The recent weakness in emerging market currencies, and implementation of the taper, are sure to be topics of discussion at the G-20 meetings in Australia. While the imminent retrenchment in quantitative/credit easing is responsible for some of the currency movements of late, I’m not sure this is the only way to look at recent events; nor do I think we need see a replay of previous episodes of currency crises in response to US monetary tightening.
Figure 1: Log exchange rate against USD of Argentina Peso (ARS, blue), Brazilian Reals (BRL, red), Chilean Peso (CLP, green), Indonesia rupiah (IDR, black), Indian rupee (INR, teal), Russian ruble (RUB, purple), South African rand (ZAR, olive green), Thai baht (THB, dark blue), and Turkish lira (TRY, pink), all expressed against the USD, 2013M05=0. February observation pertains to 18 February. A decline indicates a weakening of the currency. Source: IMF International Financial Statistics, Federal Reserve Board via FRED, and Pacific Exchange Services, and author’s calculations.
Interpreting EM Currency Movements
For those of us who believed unconventional monetary policy (quantitative and credit easing, as well as forward guidance) had an impact on cross-border asset prices, including exchange rates (see this this post and this BIS discussion paper) it was has been no surprise that exchange rates should move in response to talk of reducing the amount of monetary stimulus.
While forward guidance has been consistent in its phrasing, apparently recent discussion of more durable rapid growth in the US has meant a movement forward in the market-predicted raising of the Fed funds rate:
Notice the precipitous decline in the median time of exit from the ZLB between September and December of 2013. The recently released Fed minutes gives further weight to this view. 
Financial Stress and Idiosyncratic Factors
That being said, it’s clear from the heterogeneity in responses – Argentina has suffered far more than Thailand, for instance – it’s clear that domestic factors are very important. (For the debate over the importance of local factors, see this presentation).
Nonetheless, thinking back to a previous episode of monetary tightening, in 1994, gives pause for thought.
Figure 2: US Federal funds rate (blue), and ten year constant maturity yields (red). NBER recession dates shaded gray. Vertical dashed lines at dates of currency crises in Mexico, Thailand and Korea. Source: FRED and NBER.
A casual inspection of the timing of the interest rate hikes — at least in the 1990′s — and subsequent currency crises would suggest a pretty straight line cause-and-effect. However, casting a broader net over the data yields a slightly different story. From Hooper, Luzzetti, and Slok, “Fed taper and EM: Investors more selective,” Global Economic Perspectives (Deutsche Bank, 20 Feb 2014):
from an historical perspective, EM capital flows have not been highly correlated with shifts in Fed policy. For example, since 1990, the correlation between the fed funds rate and private capital flows to EM as a percent of EM GDP is slightly positive (7%) – the opposite sign from what would be expected (Chart 11). Looking further back to 1980, this correlation takes the correct sign, but remains relatively low (-28%). Instead, EM capital flows have been more highly correlated with uncertainty and growth prospects than with the fed funds rate (Table 3). In particular, since 1990, capital flows to EMs have been highly correlated with the VIX (-62%) and EM growth (47%).
This finding is reminiscent of recent findings by the IMF in its External Balance Assessment (EBA) approach; a volatility index similar to the VIX accounts for a component of current account balances (see discussion here and IMF Working Paper). [Disclaimer: I provided input into portions of this project]
These findings suggest to me that US monetary policy is only part of the story. Reduced pressure on EM currencies depends on minimizing financial stress in the core advanced economies, and the vagaries of individual EM country growth prospects.
That being said, EM policymakers will be confronted by difficult choices – whether to allow depreciation or use the interest rate defense to respond to capital outflows (caused by either higher foreign returns or financial stress). Which one is better depends on several factors, including how much foreign currency denominated debt has been accumulated as currencies have been stabilized (see the Economist’s take, using the Aizenman-Chinn-Ito exchange rate stability indices, here), versus the interest sensitivity of aggregate demand.
What are the prospects emerging market economies as US financial markets and monetary policy normalize? The IMF has just released an assessment in anticipation of the G-20 meetings.
Global activity has picked up, largely on account of advanced economies. Growth firmed in 2013H2, driven largely by stronger outturns in advanced economies as final demand expanded broadly as expected. In many emerging markets, despite a boost to output from stronger exports, domestic demand has been weaker than expected, reflecting in part tighter financial conditions.
