Archive for the ‘economics’ Category
Time’s website has an article that proposes a radical liberalization of immigration:
However, there is another, and much more effective way to increase technological capabilities in low-income countries. Instead of focusing on innovating more technology to make people more productive, we could focus on getting more people to places where they would be productive.
While allowing the free mobility of goods (free trade) can add several percentage points to global GDP, we have long known that free mobility of people could add anywhere from 67-147% to global GDP. Allowing free mobility could essentially double the world’s income.
This is because people in poor areas are not inherently unproductive but their circumstances mostly make them unproductive. So, if they were to migrate, from say, Guatemala to the US, they would become much more productive.
In other words, let people move to places where they can actually be productive. My one criticism is that the authors focus on skilled workers, but there is little reason to think that allowing low-skilled migration wouldn’t be a benefit. Still, I applaud Time for allowing this idea to be debated.\
Yale is hosting a conference on $$$, which is open to the public, next Fri., Sept. 12th at Yale.
The line-up is both impressive and exciting, not least of all because it involves our orgtheory crew plus beloved colleagues and dear orgtheory readers!
Friday, September 12, 2014
Nina Bandelj ~ Sociology, University of California at Irvine
Daniel Markovits ~ Yale Law School
Frederick F. Wherry ~ Sociology, Yale University
With papers from:
Bruce Carruthers ~ Sociology, Northwestern University
Christine Desan ~ Harvard Law School
Nigel Dodd ~ Sociology, London School of Economics
Akinobu Kuroda ~ Institute for Advanced Studies on Asia, Tokyo
Simone Polillo ~ Sociology, University of Virginia
Akos Rona-Tas ~ Sociology, University of California at San Diego
Alya Guseva ~ Sociology, Boston University
Rene Almeling ~ Sociology, Yale University
David Grewal ~ Yale Law School
Kieran Healy ~ Sociology, Duke University
Marion Fourcade ~ Sociology, University of California at Berkeley
Supriya Singh ~ Sociology, RMIT, Australia
Stephen Vaisey ~ Sociology, Duke University
Shane Frederick ~ Psychology, Yale School of Management
Daniel Markovits ~ Yale Law School
The Social Meaning of Money
Nancy Folbre ~ Economics, University of Massachusetts
Arlie Hochschild ~ Sociology, University of California at Berkeley
Eric Helleiner ~ Political Science, University of Waterloo
Bill Maurer ~ Anthropology, University of California at Irvine
Jonathan Morduch ~ Economics, New York University
Co-Sponsored by The Office of the Provost, Yale University ~ Yale Center for Cultural Sociology
Center for Organizational Research at the University of California, Irvine
Yale Center for Comparative Research ~ Yale Law School ~ Yale School of Management
Here’s the program:
Money Talks: A Symposium at Yale
Friday, September 12, 2014
Morning Sessions:Yale School of Management, Evans Hall, 165 Whitney Avenue. Class of 1980 Classroom, 2400
Afternoon sessions: Yale Law School, 127 Wall Street, Room 127 (TBC).
9:00 ~ 9:15 AM Welcome
Richard Breen ~ Yale University, Chair of the Department of Sociology
Daniel Markovits ~ Yale Law School, Symposium Co-host
Frederick Wherry ~ Yale University, Symposium Co-organizer
Nina Bandelj ~ University of California, Irvine, Symposium Co-organizer
9:15 ~ 10:45 AM Panel 1: Money and Markets
Bruce Carruthers ~ Northwestern University
Some A-B-C’s of Financial Fables: Rethinking Finance and Money
Akinobu Kuroda ~ Institute for Advanced Studies on Asia, University of Tokyo
The Characters of Money: A Historical Viewpoint from Complementary Currencies
Simone Polillo ~ University of Virginia
A Macro-Sociology of Money
Alya Guseva ~ Boston University & Akos Rona-Tas ~ University of California, San Diego
Money Talks, Plastic Money Tattles
Moderator: Alice Goffman ~ University of Wisconsin, Madison
10:45 ~ 11:00 AM Coffee Break 11:00 AM ~ 12:30 PM Panel 2: Money and Morals
Rene Almeling ~ Yale University
Money, Technology, and Bodily Experience: Comparing the Production of Eggs for Pregnancy or for Profit
David Grewal ~ Yale Law School
The Meaning of the Mirage: Money and Sin in Early Political Economy
Marion Fourcade ~ University of California, Berkeley & Kieran Healy ~ Duke University
Seeing Like a Market
Supriya Singh ~ RMIT University, Australia
Money and Morals: The Biography of Transnational Money
Moderator: Olav Sorenson ~ Yale School of Management
12:30 ~ 2:00 PM Lunch Break 2:00 ~ 4:00 PM Panel 3: The Social Meaning of Money, 20 Years Later
Nancy Folbre ~ University of Massachusetts, Amherst
Accounting for Care
Arlie Hochschild ~ University of California, Berkeley
Going on Attachment Alert: Paying Money, Managing Feeling
Eric Helleiner ~ University of Waterloo, Canada
The Macro Social Meaning of Money: From Territorial Currencies to Global Money
Bill Maurer ~ University of California, Irvine
Zelizer for the Bitcoin Moment: The Social Meaning of Payment Technology
Jonathan Morduch ~ New York University
Economics, Psychology, and the Social Meaning of Money
Moderator: Nina Bandelj ~ University of California, Irvine
4:00 ~ 4:15 PM Coffee Break 4:15 ~ 6:00 PM Panel 4: The Moralities, Solidarities, and Meanings of Money
Stephen Vaisey ~ Duke University
What Would You Do For a Million Dollars?
