by Anantha Divakaruni∗ Peter Zimmerman†
The Lightning Network (LN) is a means of netting Bitcoin payments outside the blockchain. We find a significant association between LN adoption and reduced blockchain congestion, suggesting that the LN has helped improve the efficiency of Bitcoin as a means of payment. This improvement cannot be explained by other factors, such as changes in demand or the adoption of SegWit. We find mixed evidence on whether increased centralization in the Lightning Network has improved its efficiency. Our findings have implications for the future of cryptocurrencies as a means of payment and their environmental footprint.
Bitcoin was originally designed to serve as a “peer-to-peer electronic cash system” — that is, a reliable means of payment outside the control of centralized monetary authorities (Nakamoto (2008)). Since its introduction in 2009, Bitcoin has grown immensely in value, but still sees relatively little use as a means of payment (Bolt and van Oordt (2020)). One important reason is that Bitcoin’s blockchain technology imposes capacity constraints on processing transactions. These constraints allow Bitcoin to handle, on average, merely seven transactions per second, which compares unfavorably with centralized payment systems such as Visa or Mastercard.1 When transaction demand is high, the processing limits mean that Bitcoin transactions can take a long time to settle. In recent years, many solutions have been proposed to resolve this so-called scalability problem, to help Bitcoin achieve its potential as a large-scale payments system. One such solution is the Lightning Network (LN), which allows Bitcoin users to make payments outside the blockchain. Rather than inscribe every individual payment onto the blockchain, two individuals can open an LN channel and make bilateral payments. Once they have completed their payments, they can close the channel and settle the net amount. In principle, doing this requires only two transactions on the blockchain — one to open the channel, and another to close it — regardless of the amount settled or the number of underlying payments. In this way, adoption of the LN can reduce demand for blockchain space and ease congestion. We find that adoption of the Lightning Network has led to a reduction in Bitcoin blockchain congestion and lower mining fees. The results are significant, both statistically and economically, and cannot be explained by changes in demand for blockchain space, nor by other technological developments. We find limited evidence that greater centralization of the network is associated with lower fees. Our results suggest that the Lightning Network can help Bitcoin achieve greater scalability, allowing it to operate better as a payments system. According to our results, if the LN had existed in 2017, congestion could have been 93 percent lower. Our analysis covers the period January 1, 2017, to September 5, 2019. Data limitations prevent us from extending our data set. The Lightning Network continues to grow, doubling in size over 2021.2 There has been institutional adoption, too. For example, Twitter allows tipping using the LN, among other payment methods.3 El Salvador enables Bitcoin payments among its citizens using the Chivo Wallet, which features LN functionality (Alvarez, Argente, and Patten (2022)). And several cryptocurrency exchanges have introduced support for the LN.4 But recent episodes of high congestion, especially in early 2021, suggest that the LN is not a panacea. The development of the Lightning Network may have consequences for welfare. First, as Bitcoin becomes a more efficient payments system, users are better off. Their transactions settle more quickly and more cheaply (Zimmerman (2020)). Second, since fewer transactions need to be recorded on the blockchain, less memory and energy are needed to run a Bitcoin node. This saving lowers the cost of maintaining the blockchain, allowing more nodes to participate and making the system more secure against a double-spending attack (Budish (2018)). Third, by reducing fees, the LN reduces the incentive for Bitcoin miners to use large amounts of computing power, meaning less energy use and positive consequences for the environment.5 Fourth, less blockchain congestion may mean lower barriers to arbitrage across cryptocurrency exchanges, thereby improving market liquidity (see Hautsch, Scheuch, and Voigt (2018)). While this paper focuses on Bitcoin, the same technology can allow other cryptocurrencies to be widely used, secure, and decentralized. For example, the Raiden Network is a similar netting solution for Ethereum. Other solutions to the scalability problem have been proposed, including sharding, and batching at exchange level.6 If the scalability problem can be successfully addressed, it may be possible for a currency based on a permissionless blockchain to obtain wide acceptance. The rest of the paper is organized as follows. Section 2 briefly describes the Lightning Network and outlines findings from the existing literature. Section 3 describes our data, and Section 4 our results. Section 5 concludes
2 The Lightning Network
The Lightning Network was first introduced by Poon and Dryja (2016), and began to attract widespread usage in January 2018. The LN is a secondary transaction layer that operates outside the blockchain. Two users open an LN channel by contributing Bitcoin to a smart contract. They can then transfer these coins between them without creating traffic on the blockchain (Auer (2019)). Once the channel is closed, only the net amount needs to be settled on-chain as a single payment. This netting reduces the required number of on-chain transactions to just two: one to introduce the smart contract that opens an LN channel, and a second to close it. In this way, the system can handle a much larger number of payments. Arcane Research (2022) provides an up-to-date description of the Lightning Network. In payments system terms, the Lightning Network can be thought of as a net settlement system appended to Bitcoin’s gross settlement system (Kahn, McAndrews, and Roberds (2003)). This economizes on liquidity, but introduces counterparty credit risk. The LN introduces various safeguards to minimize the risk of counterparty default. In particular, if one party tries to close the channel without the approval of the other, she may forfeit her claim on any Bitcoin that are locked in. The Lightning Network protocol itself relies on Segregated Witness (SegWit), which is a change to the Bitcoin transaction format activated on August 23, 2017. SegWit improves the efficiency of blockchain storage, so that a single Bitcoin block can potentially store up to four times as many transactions as before. Brown, Chiu, and Koeppl (2021) show that the introduction of SegWit has reduced Bitcoin mining fees. Only a couple of papers in the economics and finance literature focus on the Lightning Network. Guasoni, Huberman, and Shikhelman (2021) build a strategic model in which Bitcoin users choose whether to open LN channels, and examine the characteristics of the network that emerges. Bertucci (2020) studies a strategic model of network formation and shows that competition between nodes prevents the network from becoming highly centralized. More broadly, our paper relates to a literature examining the fee-based market for blockchain space; see, for example, Easley, O’Hara, and Basu (2019), Hautsch, Scheuch, and Voigt (2018), Huberman, Leshno, and Moallemi (2021), Lehar and Parlour (2020), Makarov and Schoar (2020), and Zimmerman (2020). In the computer science literature, papers have focused on the financial viability of the LN (e.g., B´eres, Seres, and Bencz´ur (2019) and Brˆanzei, Segal-Halevi, and Zohar (2017)); its network structure (e.g., Lin et al. (2020) and Martinazzi and Flori (2020)); and its ability to guarantee security and privacy (e.g., Harris and Zohar (2020), Kappos et al. (2021), and P´erez-Sol`a et al. (2020))