A new bout of financial volatility has affected emerging market economies as markets reassess their fundamentals. While the pressures were relatively broad-based, emerging economies with relatively high inflation and high current account deficits saw the largest asset price declines initially. Markets are showing signs of stabilizing recently, although they are still fragile, on the back of actions by key emerging economies to shore up confidence and strengthen their policy commitments. This episode, however, underscores vulnerabilities and the challenging environment for many emerging economies. The rapid jump in global risk aversion had also driven down advanced economy equity prices.
However, the recovery is still weak and significant downside risks remain. Capital outflows, higher interest rates, and sharp currency depreciation in emerging economies remain a key concern and a persistent tightening of financial conditions could undercut investment and growth in some countries given corporate vulnerabilities. A new risk stems from very low inflation in the euro area, where long-term inflation expectations might drift down, raising deflation risks in the event of a serious adverse shock to activity.
One bit of good news is that short term debt-to-reserves ratios is low for many EM countries.
Source: Hooper, Luzzetti, and Slok, “Fed taper and EM: Investors more selective,” Global Economic Perspectives (Deutsche Bank, 20 Feb 2014) [not online]
As discussed in this post, a higher reserves-to-short term debt means a greater resilience to a negative global economic environment. This bodes well for many countries, although several countries are in a more challenging situation than in 1997 – India and Ukraine for instance (Turkey experienced its currency crisis in 2000-01). (Recent developments in selected EM countries discussed in this post.)
For more on recent developments in emerging markets, see here, here, World Bank via Econbrowser, Barry Eichengreen, Helmut Reisen, and WSJ RTE; Economist and Capital Ebbs and Flows on international macro cooperation.
JUST BECAUSE A MODEL DESCRIBES THE EXISTING DATA DOESN’T MEAN THAT IT WILL DESCRIBE DATA THAT HAS NOT BEEN OBSERVED
So far we’re in agreement; in fact I’m going to repeat this point to my econometrics class. He then continues:
You see, in science, you don’t prove the theory by showing that it describes the existing observations. You prove the theory by showing that it predicts data that haven’t been observed.
Well, gee, if this is the standard for proving or disproving hypotheses, either generally, or in econometrics, we’re not going to get very far. In this view, I won’t see our sun go nova, so might as well call it a day — science can’t proceed until we get the data! But this is the sort of nihilistic worldview that pervades the global climate change deniers.
For a more succinct critique, see below:
In my ten years living in Madison, this has been the coldest Winter thus far. Keeping in mind everything is probabilistic, it’s likely that I have anthropogenic climate change to thank for experiencing this event.    (Just like one can’t say Hurricane Sandy was directly a result of global climate change, the likelihood of such events rises with global climate change.)
Tabulating Climate Change
From NASA via RealClimate comes this graph of annual temperatures taking into account El Nino and La Nina phenomena:
Figure 1: The GISS data, with El Niño and La Niña conditions highlighted. Neutral years like 2013 are gray. Source: NASA.
This figure illustrates that while temperatures vary with the El Nino and La Nina phenomena, allowing for a mean shift, one sees a clear pattern toward overall warming temperatures (notice no hiatus once looking at the data in this fashion). Now, while the annual averages are rising, global climate change models imply greater climate variation as well. (I’ve discussed the greater dispersion in temperatures in summer months here, that is spread as well as mean change.) NOAA has generated indices to measure climate extremes; components are reported here.
Figure 2: U.S. Climate Extremes Index (CEI). Source: NOAA, accessed February 16, 2014.
The data indicates that extreme high/low temperatures and extreme precipitation are rising in frequency.
Economic Implications, Again
I was thinking (again) about the economic impact these extremes as I was contemplating Representative Marsha Blackburn’s twofold (and seemingly internally inconsistent) assertion today that there was no consensus on anthropogenic climate change, and even if there were, we should think about all the positives that would come about from an upward shift in the mean and increase in support of the distribution of temperatures in the United States (maybe there’s an upside to West Nile fever! ).
Here are the immediate impacts (Informal poll: How many people had trips cancelled because of the weather this Winter? I’m the first vote yes).
Figure 3: Flight cancellations in the United States. Source: NY Times.
