Raghuram Rajan is Not Paul Volcker

Raghuram Rajan will take over leadership of the Reserve Bank of India (RBI) on September 4. The BBC lists some of the challenges facing the Indian economy, including a large current account deficit, weak rupee, the slowest growth in a decade (around 5%), and inequality, adding, "Many believe that the single biggest failure of the government's economic policies in recent years has been the inability to control inflation in general and food prices in particular."

It's clear that Rajan will have his work cut out for him, but what kind of work will that be? 

"The monetary situation is such that he may be forced to act as India’s Paul Volcker, hiking up rates and perhaps even orchestrating a recession to get the currency and inflation under control," writes Dylan Matthews. Matthews notes that "By law, India’s central bank doesn’t have much political independence, as Rajan serves at the pleasure of the government and can be sacked at any time. That could deter him from making tough moves against inflation that could have unpopular implications for growth. But Subramanian thinks he’ll have a great deal of flexibility in practice, even if the opposition Bharatiya Janata Party comes to power again."

There is a tendency to want to frame the challenges of the Indian economy in terms of a power struggle between monetary and fiscal authorities-- an "unseemly battle of wits over interest rates," according to Rajrishi Singhal at Bloomberg.  Singhal describes how the Finance Ministry has piled pressure on Rajan's predecessor, RBI Governor Duvvuri Subbarao, to keep rates low. The idea is that, if only the new Governor can stand up to "bullying" and raise rates, then inflation and the rupee can be stabilized. But India in 2013 is not the U.S. in 1979, and Raghuram Rajan need not imitate Paul Volcker.

A central banker's role in India is much different than a central banker's role in the United States or Europe. Monetary policy, remember, is ultimately based on frictions. Prices and inflation are nominal variables. In a frictionless economy, there is no role for monetary policy. It is because of certain frictions that monetary policy can have short-run effects, and these frictions provide the justification for using monetary policy to stabilize economic fluctuations over the business cycle. The optimal monetary policy depends on the nature and magnitude of these frictions. Sticky prices and sticky information are the two categories of frictions most used in the analysis of monetary policy. Both have similar implications for how monetary policy should generally work.

The theory behind the Taylor rule is based on the sticky price friction. The rule recommends a relatively high interest rate or “tight” policy when inflation or employment is relatively high, and a relatively low interest rate in the opposite scenario. According to the Taylor rule, India's policy rates are currently too low. As Ashok Rao writes (in a very excellent post), "A healthy Taylor rule requires an accurate estimate of the output gap which is founded on long-run trend growth. “Trend” growth is a useless concept in countries like India and China, whose growth rates have both a high mean and variance."

But there may be a more fundamental reason why the Taylor rule is not suitable for India. The Taylor rule is based on the sticky price friction, which may not be the dominant friction. Amartya Lahiri suggests that the dominant friction is asset market segmentation (emphasis added).

A well-known feature of the Indian economy is that access to asset markets and instruments is extremely limited. About 140 million households in India do not have access to any formal banking at all. Consequently, less than half of all individuals have access to any formal financial services...It is thus abundantly clear that there is endemic and widespread segmentation in asset markets in India with only a small subset of India having access to formal asset markets. But curiously, discussions about monetary policy in India are completely divorced from this asset market segmentation."

How does asset market segmentation impact the monetary policy calculus? In this case the central bank needs to use monetary policy to provide insurance to those that are absent from these markets. This policy imperative can naturally imply very different monetary policy responses relative to when prices are sticky.

Rajesh Singh, Amartya Lahiri, and Carlos Vegh study optimal monetary policy in environments with segmented asset markets. As Lahiri summarizes,

Intuitively, the role of policy under this friction is to protect households that are excluded from asset markets from excessive fluctuations in their consumption levels. When output is high, consumption of these households tends to rise due to (a) higher income; and (b) higher real money balances as the exchange rate tends to appreciate. By expanding money supply, the central bank can inflate away some of the increase in real balances and thereby moderate the rise in consumption. This procyclicality of the optimal monetary policy is clearly at odds with the Taylor rule prescription that monetary policy should be countercyclical.

The authors also find that asset market segmentation has surprising effects on optimal exchange rate regimes (effectively the opposite of the Mudellian model). They come down in favor of targeting monetary aggregates instead of targeting the exchange rate. The model of Singh et al. is admittedly very stylized and I have found no existing tests of its empirical implications. So I am certainly not actually recommending that Rajan rush to lower interest rates. Nor am I suggesting that Rao's proposal of a rule-based exchange rate policy should be off the table.