We aim to test whether adoption of the Lightning Network is associated with reduced congestion on the Bitcoin blockchain. We construct measures of congestion using data on the Bitcoin mempool; that is, the set of payments waiting to be added to the blockchain. Our data come from Jochen Hoenicke.7 We collect data on: (i) the number of pending transactions (mempool txn count); (ii) the fees attached to pending transactions (mempool txn fees); and (iii) the proportion of transactions with fees under 10 satoshis per virtual byte (low fee txns).8 Data on the Lightning Network come from the website hashXP.9 This repository contains detailed historical information on all public Lightning nodes (both active and inactive), channels between these nodes (both open and closed), and channel capacity (in bitcoin and USD). In addition, hashXP provides complete details of Bitcoin transactions executed in order to open and close LN channels.10 The shape of the Lightning Network may affect its efficiency. For example, if Lightning channels tend to be intermediated via a few central nodes, then collateral (i.e., the Bitcoin that users have locked into the LN) can be used more efficiently. In other words, when the network is more
centralized, each channel, and each Bitcoin locked into the protocol, is likely to support a higher volume of payments (see Martinazzi and Flori (2020)). To account for this effect, we include the LN clustering coefficient as an independent variable. This network statistic is defined by Watts and Strogatz (1998) as the average probability that two neighbors of any given node are themselves connected. When the network is more centralized, the clustering coefficient is lower. Thus, we predict that when the clustering coefficient is high, mempool congestion is worse. As Figure 1 shows, the network has tended to become more clustered — and thus less centralized — as it develops, though the last few months of the sample period show a trend toward greater centralization. Figure 1: Mean clustering coefficient among Lightning Network nodes. Source: hashXP. We introduce proxies for Bitcoin demand over this period. Higher demand for transactions on the Bitcoin blockchain can increase congestion for reasons unrelated to LN adoption, so we need to take it into account. While demand cannot be observed directly, Liu and Tsyvinski (2020) suggest that it is positively related to historical price changes; in other words, there is a momentum factor. Motivated by this observation, we introduce 1-day price change as the log-lagged change in the Bitcoin price at midnight UTC (Coordinated Universal Time) each day. We also use price volatility as a proxy for speculative demand for Bitcoin. We define 30-day volatility as the rolling standard deviation of Bitcoin returns from each of the past 30 trading days. These two measures are computed using price data from Coin Metrics (https://coinmetrics.io/). We also include a measure for the supply of blockchain space. Unlike demand, supply is directly observable ex post, since we can see how many blocks were created each day. We proxy supply by dividing miners’ total hash rate divided by average mining difficulty, using Coin Metrics data. We call this measure mining intensity. While the Bitcoin protocol aims for a long-run mean of one block every 10 minutes, the realized rate of block creation can vary due to chance, or due to changes in miners’ hash rate since the previous difficulty adjustment (Nakamoto (2008)). In addition, since SegWit adoption may affect mempool congestion, we control for it in our regressions. We obtain data from Bitcoin Visuals on the estimated proportion of Bitcoin transactions that use SegWit (https://bitcoinvisuals.com/chain-tx-block). A description of each variable can be found in the Appendix. Our sample period contains daily data from January 1, 2017, to September 5, 2019, so it includes a period of about a year before the LN was widely adopted. We cannot extend our data set any later because, beyond these dates, hashXP was no longer actively monitoring the Lightning Network and providing accurate data. As a result, we are unable to study any more recent developments in the LN. Hoenicke’s mempool data set is missing six days: Feb 1, 2017; Apr 17–19, 2017; Jun 1, 2019; and Jun 26, 2019. We drop these days from our data set. We use first-differenced data (see later in this section), so we also drop the following days (i.e., Feb 2, 2017; Apr 20, 2017; Jun 2, 2019; Jun 27, 2019). As a result, we have a total of 968 daily observations of the dependent variables. In addition, the Coin Metrics data on prices are missing one day (Jan 1, 2019). Table 1 shows summary statistics for our data. Many of the variables are highly volatile with substantive right-skew. Because of this skewness, we use the logarithms of mempool txn count, mempool txn fees, LN channels, and LN capacity in our regressions. Figure 2a plots the number of transactions waiting to be confirmed in Bitcoin’s mempool (denoting congestion) over our sample period, along with active LN channels over time and the percentage of transactions that use SegWit. Congestion in Bitcoin has fallen markedly since early 2018, coinciding with the introduction and rapid adoption of the LN. Congestion has remained relatively low since
Table 1: Summary statistics. count mean std dev min median max Mempool txn count 968 23,042 40,619 92 5,731 252,750 Mempool txn fees (USD) 968 106,180 440,206 39 3,008 4,750,619 Low fee txns (%) 968 53.45 28.30 0 52.04 95.99 Lightning Network channels 968 12,671 15,374 0 7,575 44,087 Lightning Network capacity (USD) 968 2,766,535 4,080,066 0 205,388 11,794,337 Lightning Network mean clustering 968 0.06 0.07 0 0.06 0.19 SegWit txns (%) 968 20.72 15.48 0 27.61 46.80 30-day volatility 968 4.16 1.54 1.10 4.03 8.07 1-day price change 967 0.00 0.04 -0.21 0.00 0.23 Mining intensity 968 7.49 0.82 3.98 7.51 9.79 Notes: Daily data from January 1, 2017, to September 5, 2019. See the Appendix for variable definitions and data sources. then, although it picked up slightly in mid-2019.11 Figure 2b plots similar measures weighted by monetary value: we measure congestion using mempool fees, LN adoption using the USD value of locked Bitcoin, and SegWit usage by the monetary value of transactions. Total fees attached to payments waiting in the mempool have fallen since 2017, suggesting either lower demand or greater supply of settlement capacity. Over this period, the total value of Bitcoin used to collateralize LN channels has risen. Figure 3 shows that the distribution of fees has changed over our sample period. Generally, fees have fallen in nominal bitcoin terms. The proportion of transactions with fees below 10 satoshis per virtual byte rose from 32.6 percent on January 1, 2018, to 74.2 percent on September 5, 2019.12 We are interested in whether LN adoption is associated with lower mempool congestion. We test for relationships using autoregressive integrated moving average (ARIMA) specifications, which
Notes: Daily data from January 1, 2017, to September 5, 2019. The chart plots fees in satoshis per virtual byte. There are 100 million satoshis to a bitcoin. Source: Jochen Hoenicke. where c is a constant term, y d is the variable of interest expressed after taking d differences, Xd t is a vector of the d-differenced independent variables, and εt is a residual term. The parameter p is the number of lags of the variable of interest, d is the number of differences taken, and q is the length of the moving average window of historical residual terms. For each specification, we estimate the parameters (p, d, q) using the Hyndman-Khandakar algorithm (Hyndman and Khandakar (2008)). The time variable t is daily. We employ robust standard errors, since we cannot be sure of homoskedasticity. Figures 2a and 2b suggest that the data are non-stationary. We take first-differences of all our variables. Augmented Dickey-Fuller (ADF) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests confirm that these first-differenced variables are stationary (i.e., d = 1).
