Archive | Political Economy

If You Give a State A Federal Reserve Bank (or two…)

missouriWhat are the chances that both bank presidents—one as hawk, the other as d0ve—will dissent from the Federal Open Market Committee’s policy statement?  Today’s double dissent from the St. Louis and Kansas City Federal Reserve Bank (FRB) presidents highlights some curiosities about the Fed’s governance and FOMC voting rules.  Why are two of the twelve FRB’s located in Missouri? And why do the two bank presidents rotate on and off the voting roster of the FOMC together?   (And why does the Kansas City Fed’s regional conference take place in Jackson Hole?  Never mind.)

So the Missouri curiosity isn’t the most pressing takeaway from today’s Fed statement and press conference.  (I’d give that prize to Ben Bernanke’s communications challenge in conveying the FOMC’s monetary policy choices.)   But the Missouri matter does beg questions about the politics that underlie the structure and governance of the Fed. And to the extent that a diversity of policy views on the FOMC complicates the Fed’s exit from unconventional asset purchases, then the design of the Fed and its monetary policy committee is worth pondering.

So, first, how did Missouri scam two banks from the committee (comprised of three Democratic political appointees) that Congress charged with organizing the Federal Reserve System in 1914?  Mark Spindel and I explore the politics here, showing that patterns of economic development and the preferences of banking communities influenced where the committee located the twelve FRBS in the new Federal Reserve System.  Although some believed at the time that Missouri received two banks because the Democratic Speaker of the House, Champ Clark, hailed from Missouri and because one committee member had served as president of Washington University in St. Louis, we argue that partisan connections at best smoothed the way for selecting two Missouri cities. More likely, the choice reflected the Midwest’s political economy (with Kansas City looking westward and St. Louis to the east) and the desire to curry support of the most active banking communities (which, when surveyed in 1914, favored locating a reserve bank in Kansas City, rather than in any of the regional contenders such as Lincoln, Omaha, Denver).  As one Dallas banker said when he lobbied the committee to give Dallas a reserve bank:

“The matter of locating regional banks is not primarily, nor even principally, a political question. Every governmental faculty, however, has a political element and every governmental agency a political phase. No system of banking will long succeed that does violence to a great fraction of the wishes of the people of this country. Such political considerations as affect this feature of the problem are therefore of an entirely proper character for consideration by this committee.”

Those 1914 choices proved sticky.  Despite a century of economic, demographic, and technological change that has altered the nation’s political economy, Congress has not relocated the FRBs.  In other words, century-old politics made possible today’s conflicting dissents from the two Missouri reserve banks (suggesting the limits of a region’s  economic conditions in shaping central bankers’ votes).

Second, why do Kansas City and St. Louis rotate on and off the FOMC voting roster together?  When Congress revamped the FOMC in 1935, reserve bank directors were empowered to select the five reserve bank presidents who would vote on the FOMC; the 12 FRBs were paired in different groups, with Kansas City and St. Louis placed in different pairings.  Moving in 1942 to increase the Fed’s role in financing the war, Congress rewrote the FOMC voting rules—giving New York a permanent seat, moving the remaining eleven FRBs into four regional groups, and creating a new voting rotation across the groups.  In revamping the voting rules, Congress did nothing to prevent the Kansas City and St. Louis bank presidents from voting at the same time.  Why not?  No clue!  Perhaps at the dawn of an era in which interest rates would be pegged—leaving little discretion for the FOMC—voting rights beyond New York’s mattered little to lawmakers.  Whatever the reason, we’re left with today’s historical curiosity of conflicting signals from Missouri’s central bankers.  In a period of market volatility (in part stemming from confusion over the Fed’s intentions), those conflicting dissents might be more than mere curiosities.

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Is theory getting lost in the “identification revolution”?

The following is a guest post from Columbia University political scientist John Huber, and is a slightly modified version of a commentary that previously appeared in the newsletter of the Political Economy Section of the American Political Science Association.


There is a powerful movement in social science emphasizing the importance of causal identification, of making valid causal inferences in empirical research.  A wide range of methods and approaches are being employed to help us figure out “what causes what” in politics, economics and their intersection, and although much of this research is in its relative infancy, the rapid progress social scientists are making to improve our understanding of how to approach the problem of causal identification should be embraced and celebrated.   At the same time, a laser focus on causal identification can create biases in the way we think about what constitutes a good question, in the claims we make about our work, and ultimately in how deeply we really understand social science phenomena.  It’s therefore useful to reflect a bit on the nature of these biases, and on how they might be shaping the way we go about our research.