Rather, I just intend to highlight the topsy-turvy theoretical results that can arise when we alter the foundations of our models; in particular, when we acknowledge lack of financial inclusion as a significant friction. I also believe that an important role for Rajan, perhaps more important than any decision about interest rates, will be in reforming the financial system and promoting financial inclusion. I am very optimistic on this front. The Financial Times reports that "economists who know Mr Rajan well say helping hundreds of millions of Indians get access to efficient financial services is close to his heart." In 2008, he wrote "A Hundred Small Steps," a report on financial sector reforms. I am most impressed by his inclusion in Chapter 3, "Broadening Access to Finance," of this chart, which shows the interest rates actually paid by people in each income quartile.

It appears that he is quite sensitive to the severity of asset market segmentation in India and to the fact that interest rate movements are not evenly transmitted across all segments of the population.

This Time is Not So Different: The Euro Crisis and the 1840s

In the United States, the 1840s were "an era of fiscal crisis following a decade of fiscal exuberance," according to a paper by Arthur Grinath, JohnWallis, and Richard Sylla. This paper was written in 1997, but its insights into a sovereign debt crisis of long ago provide interesting parallels to today.

In the 1820s and 1830s, state governments made large investments in canals, railroads, and banks. New York and Ohio were the first two states to start canal projects. At first, the expected revenue from the projects was low or uncertain, so New York and Ohio raised taxes to service the canal debt. But the Erie and Ohio canals were highly successful, so subsequent canal construction was financed without corresponding tax increases. New York, Ohio, and other states expected internal improvement projects to produce future revenue, so they didn't feel the need to raise taxes when they began new projects. States were easily able to issue bonds to domestic and foreign (especially British) investors to finance their projects:

“Both state borrowers and lenders, foreign and domestic, anticipated that states could tax land if their bank and transportation projects failed. After 1836, increasing land values and taxable acreage were the common factor underlying state fiscal policies, bank investments, and transportation improvements nationwide. Northeastern states knew they had large amounts of untaxed land, rising in value. It was a fiscal reserve against which they could borrow to finance extensions of their transportation systems. Western states, north and south, were in the midst of the greatest land boom in American history. If northwestern states were uncertain about just when transportation investments would generate revenues, they nonetheless anticipated that many more, and more valuable, acres could soon be taxed. States were thus confident that property tax proceeds would provide adequate fiscal resources to service the debts they incurred. Investors in state bonds concurred.”

State government bonds were considered safe assets because it seemed inconceivable that a state government could default-- their investments were expected to be profitable, and even if they weren't, the states had plenty of potential to increase their tax revenue, especially since land value was rising. Grinath et al. quote Illinois Governor Ford as saying "Mere possibilities appeared to be highly probable, and probabilities wore the livery of certainty itself.”

Gary Gorton, Stefan Lewellen, and Andrew Metrick define a safe asset as one that is information-insensitive. "To the extent that debt is information-insensitive, it can be used efficiently as collateral in financial transactions, a role in finance that is analogous to the role of money in commerce." Gorton elaborates on this idea in an interview with the Region magazine, explaining that debt is "easiest to trade if you’re sure that neither party knows anything about the payoff on the debt." In other words, "The depositors believe that the collateral has the feature that nobody has any private information about it. We can all just believe that it’s all AAA."

In the 1830s, both the states and the investors in state bonds could "believe that it's all AAA" since, even if investment projects turned out not to generate much revenue, states had seemingly boundless untapped tax potential. Thus it was unnecessary for investors in state bonds to find information about the details of states' particular projects. State debt was information-insensitive, a useful property considering how slowly information traveled across the Atlantic in those days.  No need to calculate probabilities when probabilities wear the "livery of certainty itself."