We run three sets of regressions. First, we test the effect of LN adoption on mempool count, using the number of LN channels. We run four versions of this model. Model (1) contains no controls; model (2) includes the demand and supply controls; model (3) includes the proportion of transactions that use SegWit; and model (4) has all the controls. Table 2 reports the results. In each of the four specifications, an increase in the number of LN channels reduces the mempool 9 count. The results are significant at the 1 percent level. None of the supply and demand controls have a significant impact on mempool size.13 Table 2: Impact of Lightning Network adoption by number of channels on mempool count. (1) (2) (3) (4) ΔLN channels (log) -0.247∗∗∗ -0.244∗∗∗ -0.251∗∗∗ -0.249∗∗∗ (0.075) (0.077) (0.077) (0.078) ΔLN mean clustering -3.857 -3.803 -4.004 -4.007 (5.959) (6.073) (5.800) (5.918) ΔSegWit txns (%) 0.016 0.018 (0.012) (0.012) Δ30-day volatility -0.024 -0.020 (0.082) (0.082) Δ1-day price change -0.738 -0.744 (0.622) (0.620) ΔMining intensity 0.033 0.042 (0.048) (0.049) Constant -0.001 -0.001 -0.001 -0.002 (0.009) (0.009) (0.009) (0.009) Observations 967 966 967 966 AIC 2589 2590 2589 2589 Notes: Regressions of LN channels (log) on mempool transaction count (log). In all four models, the parameters selected by the Hyndman-Khandakar algorithm are: p = 6 lagged terms included for the dependent variable, d = 1 difference taken, and q = 2 length of window for the moving average of historical residual terms. Data are from January 1, 2017, to September 5, 2019. See the Appendix for variable definitions and data. Our second set of results tests a similar relationship using US dollar values. We regress the USD value of Bitcoin locked into the LN against the USD value of fees attached to mempool transactions. Table 3 shows the results. As before, greater LN capacity is associated with reduced congestion. This time, however, the results are not significant at the 5 percent level once we include supply and demand controls. Finally, we investigate how the LN affects the proportion of low fee transactions in the mempool. 13For each model, Portmanteau and Durbin-Watson tests suggest no evidence of autocorrelation in the residuals. 10 Table 3: Impact of Lightning Network adoption by capacity value on mempool fees. (1) (2) (3) (4) ΔLN capacity (USD log) -0.198∗∗ -0.199∗ -0.197∗∗ -0.198∗ (0.099) (0.102) (0.100) (0.102) ΔLN mean clustering -6.889 -7.233 -7.150 -7.572 (7.940) (7.977) (7.652) (7.674) ΔSegWit txns (%) 0.025∗ 0.027∗ (0.014) (0.014) Δ30-day volatility 0.115 0.122 (0.105) (0.105) Δ1-day price change -0.549 -0.548 (0.837) (0.835) ΔMining intensity 0.030 0.041 (0.058) (0.058) Constant 0.003 0.003 0.002 0.002 (0.010) (0.010) (0.010) (0.010) Observations 967 966 967 966 AIC 3042 3043 3040 3041 Notes: Regressions of LN capacity (USD log) on mempool fees (USD log). In all four models, the parameters selected by the Hyndman-Khandakar algorithm are the same: p = 6 lagged terms included for the dependent variable, d = 1 difference taken, and q = 1 length of window for the moving average of historical residual terms. Data are from January 1, 2017, to September 5, 2019. See the Appendix for variable definitions and data. Table 4 shows that greater LN usage is associated with a significant increase in low fee transactions. Unlike the first two sets of regressions, clustering has a significant and negative impact on low fees. In other words, a more centralized network means that transactions are likelier to have low fees, in line with our priors. Overall, these results suggest that increased LN usage is associated with a significant reduction in mempool congestion. Since there is no theoretical upper limit on LN usage, there is the potential for further reductions in congestion in the future. However, network centralization does not have a clear effect on the efficiency of the network.14 14We also run a set of regressions that include interaction terms between the LN adoption variable and the mean clustering coefficient. In each case, the interaction terms are not statistically significant and their inclusion does not affect the other results. More details are available upon request. 11 Table 4: Impact of Lightning Network adoption by number of channels on low fee mempool transactions. (1) (2) (3) (4) ΔLN channels (log) 0.192∗∗∗ 0.186∗∗∗ 0.195∗∗∗ 0.187∗∗∗ (0.034) (0.035) (0.034) (0.035) ΔLN mean clustering -2.244∗∗∗ -2.295∗∗∗ -2.169∗∗ -2.144∗∗ (0.858) (0.863) (0.864) (0.871) ΔSegWit txns (%) -0.016∗∗∗ -0.016∗∗∗ (0.004) (0.004) Δ30-day volatility -0.018 -0.024 (0.024) (0.024) Δ1-day price change 0.059 0.065 (0.224) (0.223) ΔMining intensity 0.042∗∗∗ 0.036∗∗ (0.015) (0.015) Constant -0.002 -0.002 -0.002 -0.001 (0.002) (0.002) (0.002) (0.002) Observations 967 966 967 966 AIC 692 674 676 658 Notes: Regressions of LN channels (log) on proportion of mempool transactions with fees below 10 satoshis per virtual byte. In all models, the parameters selected by the Hyndman-Khandakar algorithm are the same: p = 6 lagged terms included for the dependent variable, d = 1 difference taken, and q = 2 length of window for the moving average of historical residual terms. Data are from January 1, 2017, to September 5, 2019. See the Appendix for variable definitions and data. In each regression, SegWit has the opposite effect of Lightning Network adoption on mempool congestion, although the results are only significant in Table 4. At first glance, the signs of the coefficients are surprising: greater use of SegWit appears to increase, rather than reduce, congestion. There are a number of possible explanations. First, LN transactions require SegWit, so there is some positive correlation between these variables. However, the exact relationship is not clear, since we do not have data on the number of LN transactions, only on the number of channels and the value of Bitcoin locked in. Second, the causality is not clear. It may be that periods of high congestion encourage greater SegWit usage. Third, since SegWit transactions use fewer virtual bytes than non-SegWit transactions (all else equal), users may be willing to pay a higher fee per 12 virtual byte.15 We can assess the economic significance of reducing Bitcoin congestion by posing the following counterfactual question: if, during 2017, the LN had existed and been the size it was at the end of our sample, by how much would Bitcoin congestion have been lowered? Our results suggest that the mempool count would have been 93 percent lower, mempool fees 96 percent lower, and the proportion of low fee transactions 197 percent higher. These numbers demonstrate that the LN can potentially have a substantial impact on blockchain congestion.