I wonder if any of those who argue better to adjust than to try to price carbon and were caught in those flight cancellations reconsidered there positions. That’s only partly facetious; I think climate change is going to impose substantial costs going forward, a lot more than just damage to sewer pipes and salt bills. 
These costs include power outages, estimated by CEA/DoE.
The Consensus on Anthropogenic Climate Change
As I mentioned earlier, Representative Blackburn argued that there was no consensus on the sources of climate change. I beg to differ — as do scientists themselves.
Here are the key graphs from “Expert credibility in climate change,” Proceedings of the National Academy of Sciences (2010). Note that UE denotes unconvinced; CE denotes convinced (by the thesis of anthropogenic climate change).
In other words, the climate scientists that are better published tend to be convinced of anthropogenic climate change; moreover, the ones that are better cited also tend to be more convinced of ACC. More in this post.
Another study, with similar results, from Eos (The Transactions of the American Geophysical Union) is here. The climatologists publishing on climate change tend to be the most convinced of anthropogenic climate change (97.4%, which in my book is pretty overwhelming).
A Time Series Analysis
Since Econbrowser has a large audience of people interested in economics, I thought it useful to post estimates of the human-activity-related component of global climate change (on average, warming), from a well-known econometrician. From Kaufmann, Kauppi, and Stock, “Emissions, Concentrations, and Temperature: A Time Series Analysis,” Climatic Change (2006):
Figure from Kaufmann, Kauppi, and Stock, “Emissions, Concentrations, and Temperature: A Time Series Analysis,” Climatic Change (2006).
The graph can be read as follows: Solid gray line is actual, gray dot-dash-dot line (the one plunging) is the component of temperatures due to natural factors, gray dash line is fitted values, and the black dotted line is the component due to human activity. The predicted values are generated from a fairly simple four equation simultaneous equations model, so economists can understand the approach.
See also Reconciling anthropogenic climate change with
observed temperature 1998–2008, PNAS (2011) (added 2/21, 3:30PM)
For those who might not be aware, James Stock is well known econometrician, who has contributed to the unit root testing, cointegration and macroeconometrics literature. (He’s ranked 32 at IDEAS, if you were doubting his credentials.)
Returning to Representative Blackburn who plea for a cost-benefit analysis, I turn to a real expert on the subject, William Nordhaus. I am not sure what she means by cost-benefit is what Profsesor Nordhaus means.
It was five years ago that the ARRA was passed…and thence arose a fierce storm of criticisms, ranging from the idea that the stimulus would occur after the recovery was complete (e.g., Ed Lazear), to the Treasury view (government spending would crowd out completely private spending, e.g., Eugene Fama). Time to take stock. The Council of Economic Advisers has released its last report on the ARRA, and other stimulus measures, discussed in a blogpost by CEA Chair Jason Furman.
First, the estimated impact on GDP of the ARRA and other fiscal measures, relative to baseline:
The concept of “relative to baseline” is one of the most mis-understood and disparaged concepts, usually by people without an economics background. I am thankful that at least professional economists who were critics of the ARRA (e.g., John Taylor) understood and used the concept of the counterfactual.
The estimated impact on GDP from the CEA, and the high-low range from the Congressional Budget Office, is shown in Figure 5 from the report.
It is of note that professional/business sector economists, who do not have a political axe to grind, and the CEA have a similar perspective on the impact of the ARRA..
See also the views from the bloggers in the Kaufmann survey and business economists in the WSJ survey. All the respondents might not have believed the stimulus package was a good idea, but almost all agree there was a positive impact on GDP and employment from the ARRA.
Parting graph, for the disbelievers in models and counterfactuals: Figure 3, shows GDP growth (in log terms, q/q SAAR) and stimulus as a share of GDP.
Figure 3: Quarter on quarter growth rate of real GDP, annualized (blue line, left scale) and sum of total receipt effects (reversed) and total expenditure effects from the ARRA, as a share of nominal GDP, 2008Q2-2011Q2. NBER defined recession dates shaded gray. Growth rate calculated as log first difference. Source: BEA via FRED and BEA 2013Q3 3rd release, NBER, and author’s calculations.
More discussion of fiscal policy and multipliers here.
Bits and bytes can be stolen just like the cash under your mattress.