Gorton says that the few really big crisis events in history come from a regime switch in which debt that is information-insensitive becomes information-sensitive. This is precisely what happened in the U.S. states. During the early 1830s expansion and boom of 1835, state debt was information-insensitive, especially as ever-rising land prices promised a large and growing fiscal reserve. But as several domestic and external factors combined to bring about the panic of 1837 and collapse of 1839, the strength of the fiscal reserve was challenged. As land values and property taxes fell, the quality of state's canal, bank, and railroad investment projects suddenly mattered for their ability to service their debt. The situation is described in another paper by Wallis and Namsuk Kim:

In July of 1839, the Morris Canal and Banking Company of New Jersey defaulted on Indiana, and the state quickly was forced to curtail construction on its network of canals and railroads. By the autumn, Illinois and Michigan were forced to slow or stop construction when investment banks defaulted on their obligations to the states. Land sales and land values in these northwestern states had been rising steadily through the 1830s. When transportation construction stopped, land values and property tax revenues began falling and, by late 1839, it was apparent that these states would soon have trouble servicing their debts. In January
of 1841, Indiana was the first state to default on interest payments.

It was a nasty spiral-- as infrastructure projects failed, land values and tax revenue fell further, eroding the states' fiscal positions, making it harder for them to issue bonds and forcing them to pay higher interest rates. This further deteriorated their fiscal positions, and led to suspensions of infrastructure projects and yet higher interest rates. This is similar to what happened in the eurozone, for example in Greece. In the early 2000s, Greece was able to run large deficits without facing high borrowing costs, because the growing economy made Greek sovereign debt information-insensitive. The economic crisis was a "regime change" making sovereign debt information-sensitive. Without the benefit of a fast-growing economy, the Greek government's ability to pay depending much more on its fiscal position, so borrowing rates rose, causing an even worse fiscal position.

The parallels with Greece continue. Many states found, when they tried to raise taxes, that they lacked the state capacity to do so. Property taxes were extremely politically unpopular, and states had trouble not only passing tax legislation but also implementing tax collection. In Maryland, for example, three counties refused to remit their share of the property tax imposed in 1841, and seven refused in 1842. It wasn't until 1845 that the tax was effectively implemented, allowing Maryland to resume debt service. Other states were even less successful in raising property taxes and ended up defaulting and repudiating their debt. Greece also faces a tax evasion problem and encountered serious public and political opposition to attempted austerity measures. In an earlier post I mentioned a paper by Mark Dincecco and Gabriel Katz called "State Capacity and Long Run Performance." State capacity refers a state's ability to tax and to provide public goods and services, called its extractive and productive capabilities, respectively. Dincecco and Katz write:

We argue that the implementation of uniform tax systems at the national level – which we call “fiscal centralization”– enabled European states to effectively fulfill their extractive role. This transformation typically occurred swiftly and permanently from 1789 onward. Similarly, we argue that the establishment of parliaments that could monitor public expenditures at regular intervals – called “limited government” – enabled them to effectively fulfill their productive role. This transformation typically occurred decades after fiscal centralization over the nineteenth century. By the mid-1800s, most European states had achieved “modern” extractive and productive capabilities, implying that they could gather large tax revenues and effectively channel funds toward non-military public services. We argue that these critical improvements in state capacity had strongly positive performance impacts. 

The institutional changes that Dincecco and Katz describe in the late 18th century Europe that brought about extractive capabilities include fiscal centralization and parliamentary, limited government. The U.S. states in the 1840s, and apparently some of the European states today, lack such state capacity, a fact which plays a role in the crises then and now.

Pennsylvania's canals were a financial disaster, so the state faced particularly high borrowing costs, until the Bank of the United States was rechartered as the Bank of the United States Pennsylvania (BUSP). The bank's charter included a promise to underwrite $6 to 8 million in state bond issues. The bank agreed to lend to Pennsylvania at 4%, and as a result, Pennsylvania bond yields in Philadelphia stayed very near to 4% for the next few years. Wallis and Kim write, "Deliberately or not, the BUSP pegged the price of Pennsylvania bonds as a result of its obligations to purchase state bonds over this 18-month period." This is similar to the ECB's Outright Monetary Transactions (OMT) policy, which, by promising to buy sovereign bonds of Eurozone member states, aims to bring down bond yields and lower borrowing costs for countries that face problems selling debt. Though the bank loan program in Pennsylvania was temporarily successful in helping Pennsylvania borrow at lower cost, when the BUSP closed down in 1841, Pennsylvania bond yields jumped immediately, from 6.01% in January 1841 to 9.5% in March.