We show that usage of the Lightning Network is associated with reduced mempool congestion in Bitcoin and with lower fees. Our findings suggest that the off-chain netting benefits of the Lightning Network can help Bitcoin to scale and function better as a means of payment. Centralization of the Lightning Network does not appear to make it much more efficient, though it may increase the proportion of low fee transactions. Data are not available on how Bitcoin is used, so we cannot say for sure whether Bitcoin is being increasingly used as a means of payment. Makarov and Schoar (2021) study blockchain data and conclude that the majority of usage is for trading and speculative purposes, but their analysis does not extend to transactions that take place on the Lightning Network. We can only say that the Lightning Network loosens a key technological constraint by allowing payments to be settled more quickly
FRONTLINE, the American news magazine, is critiquing the Federal Reserve for showering big banks, big business and Wall Street with easy money. Money that has neither reached the real economy nor the vast majority of Americans. Yes to the latter, no no no no no to the former.
31:00 “We take financial media’s words in their face but if their words are simply based on calling up the Fed ( or market makers) and asking what they did [without checking on their veracity…this is problematic for the average person who relies on such journalism to understand what’s going in the market].” Given Jeff’s sentiments, I was rather disappointed he had allowed Isabella Kaminsky exaggerate to Putin’s assertions regarding Ukraine in your latest interview a few days ago. That said, I appreciate you both going over the FrontLine docuseries on Money and the Fed…I have watched all of them (some twice)You guys look into RICHARD WIENER’S work on JAPAN. SAMURAI SUN.
AN INTERVIEW WITH JAMES RICKARDS
Octavian Report: Could you take us through what you see happening to the monetary system?
James Rickards: The dollar, the yen, the euro — all are forms of money. I would say gold is a form of money. Bitcoin is a form of money. In times past, feathers and clam shells have been money. One of the criticisms of many modern forms of money, of central bank money and Bitcoin in particular, is that they are not backed by anything.
I make the point — and I’m not the first one to say this, I actually learned this from Paul Volcker — that it is backed by one thing, which is confidence. Meaning if we have confidence that something is money, then it’s money. If you and I think something’s money, and you’re willing to take it from me in exchange for goods and services, and furthermore you believe that you can give it to someone else in exchange for goods and services and make investments, then it’s money. It functions as money. But the problem with confidence is that it’s fine as long as it lasts, but it’s very fragile and it’s very easily lost. Once it’s lost, it’s impossible to get back, or at least extremely difficult to get back.
When I talk about the collapse of the international monetary system, people think I must be an apocalyptic doom-and-gloomer. End of the world, we’re all going to be living in caves, eating canned goods. I say no, not at all. I don’t think it’s the end of the world, I just think it’s the end of a system that has in fact collapsed three times in the past 100 years. There’s nothing unusual about breakdowns in the international monetary system. When it happens, the major trading financial powers get together, sit around a table, and rewrite what they call the rules of the game. “Rules of the game” is actually a phrase, a term of art in international finance, for the operating system (just to use modern jargon) of the international monetary system.
All I’m doing is looking at the system dynamics. It’s very easy to see the collapse coming based on that, and then ask the question: when the collapse comes, what will the new system look like? That is, what will it look like after the next Bretton Woods, or the next Smithsonian agreement, or for that matter Genoa in 1922? The next time the powers sit around the table, what deal will they come up with? Then I work backwards from that to today, and ask: what can I or should I do with my portfolio now to prepare for this new deal?
That’s the scenario. Now, I have the view that basically all the central-bank models, all the risk-management models used on Wall Street and in capital markets around the world are obsolete. There are much better tools available today. The three that I use most frequently — not the only three — are complexity theory, behavioral psychology, and causal inference. Causal inference also goes by the name inverse probability, and it’s also known as Bayes’ theorem. Three branches of science: one’s physics, one’s applied mathematics, and one’s social-psychological. That’s my tool kit, along with some other things.
Using those tools, it’s very easy to see the collapse coming for two reasons. One, in complex systems — and I would make the case that capital markets are complex systems nonpareil — the worst thing that you can have happen is an exponential function of scale. Meaning that as you scale up the system, you don’t increase the risk in a linear way, you increase it in an exponential way. To take a simple example, let’s say that J.P. Morgan tripled the gross notional value of its derivatives. You go to Jamie Dimon and say, “Okay, Mr. Dimon, you tripled your balance sheet. How much did the risk go up?” He would say: “Yeah, we tripled the balance sheet, but it’s long, short, long, short, long, short. The longs offset the shorts. You net it down, the actual risk is quite small relative to the gross notional value. That’s not the way to think about it. We tripled the balance sheet, but the risk went up a little bit.”
If you ask the everyday citizen, they would probably use intuition and say, “Well, if you tripled the balance sheet, sounds like you tripled the risk.” The correct answer is that Jamie Dimon’s wrong, and the everyday citizen using intuition is wrong. The correct answer is if you triple the scale of the system, you increase the risk by a factor of 10 or 100, some X-factor based on the slope of the curve, which is a power curve. It’s basically the degree, the distribution of severity and frequency of risk.
You go back to 2008: what did we hear about? Too big to fail, too big to fail, too big to fail. Today, the five largest banks in the United States are bigger than they were in 2008. They have a larger percentage of all the banking assets, and their derivative books are much bigger. Everything that was too big to fail in 2008 is much bigger today. Given the exponential function I just described, the risk is exponentially greater than 2008. Whatever you saw in 2008, get ready. A much bigger collapse is coming. Probably sooner than later.
I was general counsel of Long-Term Capital Management. I negotiated their bailout, so I had a front row seat for that. I was on the phone with the heads of the major banks. Jon Corzine at Goldman Sachs, Sandy Warner at J.P. Morgan, David Komansky at Merrill Lynch, Herb Allison and others, and Bill McDonough and Gary Gensler from the Treasury in Washington. I basically negotiated that bailout and saw exactly how close the global system came to complete collapse. We were hours away from shutting every stock and bond exchange in the world. Literally hours away, and the bailout got done. Four billion dollars changed hands. The balance sheet was supported. A press release was issued and the crisis passed. But it was extremely close, and not a foregone conclusion at all that we could get that done.