Shane Frederick ~ Yale School of Management
Christine Desan ~ Harvard Law School
Money as a Constitutional Practice
Daniel Markovits ~ Yale Law School
Economic Inequality and the Meaning of Money
Nigel Dodd ~ London School of Economics
Is Bitcoin Utopian?
Moderator: Frederick Wherry ~ Yale University
6:00 PM A Conversation With Viviana Zelizer
Moderators: Nina Bandelj ~ University of California, Irvine & Frederick Wherry ~ Yale University
6:30 PM Reception ~ Yale Law School, The Alumni Reading Room
Seth probably just felt some nausea. For an economist, being called a good sociologist is like being called a Yankees fan at Fenway. But still, I have very much enjoyed Stephens-Davidowitz’ work and I think it can be emulated by more sociologists. Roughly speaking, Stephens-Davidowitz uses “big data” to track and measure otherwise illegitimate or stigmatized behavior, and link it to measurable outcomes. So far, his work is reported in NY Times op-eds (!), but you should definitely check it out if you haven’t done so already:
- He uses online searches for sexual materials to estimate the proportion of gay men in the US.
- He used racial slur searches (the “n-word”) to show where Obama under performed in the presidential vote.
- He’s been scraping white nationalist websites to develop profiles of racial hate groups.
I call this sociology because … it is! It isn’t economics in the sense of being about rational choice theory, except in the most bland sense of the word. It clearly is not about commerce or trade. Rather, it is about measuring specific racial and sexual identities in novel ways and seeing if it links with behavior. Bravo!
In his recent post on white nationalists, he says that he can’t go further on the question of motivation. I say he should embrace his inner sociologist and start reading social psychology and critical race theory, which has lots of testable hypotheses about when people feel heightened racial anxiety. Let the sociologist come out and I’ll welcome you with open arms!
A common, and important, critique of journals is that they don’t want to publish null results. So when I saw a new piece in Socio-Economic Review yesterday reporting essentially null findings, I thought it was worth a shout-out. The article, by economist Stefan Thewissen, is titled, “Is It the Income Distribution or Redistribution That Affects Growth?” (paywalled; email me for a copy). Here’s the abstract:
This study addresses the central question in political economy how the objectives of attaining economic growth and restricting income inequality are related. Thus far few studies explicitly distinguish between effects of income inequality as such and effects of redistributing public interventions to equalize incomes on economic growth. In fact, most studies rely on data that do not make this distinction properly and in which top-coding is applied so that enrichment at the top end of the distribution is not adequately captured. This study aims to contribute using a pooled time-series cross-section design covering 29 countries, using OECD, LIS, and World Top Income data. No robust association between inequality and growth or redistribution and growth is found. Yet there are signs for a positive association between top incomes and growth, although the coefficient is small and a causal interpretation does not seem to be warranted.
Okay, so there’s the “signs for a positive association” caveat. But “the coefficient is small and a causal interpretation does not seem to be warranted” seems pretty close to null to me.
In light of the attention this report from S&P has been getting — e.g. from Krugman today (h/t Dan H.) — all solid findings, null and otherwise, on the inequality-growth relationship warrant publication. Hats off to SER for publishing Thewissen’s.
A recent article in the Journal of Economic Perspectives reports a recent attempt to curb grade inflation. High GPA departments at Wellesley College were required to cap high grades. The abstract:
Average grades in colleges and universities have risen markedly since the 1960s. Critics express concern that grade inflation erodes incentives for students to learn; gives students, employers, and graduate schools poor information on absolute and relative abilities; and reflects the quid pro quo of grades for better student evaluations of professors. This paper evaluates an anti-grade-inflation policy that capped most course averages at a B+. The cap was biding for high-grading departments (in the humanities and social sciences) and was not binding for low-grading departments (in economics and sciences), facilitating a difference-in-differences analysis. Professors complied with the policy by reducing compression at the top of the grade distribution. It had little effect on receipt of top honors, but affected receipt of magna cum laude. In departments affected by the cap, the policy expanded racial gaps in grades, reduced enrollments and majors, and lowered student ratings of professors.