What ultimately happened in the United States was that the debt crisis forced a change in the structure of public finance. States initiated constitutional restrictions on debt issue and instituted requirements that new spending be matched by new tax increases. The debt crisis in the euro area is also likely to change the structure of public finance, but not in the same way. The United States is both a monetary union and a fiscal union, so even though the states adopted balanced budget amendments, the federal government could still do countercyclical fiscal policy. The euro area is a monetary union without a fiscal union, so it would be very costly for states to institute such restrictions on deficit spending. One possibility is that the euro area will become more of a fiscal and/or banking union; or there may be other changes in the structure of public finance that I can't foresee.

Safe Assets and Financial Crises

Mark Thoma has shared a link to a new working paper by Gary Gorton and Guillermo Ordoñez called "The Supply and Demand for Safe Assets." The paper brings to mind a once-confidential document written by economists in the Clinton Administration called "Life After Debt" which was recently made public by the team at NPR's Planet Money. The report notes:

In the year 2000, the U.S. Treasury began actively buying back the public debt; we should all appreciate the tremendous achievement this represents for the Nation as a whole... We must realize however, that a sharp reduction in Federal debt and the possible accumulation of a Federal asset raises at least three important issues. First, investors looking for an asset free of credit risk can no longer count on an abundant supply of U.S. Treasury securities, and Treasury securities may no longer provide a reliable benchmark for other interest rates. Second, the Federal Reserve may have to change the mechanisms by which it conducts monetary policy. Third, continued surpluses after the public debt has been paid off will require the Federal. government to acquire assets; either directly or though the Social Security Trust Fund. This raises issues about what kinds of assets might be acquired, and the best way to manage this task.”

Gorton and Ordoñez's paper is relevant to the first of these issues. The Clinton Administration report elaborates on this issue, saying:

US Treasuries are considered free of default risk by investors the world over...The remarkable liquidity of Treasuries is also a result of the full faith and credit of the United States Government.  Holding a liquid asset is valuable because it affords an assurance of convertibility, and thus fast and easy access to capital.  Private investors, the Federal Reserve and many foreign central banks have used Treasuries to fulfill their need for a riskless, performing asset with liquidity second only to currency.

Gorton and Ordoñez note that the share of safe assets in the U.S. economy has remained constant since 1952. However, the composition of these safe assets varies. Safe assets consist of both U.S. Treasuries and privately-produced substitutes, so when the supply of Treasuries declines, the share of private subsitutes rises. What can be a private substitute for Treasuries? Typically, asset-backed securities. Collateral is key.

In Gorton and Ordoñez's model, for simplicity there is just one type of collateral: land. Land can be either "high quality" or "low quality." While the average land quality is known, there is no public information about which land is high quality and which is not. Borrowers can use their land as collateral to finance investment projects, and lenders don't know the land quality unless they pay some cost to find out. This is a type of financial friction: it is inefficient for the economy as a whole if lenders pay a cost to learn about collateral quality, because that cost does not result in any production.

In the model, there are normal times and crisis times. In normal times, the average land quality is high enough that lenders are better off NOT paying to check the quality of the land. The inefficiency from the financial friction is avoided. However, there can be shocks to the average quality of land. Land quality may get low enough that  lenders need to check the land quality before they accept it as collateral, resulting in economic ineffiiency and a financial crisis. This is where Treasuries come in. Government bonds can also be used as collateral, and they don't suffer losses in value like land does. In short:

Since bonds can be effectively used as collateral, a larger fraction of bonds buffers the economy from potential shocks to the expected value of land that may reduce its role as collateral, inducing a lower probability that such shock translates into a financial crisis. This is consistent with the empirical findings of Krishnamurthy and Vissing-Jorgensen (2012a); an increase in Treasury debt decreases the probability of a financial crisis. In our setting this is because bonds can be used as superior substitutes for private collateral – they are independent of shocks to land.

Of course, they are not just advocating for the government to run up a huge debt.The model also includes taxes, and they make the important additional note:

But, if taxes to repay bonds are distortionary, it may be optimal for the government to issue debt in times of crisis, but not in normal times. 

"Land," remember, is a modeling simplification, and really encompasses all types of private collateral. Before the recent financial crisis, there was a surge in the creation of "safe" private assets using "pools" of collateral including loans, bonds, and mortgages. Josh Koval and Erik Stafford explain:

The essence of structured finance activities is the pooling of economic assets like loans, bonds, and mortgages, and the subsequent issuance of a prioritized capital structure of claims, known as tranches, against these collateral pools. As a result of the prioritization scheme used in structuring claims, many of the manufactured tranches are far safer than the average asset in the underlying pool. This ability of structured finance to repackage risks and to create "safe" assets from otherwise risky collateral led to a dramatic expansion in the issuance of structured securities, most of which were viewed by investors to be virtually risk-free and certified as such by the rating agencies. At the core of the recent financial market crisis has been the discovery that these securities are actually far riskier than originally advertised.