Having witnessed that, and knowing the team at LTCM — we had sixteen Ph.D.s from MIT, Harvard, Chicago, Stanford, and Yale, and two Nobel Prize winners — I said, well, if the smartest people in the world in this field with 160-plus IQ’s can get it that badly wrong, they must be missing something. There must be something wrong with the theory, because they’re not stupid. Nobody likes to lose their own money, so there must be something wrong with the theory.
I spent the next 10 years working on this, about five years figuring out what was wrong, where the flaws were, and another five years figuring out what actually works to remedy them. I refined my models enough that in 2005 and 2006 I was warning people that a collapse was coming again. That it would be worse.
Now, I didn’t say, Bear Stearns’ hedge fund is going to fail at the end of July 2007. I didn’t say that Lehman Brothers was going to collapse in mid-September 2008. It wasn’t necessary. It was sufficient to say that the collapse was coming because none of the lessons of 1998 had been learned. I’m in the same state today: the lessons of 2008 have not been learned. I’m watching the dynamics, I’m watching it play out, and you can see the next collapse coming. Here’s the tempo. In 1998, Wall Street bailed out a hedge fund to save the world. In 2008, central banks bailed out Wall Street to save the world. Move forward 10 years — let’s say 2018, to pick a number — and who’s going to bail out the central banks? Each bailout gets bigger than the one before. Each collapse is bigger than the one before, which is exactly what complexity theory would forecast based on the scaling metrics and the dynamics I described.
So who bails out the central banks? There’s only one clean balance sheet left in the world. There’s only one source of liquidity. After all, the Fed took their balance sheet from $800 billion approximately in 2008 to a little over $4 trillion dollars today. The problem is they haven’t normalized. Now that the crisis is long over, they haven’t gone back to the $800-billion-dollar level, which would be a more normal balance sheet. They stayed at $4 trillion; they’re stuck there. They’re not doing more QE, but they are rolling over what they have and they can’t reduce the size of the balance sheet.
What are they going to do in the next crisis? Go to $8 trillion? To $12 trillion? What is the outer boundary of how much money they can actually print? Legally, there is no boundary. They could actually print $12 trillion if they wanted to. At some point, however, you cross this intangible, invisible confidence boundary that I described earlier — and that goes back to the original problem of confidence in any form of money. And all the other central banks are in the same situation. The People’s Bank of China, the Bank of England, the Bank of Japan, the European Central Bank. It’s not unique to the U.S. If the central banks don’t have the wherewithal to liquefy the world in the next panic, and if the next panic is coming and you can see it a mile away, where will the liquidity come from? There’s only one source of liquidity left in the world, which is the IMF. They can print world money, which are the special drawing rights or SDRs.
When you get to the endgame, they’re going to have to print trillions of SDRs (each SDR is worth about $1.50) to re-liquefy the world in a global financial panic. Will that work? In theory it could work, but I expect if it works, it will only be because nobody understands it, like something’s happening and, as Bob Dylan sang in “Ballad of a Thin Man,” “Something’s happening here and you don’t know what it is.” At best, it will be highly inflationary.
Now, maybe it won’t work. Maybe people will say, “I’ve lost confidence in Federal Reserve money and European Central Bank money and Bank of Japan money. Why should I have any more confidence in IMF money? It’s just another form of currency, and I’ve lost confidence in all of them and I’m going to go get some gold.” If that actually happens, the world may have to go to a gold standard. Now, I guarantee there’s not a central bank in the world that wants a gold standard. They may have to go to one, not because they want to, but because they have no choice, because it’s the only way to restore confidence. That raises an interesting question: if you go to a gold standard, what’s the price of gold? I talk about this in The New Case for Gold, about the blunder of 1925 with Churchill taking the U.K. back to a gold standard at a price that Keynes warned him was deflationary. Keynes didn’t favor a gold standard at the time. He did in 1944 and he did in 1914. He didn’t in 1925, but he did tell Churchill if you’re going to do this, you need a much higher price to avoid deflation. Churchill ignored him and threw the U.K. into a depression.
So question is: what is the implied non-deflationary price of gold today? I’ll use M1 of China, U.S., and the ECB, just as a frame. The answer’s $10,000 now if you have 40 percent backing — and over $50,000 if you have 100 percent backing of M2, which is a broader money supply. I don’t think you have to use M2. I don’t think you need 100 percent. It’s a judgement that’s debatable. But even on the modest assumptions of M1 using 40 percent backing, gold would have to be $10,000 an ounce to support the money supply. You may or may not have a gold standard, but if you do gold will be $10,000 an ounce.
Now, if you don’t, if the SDR thing actually works, gold will get to $10,000 an ounce the other way, which is inflation. Gold is going to shoot much higher. In the SDR scenario it will shoot much higher because of inflation, and in the gold-standard scenario it will shoot much higher because it has to, to avoid deflation. It’s not really the price of gold going up, it’s the devaluation of the paper currency. It’s the same thing. The dollar price of gold is just the inverse of the value of the dollar.
OR: Why do you say gold has to be part of a new system? What do you say to the people who think it’s an anachronism?
Rickards: The flaw in that — and I think this is one of the biggest problems today, and certainly an issue with everyone from Milton Friedman to Janet Yellen — is that they take confidence for granted. If you assume that confidence in paper money is infinitely elastic, then there’s no reason why the money supply cannot be infinitely elastic.
There are a lot of gold bashers out there who will be very quick to tell you that gold’s a barbarous relic, blah blah blah. But there are some more thoughtful people out there, among whom I would include Stephanie Kelton, Warren Mosler, Richard Duncan, and others. They’re all different, but they’re smart people. I’ve met a lot of them. Paul McCulley, formerly of PIMCO and a close associate of Bill Gross. They call themselves modern monetary theorists.
I think Adair Turner is coming out this way in his new book, Between Debt and the Devil, and even Larry Summers in some ways. What they’re saying is that there’s nothing you cannot print yourself out of. You get too much deflation? Print more money. Deflation won’t go away? Print more. People won’t spend? The government can spend. Maybe you cannot force people to spend it, but the government loves to spend money, they know how to do that really well. If it increases the deficit, so what? Just issue more debt to cover the deficit, and if people don’t want your debt, the central bank can buy it. Don’t worry about paying it back because the central bank can convert the treasury bonds into perpetual bonds. The whole thing just goes away. You don’t have to worry about the national debt — the Fed can just buy the whole $19 trillion of it, sock it away on their balance sheet, make it a perpetual note, and go play golf. What’s the problem?
