid increase conflict?

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Via the Freakonomics blog, this working paper by Nathan Nunn and Nancy Qian tackles a big question with an interesting design. Abstract:

This paper examines the effect of U.S. food aid on conflict in recipient countries. To establish a causal relationship, we exploit time variation in food aid caused by fluctuations in U.S. wheat production together with cross-sectional variation in a country’s tendency to receive any food aid from the United States. Our estimates show that an increase in U.S. food aid increases the incidence, onset and duration of civil conflicts in recipient countries. Our results suggest that the effects are larger for smaller scale civil conflicts. No effect is found on interstate warfare.

Political obstruction of the economy?

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The New York Times profiles Yale economist Ray C. Fair, who uses economic conditions to predict presidential election outcomes.

I’ve never heard of Fair before, but I have heard of many political scientists who do the same thing, so it is odd that the Times piece only quotes Fair. But what I found most interesting was actually some speculation by the author at the end of the piece:

Using Professor Fair’s model, I plugged in several forecasts. The consensus of the most recent Livingston Survey of the Federal Reserve Bank of Philadelphia calls for 2.1 percent annualized growth in the first half of the year. Using that figure for three quarters, the Fair model projects the president trailing slightly in the popular vote. Then I plugged in a figure of zero economic growth through the election. The president’s total was lower but the election was still too close to call. Plug in a G.D.P. decline of 2 percent — a recession — and the model shows the president losing.

These calculations suggest the quandary faced by the opposition party. New measures that stimulate the economy could decide a close election. But if the Republicans are obviously obstructionist, they could take some blame for a weak economy. The equations may not capture this kind of political calculus.

I could pick on Fair and say that as an economist he can’t delve into these deeper questions, but the truth is neither have political scientists, to my knowledge. There is some work on how incumbent parties allegedly manipulate the economy to their advantage; Edward Tufte wrote the book on this topic and Larry Bartels provides a recent update of sorts. But I don’t know of any research on whether the opposition party, knowing that the incumbent’s fortunes hinge on economic performance, actively try to sabotage the economy in an election year.

Two posts on the Monkey Cage during the debt ceiling imbroglio discussed this, one by John Sides and one by Joshua Tucker.

Tucker’s post lays out the basic logic most clearly:

Let’s posit the following three assumptions:

Incumbent US presidents perform worse when economic conditions are worse.
If the US defaults on its debt, there could be catastrophic consequences for the US economy.
The number one goal of the Republican party is to defeat Obama in 2012.

Ergo, the Republican party ought to do everything possible to ensure the US defaults on its debt.

See the comments to those two posts for some discussion.

What can we learn from heritability studies?

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The Journal of Economic Perspectives has released a special issue devoted to “neuroeconomics.” Northwestern University economist Charles Manski contributes a short piece in which he argues “research on heritability is fundamentally uninformative for policy analysis.” Manski notes that most heritability research boils down to “analysis of variance” and is (therefore?) useless for thinking about policy making.

At one extreme, suppose that the population is composed entirely of clones who face diverse environments. Then the variance of g is zero, implying that heritability is zero. At the other extreme, suppose that the population is composed of genetically diverse persons who share the same environment. Then the variance of e is zero, implying that heritability is one.

What does this have to do with policy analysis? Nothing. Policy analysis asks what would happen to outcomes if a conjectured intervention were to change persons’ environments in some manner. Heritability is uninformative about this.