For a time, the AAA-rated top tranches of these manufactured assets were considered really safe, and it was like the "normal times" in the model when lenders trust that on average, collateral quality is good enough that they don't need to pay the extra cost to check on it. But then it became apparent that the average quality was much lower, and these assets became less effective collateral, and the financial crisis began. There are at least some claims that the Clinton surplus kicked off the rise in mortgage-backed securities issuance. (I included two graphs below, made using data from FRED, in case you want to evaluate the claims for yourself.) If you decide to read "The Supply and Demand for Safe Assets," please do also look at Krishnamurthy and Vissing-Jorgensen's empirical counterpart. Or, for something lighter, listen to Planet Money's episode "What If We Paid Off The Debt? The Secret Government Report."

Notes on the Princeton Initiative on Macro, Money, and Finance

The Princeton Initiative on Macro, Money, and Finance was a fantastic event. I flew from Oakland through Houston and into Newark on Thursday night and arrived at the Nassau Inn just after midnight. It is a nice and conveniently located hotel. On Friday morning, September 7, I left the Inn around 7 a.m. with my roommate, a finance student at the Haas school at Berkeley. For us Californians, it felt like 4 a.m., but luckily plenty of coffee was provided at breakfast in the gorgeous chemistry building on campus.

The day began with a lecture by Markus Brunnermeier called "A Brief History of Macroeconomics." Interestingly, macro and finance developed along different paths, often developing similar notions using different languages. Now, it seems, they are converging. This lesson on history seemed equally a lesson on the future-- a future in which finance and macro are studied in a more unified framework and financial frictions are taken more seriously. This conference urged us future economists to consider going in that direction.

Professor Brunnermeier continued with a lecture on "Liquidity concepts: amplification, persistence, and asymmetry." He gave a convincing argument that liquidity risk is a more fundamental concern than maturity risk. There are three types of liquidity. On the asset side, there is technological liquidity, referring to the reversibility of investment in physical capital, and market liquidity, referring to the specificity of claims capital (if the second best use of the capital is still nearly as good as the best use, market liquidity is high). On the liability side, funding liquidity is tied to the maturity structure of debt. We saw many times in the lecture how different classic and newer papers have incorporated one or more types of illiquidity. For example, technological illiquidity is represented by capital adjustment costs, or in the extreme case by a fixed capital stock.

Another interesting concept he discussed was the "volatility paradox." An interesting implication of some of his models is that systemic risk can build up in times of low volatility, in the form of bubbles or imbalances. Then, once a crisis hits, this risk that has stealthily built up in the background results in direct and indirect spillover effects. Direct spillovers come from the direct contractual interconnectedness of the financial sector, and have been studied a lot and found to be important, but not enough to account for the severity of crises. Indirect spillovers, like fire-sale externalities, credit crunches, and liquidity spirals, are as of yet less studied, but seem much more dramatic. The thing about these effects is that they are a "general equilibrium phenomenon": you cannot simply detect them in the data unless you have a model (and it must be a dynamic model) because there is not a simple story of cause and an effect. All agents are optimizing across time and taking other agents' optimizations into account.

It was extremely helpful to get an overview of the broad similarities and key differences between the important papers in the financial frictions literature: Townsend 1979, Bernanke and Gertler 1989, Carlstrom and Fuerst 1997, Kiyotaki and Moore 1997, and a number of others. I had seen most of these papers in previous classes, but seeing how they fit together and how the field has progressed in a logical order was enlightening.

Professor Yuliy Sannikov gave the pre- and post- lunch lectures on "Heterogeneous agent models with financial frictions: a continuous time approach." The reason to study heterogeneous agent models is due to the fact that with incomplete markets, distribution of wealth matters (because agents cannot fully insure themselves.) Non economists may find it strange to learn that economists usually assume that the economy can be modeled as if there were a single representative consumer. With no financial frictions, this is perfectly reasonable. Moreover, it comes from one of the most powerful results in macroeconomics--if agents have access to complete markets, they will trade securities in a way that smoothest consumption across possible future states of the world. This then makes the math of "aggregating" agents very nice; you can just act as if they are a single agent. I can't emphasize enough how crucially most macroeconomic models rely on this result. This is precisely why, if financial frictions matter, they will matter in a big way.