This is sometimes called “helicopter money.” It’s called “people’s QE” by Jeremy Corbyn, it’s called “fiscal dominance” by Rick Mishkin. It has different names, but it always says the same thing: there’s no outer boundary on how much money you can print, so what’s the problem?
My thesis — and here I’ll flip over to the behavioral-psychology side a little bit — is that it’s not a problem until it is. In other words, confidence can be sustained until it can’t. You can lose this very quickly, so I don’t believe that confidence is infinitely elastic. There’s nothing in human nature or history that says that. If you’re relying on confidence to say that money can be infinitely elastic, then you’re wrong. The concern is that the elites will go down this road — having been wrong about the wealth effect, about QE1, QE2, and QE3, about Operation Twist — and then they’ll somehow wake up and see they’re wrong again. But they’ll find out the hard way because confidence in the entire system will collapse. At which point, your only two remedies are SDRs and gold.
OR: What’s your take on central banks and gold?
Rickards: With regard to central banks and gold, I always say watch what they do, not what they say. If the U.S. has a budget problem, and we’re sitting on about $380 billion in gold, why don’t you just sell the gold and get some money? That’s what Canada did. That’s what the U.K. did. Why are we hanging onto it?
The question answers itself. Obviously, it has some value. Obviously, it has some role in the monetary system. In my book, I write about a discovery I made — one of those discoveries that’s hiding in plain sight. I had been working on a thesis that the Fed is, at least on occasion, insolvent. My basis for that was to look at the Fed’s balance sheet. They’re leveraged today about 113 to one. That’s unheard of. I’ve been in the hedge-fund business, I’ve been in the investment-banking business, I’ve been in the banking business for decades. Banks leverage maybe 12 to one, broker-dealers and investment banks leverage maybe 15 to one, a hedge fund will lever two or three to one (although that’s getting a little risky). Even Long-Term Capital Management was never leveraged more than 20 to one, and we were very aggressive about leverage. 113 to one is way, way off the charts.
Now, just to be clear, the Fed does not mark its value sheet to market. My thought experiment is: what if they did? There’s a lot of data out there about the composition of the bond holdings of the Fed, particularly those held at the New York Fed. It’s not difficult to get that information, to do some bond math, and mark it to market. Doing that, I discovered that they were in fact insolvent at various times along the way. I had this conversation with several Fed officials. One member of the board of governors, another individual who was not a member of the board, but a very, very close advisor to Bernanke and Yellen, a true insider, a Ph.D. economist, a friend of mine. I was able to have this conversation with him. Interestingly, the member of the board of governors more or less conceded my point, but her rejoinder was, “Well, maybe we’re insolvent, but it doesn’t matter.” In other words, central banks don’t need capital. That’s a point of view.
The other conversation was with the insider: he was adamant that they’ve never been insolvent, ever. Regardless of bond-market moves. He wouldn’t tell me why, so I got to thinking about it. I went back to the balance sheet to see what I was missing. And lo and behold, there was this gold item valued at $42 an ounce. I said, “Well I should mark that to market. If I’m going to mark the bonds to market, I need to mark the gold to market.”
Now, as I was doing this I noticed a couple things. The Fed’s gold holdings are approximately 8,000 tons exactly. Close to exactly the amount held by the U.S. Treasury. In intelligence work, the first rule is there are no coincidences, and this non-coincidence explains why the Treasury stopped selling gold in 1980. Bven as late as the late 70’s, the Treasury was still dumping gold to suppress the price. That’s not speculation; there’s declassified correspondence among President Ford, Henry Kissinger, Arthur Burns, and the Chancellor of Germany that lays this out. The Treasury was actually dumping thousands of tons of gold in the late 1970s, but then in 1980 it just stopped on a dime. The U.S. has sold almost no gold since. Instead, we got everyone else to dump their gold. We got the U.K. to dump six hundred tons in the beginning of 1999. We got Switzerland to dump over a thousand tons in the early 2000s. We got the IMF to dump four hundred tons in 2010. The U.S. has been prevailing upon all these other people to sell their gold, but we won’t sell any ourselves. Why? They can’t. The Treasury has to hold the gold they’ve got in order to honor, on legal and constitutional grounds, the certificate held by the Fed. This was received in exchange for the gold (with an explicit guarantee that the gold was there to backstop the Fed’s balance sheet).
So I was wrong the first time. The Fed has never been insolvent; my insider friend was correct. The reason I was wrong was not because of the bond portfolio, which would have made them insolvent, but because of the gold, which adds about $350 billion to the balance sheet. When you add that to capital on a mark-to-market basis, the leverage ratio drops from 113 to one to about 13 to one, which is pretty healthy for a normal bank. On top of everything else we’re discussing, you find that the Federal Reserve has a hidden asset, which is the value of gold, and that it’s well capitalized — if you count the gold. What does it mean when central bankers and public officials disparage gold, tell you it’s an anachronism, tell you it’s a tradition, tell you it’s a barbarous relic, tell you that you’re a fool to own it — and yet they themselves are propped up and made solvent by gold?
OR: More and more physical gold is leaving the tradable system as China and Russia stockpile it, yet huge derivatives are still being written on it. Can you talk about that disconnect?
Rickards: Well, there is a world of paper gold and there’s a world of physical gold. Now, paper gold to me is not paper gold. It’s paper, but it references the price of gold. There’s not going to be any actual large difference between the paper price and the physical price quoted whether it’s in London or Beijing, because of the arbitrages.
I just recently returned from Switzerland where I met with the head of the country’s biggest gold refinery, who told me that he’s seeing severe shortages in supply. This guy, he knows who all the big sellers are, he knows who all the big buyers are because he’s the biggest gold refiner and he takes it in and ships it out. He knows who all the players are and this is, again, in the physical world. He said that with regard to his selling side, he has more demand than he can handle. He’s sending the Chinese 10 tons a week; they want 20 tons. He won’t provide it because he doesn’t have that much gold and he has other customers to take care of.
The physical shortages are already showing up and they’re getting worse. I’ve heard similar things from wholesale dealers — people who deal directly with London Bullion Market Association members and Comex-approved warehouses. These are the large holders of gold in the world, and they are saying that it is taking longer and longer to fill deliveries. The supply situation is stretched and probably about to break.
Meanwhile, the paper gold market continues to expand with 100-to-one leverage. Warehouses continue to get drawn down, contracts continue to be written. You have a very, very large inverted pyramid, with a broad base of paper gold on top and a tiny sliver of physical gold supporting the whole thing. It’s becoming wobbly, and it’s about to tip over.