The argument behind the “identification revolution” is well-rehearsed:  standard analyses of observational data, such as traditional multivariate regression with covariate adjustment, do not reveal the causal impact of variables because it is typically impossible with such approaches to understand the direction of causation, or to know if “effects” we attribute to some variable of interest are in fact due to some other unobserved variable that we have not measured.  We must therefore employ other approaches that allow random assignment of the causal variables of interest (such as field, laboratory or survey experiments), or at least that employ approaches to observational data that make causal inference possible (such as regression discontinuity models, instrumental variables, difference-in-difference models, or natural experiments).

Although the arguments underpinning the identification revolution are clearly correct from a methodological perspective, it is less obvious what the implications should be for how we proceed in efforts to understand social, economic and political  phenomena, and I worry there may be two unhelpful biases in how the on the identification revolution is influencing research strategies and agendas.  The first bias concerns the menu of questions we study.   Some “identificationists” take the strong position that social science research that cannot solve the identification problem is not worth doing, or at least is not worth publishing in leading journals.  If we move towards this position, we excessively narrow the range of questions we ask, and thus unnecessarily limit our understanding of the social processes we study.  One problem is that many things we care about – democracy, growth, institutions, diversity, inequality, wealth, violence, stability, rights, participation – cannot realistically be randomly assigned, and the extent to which the natural world presents us with causal identification opportunities can be quite limited.   Another problem is that many of these substantively important variables are embedded in dynamics of reciprocal causation with each other that will often frustrate the ambitions of even the most determined and talented “identificationists.”  Thus, good causal identification is not always possible on questions of central importance.

Does this mean we should not study such questions?  Sometimes research agendas reach a point where we won’t make much more useful progress until someone solves the identification problem.  The theories are well-developed, there exist no data limitations on how we describe empirical associations, and the traditional empirical methods have pushed observational data to their limits.  In these situations, further studies that leave unaddressed questions of causality seem a waste of time.  But the number of questions on which we’ve reached this point might be smaller than many imagine, and there is often much to be gained from working on questions for which we cannot see clear solutions to the identification problem.  Indeed, for many important questions, there is little clear theory, and providing one will be helpful in orienting empirical research.  Similarly, demonstrating the presence of previously unknown empirical associations can dramatically shape how we think about social phenomena, even if we can’t nail down causation.  It’s pretty impressive, for example, how often simple bivariate scatter plots make a lasting impact on how we think about the world around us.   Add the two together – theory and empirical association – and something very useful results, including making it possible to offer much better advice about what specific type of “identification study” is likely to yield the most useful insights.

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“Rich States, Poor States, 6th Edition”

Arthur Laffer, Stephen Moore and Jonathan Williams write:

All across the nation, states are looking for ways to boost their economies and become more economically competitive. Each state confronts this task with a set of policy decisions unique to their own situation. . . .

Fortunately, the United States, with its “50 laboratories of democracy,” provides us with empirical evidence to track exactly [sic] which policies lead to economic prosperity and which fail to deliver. . . .

Armed with years of economic data and empirical evidence from each state, the authors identify which policies can truly [sic] lead a state to economic prosperity. Rich States, Poor States not only identifies these policies but also makes sound research-based conclusions about which states are poised to achieve greater economic prosperity and those that are stuck on the path to a lackluster economy.

I’m supportive of this sort of effort but I don’t think the authors help their credibility by stating their conclusions with such certainty. Can’t the American Legislative Exchange Council hire an economist or political scientist or statistician who can tell them about causal identification?

The report is pretty long and I did not read the whole thing. But it looks like what they’re doing is giving each state two rankings:
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Sympathy for the Kinsley

Paul Krugman, Daniel Drezner, and others slam fabled contrarian Michael Kinsley for his argument that we need to cut the budget deficit now because “we have to pay a price for past sins, and the longer we put it off, the higher the price will be” and that this attitude follows “the lessons of Paul Volcker and the Great Stagflation of the late 1970s.”

You know you have a problem with Drezner when, instead of calling your work “piss-poor monocausal social science,” he doesn’t even call it “social science” at all.

I have some sympathy for Kinsley, though, not on the merits—-I have no idea on the merits, for all I know he’s exactly correct on the economics—-but on his reactions to this event.

I think I’m well qualified to write about this because I know about as much of macroeconomics as Kinsley does (or so I suspect).
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How Politics Can Make People Cooperate

Much of politics is about collective action, whereby groups of people need to cooperate in order to produce an outcome.  One of the biggest challenges is getting people to cooperate in providing a public good, which by its nature can be shared by everyone regardless of whether they’ve cooperated in the first place.

One way to enforce cooperation is via some central authority that’s external to the group (like a government).  Another way, prominent in Elinor Ostrom’s work, is via internal policing by peers within the group.

In this NSF-funded study by Guy Grossman and Delia Baldassarri show that a third way can work as well: developing a leadership or authority structure within the group itself.  More importantly, they show that the success of such an authority depends on politics itself.  Leaders need to be elected to induce cooperation.