The high-flying bitcoin digital currency took a big hit when MtGox, once the world’s largest bitcoin exchange, suspended withdrawals until it can resolve a problem with what it calls transaction malleability:
A bug in the bitcoin software makes it possible for someone to use the Bitcoin network to alter transaction details to make it seem like a sending of bitcoins to a bitcoin wallet did not occur when in fact it did occur. Since the transaction appears as if it has not proceeded correctly, the bitcoins may be resent. MtGox is working with the Bitcoin core development team and others to mitigate this issue.
Tyler Shibata attributes last week’s losses at Silk Road to the same software flaw:
The Bitcoin community suffered another shock on Thursday morning when it was revealed that the Silk Road 2.0 had been hacked, and that all 4,474 Bitcoins– roughly valued $2.7 Million at the time of the attack– had been stolen. This heist, as some people have been calling it, was caused by a flaw in the Bitcoin protocol itself called “Transaction Malleability.”
Gavin Andresen, chief scientist at the Bitcoin Foundation– which oversees and develops the Bitcoin software– denied the problem was its fault.
“The issues that MtGox has been experiencing are due to an unfortunate interaction between MtGox’s highly customised wallet software, their customer support procedures, and an obscure (but long-known) quirk in the way transactions are identified and not due to a flaw in the Bitcoin protocol,” he told the BBC.
The value of bitcoin has fallen to half its December peak on the news. But nobody’s giving them away– one bitcoin will still cost you $560 at the current “depressed price”. And bitcoin proponents like Timothy Lee are not deterred:
And this is one of Bitcoin’s great strengths. Right now, companies such as Mt. Gox, BitStamp, BitPay and Coinbase are important players in the Bitcoin ecosystem. But Bitcoin itself is an open-source technology platform. It’s not owned by anyone, and its success doesn’t depend on the success of any specific bitcoin-based company. If the current crop of Bitcoin businesses fail, a new generation can and likely will emerge to take their place.
In case you hadn’t noticed, we’re also learning more about the vulnerabilities of more conventional digital transactions. The most dramatic recent development on this front was the December theft of credit account information for 70 million customers of Target. Bloomberg reported last month that this may be showing up in the retailer’s bottom line:
Target is already suffering from the hacking of its customer data. Sales at its U.S. unit were “meaningfully weaker” after the data theft was disclosed, the company said. U.S. same-store sales will fall about 2.5 percent in the quarter through January, compared with an earlier projection they would be little changed. Adjusted earnings per share will be $1.20 to $1.30 for the division, down from a previous estimate of at least $1.50.
Bob Eisenbeis worries that vulnerabilities in our system for conventional credit card and debit card payments could end up causing bigger problems:
The points of vulnerability are many, especially since many institutions have outsourced the actual processing and warehousing of data, and this trend is accelerating as more and more businesses move their computing into the cloud….
The overarching issues concern threats to the payment system itself and the risks that breached information will be used to commit wholesale electronic theft that might threaten the solvency of a major financial institution, be it a bank, investment bank, insurance company, etc. Additionally, such insolvency could have systemic implications for the financial system as a whole. The systemic risks are further amplified by the complex interrelationships among traditional business firms, operators of the private-sector payments-transfer infrastructure, and financial firms. A hack of customer data held by a nonfinancial firm or payments processor could result in losses that can quickly bleed over into the financial system if data are compromised and transactions are initiated and consummated before the breach is discovered or reported.
Is that overstating the concerns? Maybe so. But I do believe that it’s easy to get lulled into complacent confidence in our payment systems given that technology, both in the hands of the good guys and the bad guys, is changing so quickly.
Ed Hanson argues that 2013 Gross State Product (GSP) data for 2013 will show a Wisconsin renaissance:
I am looking forward to each release of data for each state. I note that preliminary data both Minnesota and Wisconsin snapped up in new employment in December, Wisconsin more so. State GDP release is months away but will be relevant to this discussion. It is an unfortunate but reality that discussions derived from economic releases are limited mostly to pre-2013 data. The discussions of our differing positions will become better as new economic releases for 2013 become available.
GSP will only be released in June, as Mr. Hanson correctly notes, but why wait? One can use already released data — namely the Philadelphia Fed coincident indices, calibrated to match trends in GSP, to estimate what 2013 GSP data will reveal.