Any break in that market — coming back to the issue of confidence — would lead directly to what I would call the mother of all short squeezes and a buying panic. What would I mean by a break? Well, most likely a failure to deliver. Suppose some dealer, some large bank, some exchange, some intermediary somewhere has sold a lot of paper gold and has been called upon by the buyers to deliver. They say, “I don’t want to roll over my contract, I don’t want cash settlement, I want the gold, please. Give me the gold.”
They’re not going to be able to get it. That failure will become public, because it always does, and will create a crisis of confidence. Everyone will run down to their dealers, their exchanges, and their brokers all at once and say, “Give me my gold!” They’ll then discover that there’s only about one percent of what’s needed to fulfill that demand, and there’s nowhere near enough gold in the world at anything close to today’s prices (even if you could find it, which you probably will not be able to) to satisfy those contracts.
What would happen next? The answer is that, since you cannot deliver the gold, you’re going to have to terminate the contract, and it will come as a surprise to a lot of paper gold buyers that such terminations are totally legal. If you actually read the contracts gold buyers sign you’ll find what are called force majeure clauses or material adverse change clauses, meaning gold exchanges have the power to suspend delivery. There’s also what’s called trading for liquidation only, which means you can roll over your contract or close it out, but you cannot take delivery. They have emergency powers to do, really, whatever they want to maintain orderly markets. So what they’ll do is they’ll terminate all these contracts using these contractual and governance provisions. They won’t steal your money, they’ll send you a cash settlement for yesterday’s price. But meanwhile the price of gold today will be going up $200, $300, $400, $500 an ounce. Day after day you’ll be sitting there, watching the exact hyperbolic price movements that were the reason you bought the gold in the first place — and you will not be participating in them.
You will be closed out at exactly the time when you most want the contract. That always happens. That’s the conditional correlation effect. The time you most want it is the time you won’t have it, because it doesn’t work for the other guy. They close you out, send you a check for yesterday’s price, and you’ll miss the move. And by the way, even if you want to jump back in, you won’t be able to buy any. Dealers will be sold out, mints will be backlogged, refiners will be backlogged. They won’t even take your calls. That’s what my friend in Switzerland told me. He said if I didn’t know you and you weren’t already a customer, I wouldn’t take your call. I’m not taking any new business because I cannot supply it.
OR: Can you talk about the so-called war on cash and the potential confiscation of gold?
Rickards: The war on cash is over. The government won. We hear about the cashless society and I think Sweden may be the first to get there. Others are considering it. Larry Summers writes an op-ed on abolishing the 100-dollar bill, and there’s a movement in Europe to get rid of the 500-euro note, so there are a lot of significant legal and political trends against cash. It’s really irrelevant, because we don’t use cash anywhere. You might have a few bucks in your wallet, but people get their paychecks from direct deposit, they get their retirement checks from direct deposit, they pay their bills online, they use their credit cards, they use their debit cards, and there hasn’t been a paper Treasury security issue, I think, since the late 1970’s. The dollar is already a digital currency, and so are all the other major currencies.
To the extent we have any paper money at all, it’s a token. To the point where you buy a two-dollar candy bar, you don’t even reach in your pocket and get out a five, you just swipe your debit card. We already live in a world of digital currencies, with respect to the dollar, the major currencies.
People say, “Yeah, but it’s still legal. I can go down to the bank and get $10,000 or $20,000 and stick it in a safe to avoid negative interest rates or have it for an emergency.” They’re wrong. It’s not that easy. If you go actually do it, actually go down to the bank and ask them for $15,000 or $20,000, you will be treated like a drug dealer or a tax evader. Some banks will tell you to come back in a couple days, that they have to order the cash. There’ll be reams of paperwork to fill out. They’ll file a report with the Treasury.
People are kidding themselves about the ease with which they can get cash. They are locked into a digital system. The war on cash is over and the government won. That’s just the prelude to negative interest rates. It’s like slaughtering pigs: you don’t chase the pigs around a field. You get them into a pen and then you slaughter them. What’s happening with savers is that everyone’s being rounded up into one of four or five digital pens, i.e. Citi and J.P. Morgan and Wells Fargo, and they’re going to be led to the slaughterhouse of negative interest rates.
OR: Do you think we are seeing a currency war going on internationally at the moment?
Rickards: About currency wars, let me say I’m always amused when I see a journalist or someone write a story saying “Oh gee, there’s a currency war. Look at this. China’s weakening against the yen.” I make the point that the most recent currency war started in 2010. I talk about it in my book on the subject which came out in 2011. It’s the same currency war. Wars consist of many battles, wars are not continuous fighting all the time. There are big battles and little battles; there are quiet periods and then a new battle erupts. You have an occasional D-Day or Battle of the Bulge, but some episodes are more intense than others.
Currency wars are the same. There are quiet periods, but it’s the same war. What I call Currency War One lasted from 1921 to 1936. What I call Currency War Two lasted from 1967 to 1987. I make the point that the world is not always in a currency war, but when we are they can go on for a very long time because they have no logical conclusion. It’s just back and forth, with a race to the bottom via competitive devaluations. The only conclusion to a currency war is either systemic reform or systemic collapse. Either the system breaks down completely or people get together, as they did at the Plaza Hotel in 1985, to give the system some coherence.
I don’t see the leadership, I don’t see the giants today. I don’t see people like James Baker, Bob Rubin, George Schultz, or John Maynard Keynes. I don’t see people like that on the landscape. I see a lot of people not of that stature in positions of power. I don’t see any awareness that this collapse is coming. So given the two possible outcomes — systemic reform or systemic collapse — I think systemic collapse is the more likely. But we are in a currency war and have been since 2010. We will be perhaps until 2025. Unless the system collapses earlier, which is what I expect.
James Rickards is an investor and the bestselling author of The New Case for Gold, Currency Wars, and The Death of Money.
President Richard Nixon’s actions in 1971 to end dollar convertibility to gold and implement wage/price controls were intended to address the international dilemma of a looming gold run and the domestic problem of inflation. The new economic policy marked the beginning of the end of the Bretton Woods international monetary system and temporarily halted inflation.
The international monetary system after World War II was dubbed the Bretton Woods system after the meeting of forty-four countries in Bretton Woods, New Hampshire, in 1944. The countries agreed to keep their currencies fixed (but adjustable in exceptional situations) to the dollar, and the dollar was fixed to gold. Since 1958, when the Bretton Woods system became operational, countries settled their international balances in dollars, and US dollars were convertible to gold at a fixed exchange rate of $35 an ounce. The United States had the responsibility of keeping the dollar price of gold fixed and had to adjust the supply of dollars to maintain confidence in future gold convertibility.