To present his recent work with Professor Brunnermeier (they call it the BruSan model), Professor Sannikov began by going over a more basic version by Basak and Cuoco (1998). A critical step in a model with heterogeneous agents is defining a state variable that characterizes the wealth distribution in the economy, and then finding the law of motion for that variable. In this model, there were two types of agents, and the state variable was just the fraction of wealth held by one of the agents. Im not sure, with more than two types of agents, whether you would represent the wealth distribution with a single summary statistic or if you would need to use multiple states (maybe the number of agents minus 1?) to have high enough information content. Since I have not taken any continuous time asset pricing classes, I was grateful to get the basic model first, to see the notation and basic tools for continuous time stochastic processes. A capital dividend stream is often assumed to be a Brownian motion process, and to characterize the laws of motion for functions of continuous variables, you can use Ito's lemma, which is kind of like using the chain rule and product rule in "regular" calculus. The "history" lecture in the morning mentioned that finance began in continuous time and macro in discrete time. Since I have mostly studied macro and barely studied finance, continuous time models are not very familiar. I will certainly find a textbook on stochastic calculus to browse and then take another read of BruSan.

The final lecture of the day, by Professor David Sraer, was "Financial frictions: empirical facts." Neoclassical models with complete markets and no financial frictions make some basic predictions, such as how investment should react to cash flow (in short, it shouldn't.) Taking the neoclassical model as the "null hypothesis," can we find any empirical evidence that would cleanly reject the null? There have been a large number of suggestive studies but no "smoking gun." It is very hard to conclusively show that there are financial frictions, much less what type of financial frictions. That doesn't mean that they don't exist-- it seems, anecdotally and theoretically and even intuitively, that they exist and matter hugely, they are just very tricky to identify. As I mentioned, we are trying to study general equilibrium effects. A major impediment is endogeneity. Some of the papers that Professor Sraer discussed used difference-in-differences or even triple differences specifications to try to alleviate the extent of the identification. I wonder if using such specifications can ever be more than just suggestive. What would it take to be convincing?

After the Friday lectures we had a barbecue at the Bendheim Center for Finance. At dinner, like at the other meals, it was great fun to meet the other students from economics departments and business schools across the country (and even a few from schools in Europe.) I am always curious about what the graduate student experience is like in other places, and it is also neat to hear stories about what certain professors (whom I will leave unnamed) are like in person. We also got to visit a bar in downtown Princeton. The downtown Princeton street bordering campus is populated by J. Crew, Banana Republic, Ugg Boots, and Ann Taylor shops, in lieu of the the tie dye t-shirt stands on Telegraph by the Berkeley campus. The bar in downtown Princeton curiously--almost eerily-- played no music. But that did facilitate more (relatively non-econ-related) conversation.

On Saturday we heard the first presentation of the latest BruSan paper on the redistributive impact of monetary policy. It sounds like a simple idea but is really quite profound. In New Keynesian models, the reason monetary policy has an effect is because of price stickiness. Monetary policy works through its ability to alter price setting or minimize price distortions. In BruSan's so-called "I theory," monetary policy has an effect through it's ability to change the distribution of wealth. The I stands for intermediaries or for inside money. To me it seems to obvious that monetary policy affects different people differently, and that this matters, that I could hardly believe this was something new.  I think if you asked the average person on the street what they thought about monetary policy, they would complain about it being unfair in some way or another, and redistribution always seems unfair to a lot of people. But in the representative agent paradigm, this effect does not exist, because there are not different agents!

I am still pretty surprised that awareness of the redistributive impact of monetary policy was not high enough to convince people to study heterogeneous agent models more intensely long earlier. I guess it took the crisis and associated nonconventional monetary policies to drive it home. And adding agent heterogeneity, which we must do if we take financial frictions seriously, is hard! The analytical tools are only in the early stages of development. Again to give non economists an idea, at this point a "heterogeneous agent" model may well mean you have two agents instead of one. But that makes it way more than twice as hard. And there are so many possible ways of introducing heterogeneous agents that it can be daunting. To make your analysis tractable, you have to you have to make a lot of simplifying assumptions, but need to make them carefully so that the analysis still has some hope of being somewhat meaningful. Surely, the techniques that we today consider pretty easy and straightforward were also considered hard and daunting in their early stages. I am confident that the toolkit for heterogeneous agent models will develop significantly and probably rapidly (both because it is so highly demanded, and-- from what I saw at Princeton--so many smart people are up for working in it.) As the toolkit progresses, the I Theory of Monetary Policy seems one of the most natural and important applications. Moreover, it will allow us to analyze the interactions between financial stability and price stability.