My sense is that this shows that grade inflation, whatever its historical origins, acts as a competitive advantage for programs that few other market advantages. If you don’t have a strong external job market or external funding, then you can boost enrollments via grade inflation. It also absolves programs by masking racial under performance. The lesson for academic management is this: If you have inequality in funding, departments will compensate by weak grading. If you have inequality by race, departments will compensate by weak grading. Thus, academic leaders who care about either of these issues should implement policies where departments don’t choose standards and are accountable for results.
A few years ago, I bought a copy of Charles Tilly’s Why?, just for fun sociology reading. All the Important sociology reading got in the way, and I never read Why?
But while I was unpacking this week I came across it and thought I’d bring it along on a car ride to Providence over the weekend. Not only is it a fun read, as well as touchingly personal at times, it turned out to be surprisingly relevant to stuff I’ve been thinking about lately.
The book is organized around four types of reasons people give for things…any things: their incarceration in mental hospitals, why a plane just flew into the World Trade Center, whether the last-minute change of an elderly heiress’s will should be honored. In grand social science tradition, the reasons are organized into a 2 x 2 table:
|Cause-Effect Accounts||Stories||Technical Accounts|
Why? illustrates these types with a wide range of engaging examples, from eyewitness accounts of September 11th to the dialog between attending physicians and interns during hospital rounds.
Conventions are demonstrated by etiquette books: they are reasons that don’t mean much of anything and aren’t necessarily true, but that follow a convenient social formula: “I lost track of the time.” Stories are reasons that provide an explanation, but one focused on a protagonist—human or otherwise—who acts, and which often contain a moral edge: evangelist Jerry Falwell’s account of how he came to oppose segregation after God spoke to him through the African-American man who shined his shoes every week. Both conventions and stories are homely, everyday kinds of reasons.
Codes and technical accounts, on the other hand, are the reasons experts give. Reasons that conform to codes explain how an action was in accordance with some set of specialized rules. The Department of Public Works did not repair the air conditioning because they lacked a form 27B/6. While law is the quintessential code, Tilly shows that medicine follows codes to a surprising extent as well.
Finally, technical accounts attempt to provide cause-effect explanations of why some outcome occurs. Jared Diamond argues that Europe developed first because it had domesticable plants and animals and sufficient arable land, and lacked Africa’s north-south axis. Technical accounts draw on specialized bodies of knowledge, and attempt to produce truth, not just conformity with rules.
I’ve spent a lot of time in recent months thinking about what experts do in policy, and thinking about the different paths through which they can have effects. Lots of these effects are technical, of course. Expert opinion may not determine the outcome in debates over the macroeconomic effects of tax policy changes or what standards nutrition guidelines should be set at, but there’s no question that they’re informed by technical accounts.
But at least as important in influencing a wider audience are the stories experts can tell. Deborah Stone wrote about these “policy stories” decades ago, though she wasn’t especially focused on experts’ role in creating them. Political scientists like Ann Keller, however, have shown that scientists, too, translate their expertise into policy stories—for example, that human activity was creating the sulfur and nitrogen oxides that produce acid rain, destroying fisheries and making water undrinkable. These stories are grounded in technical accounts, but are simplified versions with moral undertones that point toward a particular range of policy solutions—in this case, doing something about the SOx and NOx emissions that the story identifies as creating the problem.
Some kinds of expertise, or rather some kinds of technical accounts, are more amenable than others to translation into policy stories. Economic models, in particular, are often friendly to such translation. For example, although this isn’t the language I use there, my book in part argues that U.S. science policy changed because of a model-turned-story. Robert Solow’s growth model, which includes technology as a factor that affects economic growth (by increasing the productivity of labor), became by the late 1970s the basis of a powerful policy story in which the U.S. needed to improve its capacity for technological innovation so that it could restore its economic position in the world.
Similarly, a basic human capital model in which investment in training results in higher wages easily becomes a story in which we need to improve or extend education so that people’s income increases.
Sociological models, even the formal ones, seem less amenable on average to these kinds of translations. Though Blau and Duncan’s well-known status attainment model could be read as suggesting education as a point of intervention to improve occupational status, it seems fairer to read it as saying that occupational status is largely determined by your father’s occupation and education. While this certainly has policy implications, they are not as natural an extension from the model itself. It hearkens back to that old saw—economics is about how people make choices; sociology is about how they don’t have any choices to make.
I guess part of the appeal of Why? for me was that it mapped surprisingly well onto these questions that were already on my mind. Mostly I’ve thought about this in the context of economic models becoming policy stories. I wonder, though, whether my quick generalization about the technical accounts of sociology lending themselves less readily to compelling policy stories actually holds up. What are the obvious examples I’m missing?