Initially, the Bretton Woods system operated as planned. Japan and Europe were still rebuilding their postwar economies and demand for US goods and services—and dollars—was high. Since the United States held about three-quarters of the world’s official gold reserves, the system seemed secure.
In the 1960s, European and Japanese exports became more competitive with US exports. The US share of world output decreased and so did the need for dollars, making converting those dollars to gold more desirable. The deteriorating US balance of payments, combined with military spending and foreign aid, resulted in a large supply of dollars around the world. Meanwhile, the gold supply had increased only marginally. Eventually, there were more foreign-held dollars than the United States had gold. The country was vulnerable to a run on gold and there was a loss of confidence in the US government’s ability to meet its obligations, thereby threatening both the dollar’s position as reserve currency and the overall Bretton Woods system.
Many efforts were made to adjust the US balance of payments and to uphold the Bretton Woods system, both domestically and internationally. These were meant to be “quick fixes” until the balance of payments could readjust, but they proved to be postponing the inevitable.
In March 1961, the US Treasury’s Exchange Stabilization Fund (ESF), with the Federal Reserve Bank of New York acting as its agent, began to intervene in the foreign-exchange market for the first time since World War II. The ESF buys and sells foreign exchange currency to stabilize conditions in the exchange rate market. While the interventions were successful for a time, the Treasury’s lack of resources limited its ability to mount broad dollar defense.
From 1962 until the closing of the US gold window in August 1971, the Federal Reserve relied on “currency swaps” as its key mechanism for temporarily defending the US gold stock. The Federal Reserve structured the reciprocal currency arrangements, or swap lines, by providing foreign central banks cover for unwanted dollar reserves, limiting the conversion of dollars to gold.
In March 1962, the Federal Reserve established its first swap line with the Bank of France and by the end of that year lines had been set up with nine central banks (Austria, Belgium, England, France, Germany, Italy, the Netherlands, Switzerland, and Canada). Altogether, the lines provided up to $900 million equivalent in foreign exchange. What started as a small, short-term credit facility grew to be a large, intermediate-term facility until the US gold window closed in August 1971. The growth and need for the swap lines signaled that they were not just a temporary fix, but a sign of a fundamental problem in the monetary system.
International efforts were also made to stem a run on gold. A run in the London gold market sent the price to $40 an ounce on October 20, 1960, exacerbating the threat to the system. In response, the London Gold Pool was formed on November 1, 1961. The pool consisted of a group of eight central banks (Great Britain, West Germany, Switzerland, the Netherlands, Belgium, Italy, France, and the United States). In order to keep the price of gold at $35 an ounce, the group agreed to pool gold reserves to intervene in the London gold market in order to maintain the Bretton Woods system. The pool was successful for six years until another gold crisis ensued. The British pound sterling devalued and another run on gold occurred, and France withdrew from the pool. The pool collapsed in March 1968.
At that time the seven remaining members of the London Gold Pool (Great Britain, West Germany, Switzerland, the Netherlands, Belgium, Italy, and the United States) agreed to formulate a two-tiered system. The central banks agreed to use their gold only in settling international debts and to not sell monetary gold on the private market. The two-tier system was in place until the US gold window closed in 1971.
These efforts of the global financial community proved to be temporary fixes to a broader structural problem with the Bretton Woods system. The structural problem, which has been called the “Triffin dilemma,” occurs when a country issues a global reserve currency (in this case, the United States) because of its global importance as a medium of exchange. The stability of that currency, however, comes into question when the country is persistently running current account deficits to fulfill that supply. As the current account deficits accumulate, the reserve currency becomes less desirable and its position as a reserve currency is threatened.
While the United States was in the midst of the Triffin dilemma, it was also facing a growing problem of inflation at home. The period that became known as the Great Inflation had started and policymakers had put anti-inflation policies in place, but they were short lived and ineffective. At first, both the Nixon administration and the Federal Reserve believed in a gradual approach, slowly lowering inflation with a minimum increase in unemployment. They would tolerate an unemployment rate of up to 4.5 percent, but by the end of the 1969-70 recession the unemployment rate had climbed to 6 percent, and inflation, as measured by the consumer price index, was 5.4 percent.
When Arthur Burns became chairman of the Board of Governors in 1970, he was faced with both slow growth and inflation, or stagflation. Burns believed that tightening monetary policy and the increase in unemployment that accompanied it would be ineffective against the inflation then occurring, because it stemmed from forces beyond the control of the Fed, such as labor unions, food and energy shortages, and OPEC’s control of oil prices. Moreover, many economists in the administration and at the Fed, including Burns, shared the view that inflation could not be reduced with an acceptable unemployment rate. According to economist Allan Meltzer, Andrew Brimmer, a Fed Board member from 1966 to 1974, noted at that time that employment was the principal goal and fighting inflation was the second priority. The Federal Open Market Committee implemented an expansionary monetary policy.
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Eichengreen, Barry. Exorbitant Privilege: The Rise and Fall of the Dollar and the Future of the International Monetary System. New York: Oxford University Press, 2011.
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Eichengreen, Barry. “Epilogue: Three Perspectives on the Bretton Woods System.” In A Retrospective on the Bretton Woods System: Lessons for International Monetary Reform, edited by Michael Bordo and Barry Eichengreen, 621-58, Chicago: University of Chicago Press, 1993.
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Meltzer, Allan H., “Origins of the Great Inflation,” Federal Reserve Bank of St. Louis Review 87, no. 2, part 2 (March/April 2005): 145–75.
Meltzer, Allan H., “U.S. Policy in the Bretton Woods Era,” Federal Reserve Bank of St. Louis Review 73, no. 3 (May/June 1991): 53–83.
U.S. Department of State Office of the Historian. “The Bretton Woods Conference 1944.” Accessed on October 22, 2013.
Romer, Christina, “Commentary on Meltzer’s Origins of the Great Inflation,” Federal Reserve Bank of St. Louis Review 87, no. 2, part 2, (March/April 2005): 177-85.
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Federal Reserve Chair Jerome Powell is grilled by Representative Katie Porter, a California Democrat, during his semi-annual congressional testimony Tuesday for his attendance at a party thrown by Amazon.com Inc. Chief Executive Officer Jeff Bezos last month.
<iframe width=”560″ height=”315″ src=”https://www.youtube.com/embed/8pS1edpeGqI” frameborder=”0″ allow=”accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture” allowfullscreen></iframe>Jesse Ventura visited Google’s Santa Monica office on April 13, 2011 to discuss his new bestseller: “63 Documents the Government Doesn’t Want You to Read.”