The study was conducted among Ugandans who are members of farmer organizations and experience directly the challenges of cooperating to produce public goods.  Grossman and Baldassarri not only examined how these people behaved when asked to play a simple “public goods game” in a quasi-laboratory setting, but how they actually behaved within their farmer organization in real life.  In both contexts, members cooperated significantly more when leaders were democratically elected—as was true in one experimental condition of the public goods game—or when they perceived the leadership of their farmer organization as more legitimate.   Grossman and Baldassarri summarize one implication of this finding:

We began by demonstrating experimentally something quite intuitive—that elections increase the value of a local public good. But as we began ruling out options commonly associated with why elections are deemed beneficial, we were left with an important finding. Elections increased the value of local public goods even after we eliminate incumbents’ reelection considerations, and even when we minimize the information voters have on potential candidates, reducing their ability to select more able and more responsive leaders. We found evidence suggesting that something fundamental causes us to be more prosocial when we participate in key political process such as elections. That elections affect not only the behavior of incumbents but also the behavior of constituents who had participated in the electoral process is among the key findings of our study.

It is easy to see why such a study is valuable even by the criteria proposed by Senator Coburn.  Engendering cooperative and pro-social behavior is intrinsic not only to economic productivity—as was true in these farmer organizations—but also to ensuring security and peace.  Granted, this is but one study in one setting, but this research agenda remains fundamental.  Indeed, this agenda is the reason for Ostrom’s Nobel Prize.

[For more in this week’s presentation of NSF-funded research recently published in the American Journal of Political Science, see here, here, and here.]

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White Vote by Income, 2012

Jeremy Johnson asks: “Do we have information yet about voting among whites based on income from the 2012 election?  Did lower-income whites in every state again vote more Democratic than upper-income whites?”


I don’t know about every state; but here is the picture for the Non-South and South, based on survey data from the 2012 Cooperative Campaign Analysis Project. The lowest income category is less than $10,000; the highest is $150,000 and up. The overall correlation between income and Democratic support is negative, but Obama’s voter share is higher in the top income groups than among the upper middle class. (That very low Obama share for the lowest income group in the South is based on 196 survey respondents; the other cell sizes range from 285 to 1420.)

When I was a kid, my parents bought the Encyclopedia Britannica. (For you youngsters in the audience, that was a big shelf of books containing all the good stuff that’s now on the internet.) One of the great features was that it came with a bunch of coupons you could mail in to get customized reports on any topic. (Looking back, it seems possible that we were the only subscribers in the world who were not submitting these as schoolwork.) Alas, The Monkey Cage can’t redeem every reader’s coupons—but today we’re here for you, Jeremy.

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Updated survey of small business attitudes

Sander Daniels sends along this info on a survey of small business attitudes in the U.S. A discussion of their methods is here. As I wrote when linking to their survey last year, I don’t know what to make of all of this—who knows what to make of their sample or the responses to these questions?—but I’m impressed that they seem to describe exactly what they did.

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A graph at war with its caption. Also, how to visualize the same numbers without giving the display a misleading causal feel?

Kaiser Fung discusses the following graph that is captioned, “A study of 54 nations—ranked below—found that those with more progressive tax rates had happier citizens, on average.”


As Kaiser writes, “from a purely graphical perspective, the chart is well executed . . . they have 54 points, and the chart still doesn’t look too crammed . . .” But he also points out that the graph’s implicit claims (that tax rates can explain happiness or cause more happiness) are not supported.

Kaiser and I are not being picky-picky-picky here. Taken literally, the graph title says nothing about causation, but I think the phrasing implies it. Also, from a purely descriptive perspective, the graph is somewhat at war with its caption. The caption announces a relationship, but in the graph, the x and y variables have only a very weak correlation. The caption says that happiness and progressive tax rates go together, but the graph uses the U.S. as a baseline, and when you move from the U.S. point on the graph to the right-hand side (more progressive taxes), you see a lot more points below the line than above the line. Thus the visual impression of the graph is that more progressive taxes will lead to lower happiness—-the opposite of the message from the caption.

What can be done here?

I don’t exactly think the graph is “bad data,” and, although the graph says little directly about causation, the data have some relevance to our understanding of policy debates over taxes. If nothing else, we learn that tax progressivity and average happiness some variation among countries. I think a start would be to reframe and put happiness on the x-axis and the tax system on the y-axis, which would allow us to see that, at any happiness level, there is a range of tax systems. with none of the very happiest countries having flat taxes.

Better still might be to make a line plot with three columns: First, a list of country names, in decreasing order from richest to poorest (using, for example, per-capita GDP (yes, I know, such data aren’t perfect!)), then a column showing tax progressivity (if that’s the measure they want to use), then a column showing average happiness.

The advantage of this pair of dotplots is that you get to see the spread in each of these variables with respect to a natural measure (how rich the country is), and there’s no implicit causal story getting in the way.

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