Figure 1: Log per capita Gross State Product in Wisconsin (
blue red), and in Minnesota ( red blue), in Ch.2005$, rescaled to 2011=0. Vertical dashed line at 2011. Data for 2013 is based on Philadelphia Fed coincident indicators. Pre-1997 data in Ch.1997$ is spliced to post-1997 data. Source: BEA, Philadelphia Fed, Census, and author’s calculations.
Since 2011, Minnesota’s per capita GSP has grown a cumulative 2.1 percentage points (log terms) more than Wisconsin’s.
Mr. Hanson also alleges that I have been avoiding the Wisconsin-Minnesota comparison of late, due to imminent improvement of Wisconsin economic performance. This post should put that notion to rest.
Despite the likely benefits, policymakers in Wisconsin make statements such as the following (from Fox6Now):
“I’m not a big supporter of artificially increasing the minimum wage. I think the marketplace is a much better way to go,” [Wisconsin Assembly Speaker Robin] Vos said.
The minimum wage is by definition the lowest hourly remuneration that employers may legally pay. As a legal requirement, it can only be changed “artificially”. The marketplace could alter the equilibrium wage rate, as supply and demand conditions change — but that would be changing, not the minimum wage, but … the equilibrium wage rate. I guess Speaker Vos means that the equilibrium rate — which has been delivered by the marketplace thus far — is the one we should be happy with. (Dr. Pangloss would nod in assent.) At this juncture, it’s useful to remember that, just like not all things organic are necessarily good for you, not all equilibria are optimal. A free market is not necessarily a competitive market, and in the presence of monopsony power and asymmetric information, one can imagine intervention improving outcomes.
In my view, an increase in the minimum wage could have an impact on income inequality in Wisconsin. According to EPI, 22.4% of the total Wisconsin workforce would be affected by the minimum wage (i.e., the floor would bind, and other wages not bound would rise in a ripple effect), a share slightly higher than that pertaining to the national workforce (21.3%).
More importantly, for low income workers, an even greater share would be affected.
Figure 2: Share of Wisconsin workforce with income less than $20,000 affected (both directly and indirectly) by a $10.10 minimum wage in effect by July 2016. Source: Economic Policy Institute; see EPI for details of calculations.
To the extent that minimum wage increases tend to have negligible employment effects, the higher wages should increase the total low-income wage bill, as discussed in this post, which recounts the recent empirical literature, and provides a diagrammatic exposition elaborating on this point.
The President has voiced support for an increase in the minimum wage to $10.10 in three increments, as laid out in the Fair Minimum Wage Act. A time series plot of the minimum wage starting from 1960 is presented below, along with minimum wage that would obtain assuming passage of the FMWA in March (merely for expositional purposes — this is not a forecast!).
Figure 3: Minimum wage (blue), minimum wage under Fair Minimum Wage Act (FMWA) assuming passage in March 2013 (blue +), and real minimum wage in December 2013 dollars (red), and under FMWA assuming CPI-all urban inflation evolves as in WSJ January 2014 survey, mean response, forecasts linearly interpolated (red triangles). Projection period shaded light green. Source: BLS via FREd, WSJ January 2014 survey, and author’s calculations.
Note that the increase in the minimum wage under the FMWA brings the real minimum wage (assuming the mean WSJ forecast trajectory) back only to levels of April 1979 (the maximum is 10.79, in December 2013 dollars, recorded in February 1968).
Moreover, the proposed minimum wage increase merely shrinks the nominal gap between the minimum wage and the average hourly wage rate for production and non-supervisory workers to that prevailing in 2002 (assuming the average hourly wage rate stays at 2014M01 levels, an assumption which likely overstates the degree to which the gap is closed).
Figure 4: Real minimum wage in December 2013 dollars (red), and under FWMA assuming CPI-all urban inflation evolves as in WSJ January 2014 survey, mean response, forecasts linearly interpolated (red triangles), average hourly wage for production and non-supervisory workers (olive) and for private sector workers (dark blue). Projection period shaded light green. Source: BLS via FREd, WSJ January 2014 survey, and author’s calculations.
Nobody claims that raising the minimum wage will alone reverse fully trends in income inequality in place since (particularly) the 2000′s. However, if one wants to do something concrete and feasible about income inequality, this is one place to start.