The next lecturer, Professor Ben Moll, did give us some helpful suggestions for making life easier with heterogeneous agent models. First and most obvious is to give agents log utility, so that consumption is a constant fraction of wealth. Professor Sannikov also showed us earlier why log utility implies that the Sharpe ratio is equal to the volatility of wealth. Professor Moll also recommended using continuous time stochastic processes, particularly when your model is to have persistent shocks, and using constant returns to scale production functions. Finally, he told us a useful equivalence result. You can either assume that firms own and accumulate capital, issue debt, and face collateral constraints, or you can assume that firms rent capital and face a rental limit. Results are equivalent, but the rental formulation may make the model more tractable.

Professor Moll discussed "Productivity losses from financial frictions." He made a point worth repeating, that differences in income between countries are much larger than differences in income across the business cycle in a given country. So arguably it is more important to study why there are cross country income differences than to study business cycle fluctuations. Now a lot of studies have concluded that differences in capital between countries are not nearly enough to explain differences in income. The main explanation is differences in total factor productivity (TFP), which basically means differences in how effectively the economy turns inputs into outputs. Measured TFP is the so-called Solow's residual, which is a euphemism for "what we (economists) don't know is going on." (Maybe that's not 100% fair. Someone should correct me if not.) Residuals, by construction, are the unexplained. To explain the unexplained, one entry point is financial frictions. This is the path Professor Moll takes. Financial frictions can lead to capital misallocation, i.e. putting capital to less than best uses, which can lead to TFP losses.

How do financial frictions lead to capital misallocation? In the model he presents, agents are entrepreneurs who are heterogenous in their productivity and wealth. The financial friction is a collateral constraint. They can borrow up to a certain multiple of their wealth; that multiple represents the quality of financial markets. Depending on their productivity and how much they can borrow, it may or may not be profitable for them to undertake their project. It turns out that there is a productivity cutoff for being an active entrepreneur or not. Measured TFP for the whole economy depends on the cutoff, which depends on the quality of financial markets. My housemates are development economists. I want to ask them how they think about TFP differences between countries and the role of finance.

The next Saturday lectures was "Bubbles and crashes," by Professor Brunnermeier. I learned the Brunnermeier and Abreu model of rational bubbles in a first year class, but what didn't stand out to me until this lecture is the following. Normally, backwards induction arguments rule out the possibility of a rational bubble. But such backwards induction requires common knowledge. The fact that agents are sequentially made aware of mispricing means that they eventually have mutual knowledge of first order, then of second order, and so on, but common knowledge is mutual knowledge of infinite order, which doesn't happen in finite time. Game theory and information theory are taught as segments of our first year microeconomics sequence, but they are important in macroeconomic models with financial frictions.

The final Saturday lecture was "A welfare criterion for models with distorted beliefs," by Professor Wei Xiong. He opened with a funny anecdote. Supposedly, economists Stiglitz and Wilson made a bet of $100 about whether a pillow was stuffed with natural down or synthetic fiber. One believed the probability of down was 10%, the other thought 90%. To find out who won the bet, they had to cut open (and destroy) the pillow, and they agreed to split the $50 replacement cost. Both economists had an expected value of $55 for the bet (left as an exercise for the reader) so they both happily agreed to it. But the bet had a negative net value! Whatever happened would result in a transfer payment of $100 from one economist to another, and destruction of a pillow worth $50! Bets between economists have high pedagogical and entertainment value. Maybe other people find less destructive ways to entertain themselves. But there are many more common trades that result from heterogeneous beliefs and have negative net value. A social planner should be able to make people "better off," but the question is, what beliefs should the planner use to evaluate welfare? Xiong introduces a belief-neutral welfare criterion. The set of reasonable beliefs is the set of convex combinations of agents' beliefs (in the pillow example, any probability between 0% and 90% that the pillow is down is a reasonable belief.) Choice x is called (in)efficient if it is Pareto (in)efficient evaluated using any reasonable belief. Note that a choice may be neither efficient nor inefficient under this definition. I really enjoyed this lecture, and wonder if I should attempt to do research that is more decision-theoretic. I have realized that I tend to be drawn toward topics with a common thread of uncertainty, belief formation, information processing, and ambiguity.