Jason Furman and Betsey Stevenson at the Council of Economic Advisers recap the empirical literature on the effects of the minimum wage on employment, turnover, and productivity, in a blogpost yesterday.
[Updated text: edits added 11AM Pacific - MDC]
Update, 2/18, 4:15pm:
Here’s the abstract from a paper by Doug Irwin in the February issue of the Journal of Money, Credit, and Banking:
The intellectual response to the Great Depression is often portrayed as a battle between the ideas of Friedrich Hayek and John Maynard Keynes. Yet both the Austrian and the Keynesian interpretations of the Depression were incomplete. Austrians could explain how a country might get into a depression (bust following a credit-fueled investment boom) but not how to get out of one (liquidation). Keynesians could explain how a country might get out of a depression (government spending on public works) but not how it got into one (animal spirits). By contrast, the monetary approach of Gustav Cassel has been ignored. As early as 1920, Cassel warned that mismanagement of the gold standard could lead to a severe depression. Cassel not only explained how this could occur, but his explanation anticipates the way that scholars today describe how the Great Depression actually occurred. Unlike Keynes or Hayek, Cassel analyzed both how a country could get into a depression (deflation due to tight monetary policies) and how it could get out of one (monetary expansion).
Today we are fortunate to have a guest contribution written by Jeffrey Frankel, Harpel Professor of Capital Formation and Growth at Harvard University, and former Member of the Council of Economic Advisers, 1997-99.
Friday’s jobs report showed the US unemployment rate falling to 6.6% in January. This is within a whisker of the 6 ½ % threshold that the Fed had announced at the end of 2012: It had said that it planned on keeping monetary policy easy at least until the unemployment rate had fallen below that level. But the central bank is nowhere near ready to raise interest rates, and so has had to back away from that particular “forward guidance.” The FOMC said on December 18 that it now expects to keep interest rates low well past the time that the 6 ½ % mark is reached.
Even though the Fed had always said that the unemployment threshold was a necessary but not sufficient condition for tightening, some critics believe that this shift in emphasis is a policy “U-turn” that has confused the financial markets. If so, it was avoidable.
The Bank of England has undergone a parallel sequence of events. In mid-2013 it gave similar forward guidance, with a threshold figure for UK unemployment of 7%. But Governor Mark Carney at Davos at the end of January signaled that he is now moving away from that guidance. The reason: the British labor market is now “in a different place” from what was expected: The Bank of England’s original forecast had been that the 7% number would not be reached until mid-2016; yet British unemployment, unexpectedly fell to 7.1% in the autumn and thus is poised imminently to cross its threshold as well. And yet, with the economy still weak and inflation still low, the monetary authorities appropriately do not want to raise interest rates anytime soon.
Janet Yellen is now at the helm of the Fed. She will have to re-think forward guidance and use information other than the unemployment rate. (And other than the inflation rate, which has been part of the guidance all along.)
The reason for the change in policy is again clear. The Fed hadn’t expected to reach the threshold for tightening in 2014 or even 2015. But unemployment has fallen unexpectedly quickly — not because of unexpectedly rapid growth in the economy which might call for earlier tightening (good news), but, in large part, because discouraged workers have left the labor force altogether (bad news). (To be sure, a big part of the four-year decline in the unemployment rate has indeed been due to a growing economy; and even part of the decline in the labor force participation rate is due to the benign long-term trend of baby-boom retirement. Nevertheless unexpected exit from the labor force is probably the biggest component of the unexpectedly rapid recent decline in the unemployment rate.)
Both the Fed and the Bank of England are accordingly now subject to much criticism for having delivered forward guidance that they were subsequently unable to stick to. Some of these attacks are unfair. No one should want the central bank to slavishly follow statements made in the past if circumstances have changed in an unexpected way. Any fair critic must acknowledge that the ubiquitous demand for transparency with respect to the central bank’s plans (phrased simply) inevitably conflicts with the reality that the future is unpredictable, in particular with respect to such developments as unexpected fluctuations in the labor force participation rate. This uncertainty is why the monetary authorities have always hedged their foreign guidance. Nobody is now violating a past promise. Are the critics then being entirely unfair?