On Sunday morning I woke up early enough to go running on campus and on a nice crushed gravel waterside path. Next, the morning lecture was given by Professor Chris Sims on the "Fiscal Theory of the Price Level." More aptly, the lecture was what he called a "metafiscal" theory of the price level, talking about the model from above and outside of it more than really going into it. This is what I love most, to be honest. I would be a metaeconomist if that were an option, since I spend much more time thinking about economics (in the meta sense) than thinking about the economy, and with more passion. I am the kind of person who likes the Introduction chapter of books best of all, especially if it goes way into why and how they decided to write the book. Not too productive at this stage in my career, I know. As a macro student, literally hundreds of times I have taken this little step where you have a one period budget constraint (for the government, say) and you sum it up over all periods (yes, back in discrete time!) and appeal to a transversality condition to derive an intertemporal budget constraint. The transversality condition is a restriction on asset value as time goes to infinity(!) and all professors have a slightly different way of explaining it to students. I understood the gist of why, economically and theoretically, it is needed, but sometimes its implications did seem slightly unsettling. I think Professor Sims' explanation got to the heart of why it sometimes seemed odd. The single period constraint is an accounting identity, but the TVC is NOT. It is an equilibrium condition. Professor Sims also argued that conventional ways of thinking about the independence of monetary policymakers and fiscal policymakers should be reconsidered.

Professors Nobuhiro Kiyotaki and Atif Mian gave the final lectures of the conference. Anyone who is familiar with the Kiyotaki and Moore model of credit cycles, now part of the canon for first year grad students, may be interested in Professor Kiyotaki's newer model of banking, liquidity and bank runs. I took a course on Empirical Macro Finance with Professor Mian last semester that greatly sharpened my interest in the intersection of macroeconomics and finance and taught me a lot about empirical identification strategies.

One thing I didn't realize before the Initiative is that economic history is not so common to study at non-UC schools. When I mentioned that economic history is one of my fields, people often thought I meant history of economic thought. And though the Initiative did not have any explicit focus on economic history, nearly every lecture brought to mind historical episodes I have studied. An absolute must-read, in my opinion, is "Finance Capitalism and Germany's Rise to Industrial Power" by Caroline Fohlin. To study financial frictions, it seems quite useful to study earlier stages of financial development. It is not immediately apparent whether less developed financial systems would have more or less severe financial frictions. There have likely been complicated interactions between technological and financial innovation and economic development over the centuries. Some innovations alleviating certain frictions and exacerbating others. My colleague Glenda Oskar, on the job market this year, is researching 19th century capital markets. She looks in particular at a California statute passed in 1861 that granted mining companies the right to levy assessments (like negative dividends) on existing shareholders. She has, impressively, collected a dataset that allows her to study when and under what types of conditions different mining companies exercised this privilege. How did financial frictions in that era compare to today, and what can that teach us about their impact?

Closely related to economic history is the economics of institutions, also much emphasized at Berkeley. Financial frictions depend on the "rules of the game," which realistically are neither exogenous nor static. For now, most models with financial frictions implicitly take the institutional arrangements as given. However,  both positive and normative analysis will benefit if we eventually bring institutions into the model. Many policy changes in response to crises are actually institutional changes.

The Princeton Initiative gave me a lot of interesting ideas to think about. I am grateful to everyone who made it possible. 

Introduction to the Quantitative Ease Blog

Welcome to the Quantitative Ease blog. I am a graduate student in economics at UC Berkeley and study macroeconomics and economic history. I have been blogging about a variety of topics for the past few years and now want to start a new blog with an economics focus. The following posts from my old blog may be of interest:

• Kitchen Sink Crisis: Tough Lessons from Home Economics
• Robustness
• Princeton Initiative: Macro, Money and Finance
• The First Crash
• Reading Lists
• American Exceptionalism: A Sequel
• American Exceptionalism, Then and Now
• Unsecured Consumer Credit with Risk of Default
• The Economics of Structured Finance
• After the Crisis
• To Debt
• Debt Ceiling Blame Game
•