Not entirely. There was another way. A year or two ago, many of us were suggesting that the monetary authorities could announce a target or threshold for Nominal GDP, instead of for inflation, real income, unemployment, or other alternatives. Some of us explicitly warned that a threshold phrased in terms of the unemployment rate would be vulnerable to extraneous fluctuations such as a decline in labor force participation, and argued that a nominal GDP threshold would be more robust with respect to such unforecastable developments.
Just over a year ago, for example, I wrote in favor of “a commitment to keep monetary policy easy so long as nominal GDP falls short of the target. It would thus serve a purpose similar to the Fed’s December 12, 2012, announcement that it would keep interest rates low so long as the unemployment rate remains above 6.5% – but it would not suffer the imperfections of the unemployment number (particularly its inverse relationship with the labor force participation rate…).” [Central Banks Can Phase in Nominal GDP Targets without Losing the Inflation Anchor blog, December 25th, 2012.] Other NGDP proponents issued similar warnings.
This is yet another instance of a long-standing point: if central banks are to focus attention on a single variable, the choice of Nominal GDP is more robust than the leading alternatives. A target or threshold is a far more useful way of communicating plans if one is unlikely to have to violate it or explain it away when things change later.
This post written by Jeffrey Frankel.
The U.S. federal deficit fell from around $1.1 trillion for fiscal year 2012 to under $700 billion for 2013, and is projected by the Congressional Budget Office to be below $500 B by 2015. Although it sounds like continuing improvement, the CBO’s projected path is actually unsustainable. Here’s why.
The first thing to understand is the difference between the federal deficit and the federal debt. The deficit is a flow variable, measured in dollars per year. Specifically, the deficit is the difference between the government’s spending on goods, services, transfers and interest payments over the course of an entire year and the revenues that the government takes in during that year. The $680 B/year U.S. deficit was half as big in 2013 as it had been in 2009. By 2015 the CBO is projecting that the annual deficit will be down to only a third of its 2009 value.
By contrast, debt is a stock variable that measures how much money the government owes at a particular point in time. If the deficit is positive, it means that the government spends more over the year than it takes in as revenue. In that case, the government wouldn’t pay back any of the debt it owed at the start of the year, so it would have to roll over that debt and still owe that sum at the end of the year, and in addition would need to issue new debt to cover the deficit during the course of the year. Thus whenever the government is running a deficit (regardless of whether it is a smaller deficit than the year before) the total level of debt is going to be higher at the end of the year than it was at the beginning. At the start of fiscal year 2012, the government owed $10,128 B. During that year, it spent $1,087 B more than it took in as taxes, and it ended the year owing $11,281 B, an increase in the debt of about 1.1 trillion dollars. During FY 2013, the deficit was $680 B, and we ended the year with a debt of $11,982, about 700 billion more than we started. The deficit is projected to continue to decrease further in 2014 and 2015, but the debt nonetheless will continue to climb.
A growing debt load can be offset by the fact that the U.S. economy should be bigger in 2015 than it is today. The CBO projections released last week expect U.S. real GDP to rise by over 3% a year for each of the next three years. As summarized by the table below, the CBO is a little more optimistic than some other forecasters, but their growth forecasts strike me as quite reasonable.
Given this anticipated growth in GDP, even though the debt is expected to increase every year, the economy is projected to grow even faster, so that the CBO anticipates that U.S. federal debt held by the public will decline modestly over the next several years when measured as a percent of GDP.
Interest expense is another variable to factor in. The U.S. Treasury’s net interest expense for FY 2013 was $221 B, or about 1.8% of the ending net debt level of $11,982 B. That low interest cost, despite the large debt load, is reflective of the fact that the interest rates at which the Treasury is currently borrowing are extremely low. The imputed average interest rate, 1.8%, is close to the 2.2% average yield on newly issued 10-year Treasury securities during FY 2013. Historically, you can get a good estimate of the number of dollars that the Treasury will spend in order to make its interest payments by multiplying the level of outstanding debt by the average 10-year interest rate over the year.
Low interest rates are the main reason that the net interest expense ($221 B) was so low in 2013 despite the high level of debt. If interest rates were to return to the average level of 4.5% that we saw over the last decade, it would mean that the Treasury’s net interest expense would grow to 2-1/2 times its level last year, even if we were to get the deficit all the way down to zero beginning in FY 2014 so that the level of debt never grew above where it was at the end of FY 2013.
The CBO, like most other forecasters, is anticipating that interest rates will rise gradually over the next several years. In addition, rising medical costs and an aging population are expected to bring the primary deficit back up in the near future. When the CBO put all these different factors together, their conclusion was that, despite the fact that the deficit is projected to shrink over the next two years, and despite the fact that the economy is assumed to experience above-average growth over the next several years, U.S. debt will resuming rising relative to GDP after 2017.
In the CBO’s forecast, the sum that the U.S. Treasury will be paying each year just to make the interest payments on the debt it’s already accumulated is projected to exceed the entire defense budget or the total of nondefense discretionary spending by 2021.
There’s no reason to think that the number of dollars investors around the world are willing to lend to the U.S. government would continue to grow at a faster rate than the economy itself. Something more has to give as you project this scenario forward.
The cumulative growth gap since January 2011 between the US and Wisconsin coincident indices was 2.2% in December. The forecast indicates a continued widening to 2.8% in six months time.
Figure 1 depicts the log coincident indices for Wisconsin and the US.
Figure 1: Log coincident indices for Wisconsin (blue) and US (gray), and levels implied by leading indices for 2014M06. NBER defined recession dates shaded gray. Source: Philadelphia Fed, NBER, and author’s calculations.
Reader Ed Hanson wonders about the predictive characteristics of the leading indices produced by the Philadelphia Fed. Figure 2 depicts the actual and forecasted six-month growth rates for Wisconsin.
Figure 2: Actual six-month growth rate (non-annualized) in WI coincident index (blue) and corresponding leading index (red). NBER defined recession dates shaded gray. Source: Philadelphia Fed, NBER, and author’s calculations.
A regression of the actual six-month growth rate on the forecasted yields the following results:
Table 1: Unbiasedness test.
Note that the leading index appears to be an unbiased predictor of actual six-month growth rates, since the point estimate of the slope coefficient is 1.001, with a Newey-West robust standard error of 0.058; hence one would not reject a null hypothesis of a unit coefficient. The adjusted R2 is 0.84 — in other words 84% of the variation of the six-month growth rate around the mean is explained by the leading index. Note that this is not a “real time” analysis, as the coincident series are the revised, rather than initial, figures, and one might wish to compare the forecasts to the initial.
The leading index for the United States shares the same characteristics of unbiasedness; the adjusted-R2 is 0.93.
Has the behavior of the Wisconsin index changed over time? The log ratio (WI/US) rose from 1982 to a peak in 2000 before descending. Estimating the relationship between the (log) WI coincident index and the (log) US coincident index using dynamic OLS (6 leads and lags of the first differenced right hand side variable, 2001M01-13M12) and a dummy taking a value of one from 2011M01-2013M12 yields a cointegrating coefficient of 0.507, and the coefficient on the dummy is 0.021, significant at the .0002 level using Newey-West standard errors. Literally interpreted, Wisconsin economic activity is 2% lower than usual, starting from 2011M01.
This is not a conclusive finding; using a different specification, sample period, or estimation method would likely lead to a different finding.
Update, 2/8 noon Pacific: Reader Ed Hanson still contends that Wisconsin economic performance has improved since 2011M01. He writes:
What we mainly disagree with, the Walker policies have began the change back toward superior economic performance. I say it has, and comparing the performance of Wisconsin from 2001 until 2011, with Wisconsin after 2011, supports my position. In addition, Walker policy of reduced taxation, both personal and business, since 2013 will accelerate economic performance.
Since Mr. Hanson is not persuaded by a coefficient on a dummy variable, I have undertaken another analysis, which conditions on US economic activity, and lagged WI economic activity. I estimate a error correction model over the 2001M01-2010M12 period:
Table 2:Error correction model, WI and US economic activity.
Where LCOINWI (LCOINUS) is the log coincident index for Wisconsin (United States), “D” indicates first difference. Note the high adjusted-R2, Q(6) and Q(12) statistics fail to reject the no-serial correlation null, as does the Breusch-Godfrey LM (2 lags) test.
The implied long run cointegrating coefficient is approximately 0.50; that is a 1 percent increase in the US coincident index is associated with a 0.5 percent increase in the Wisconsin index, over the 2001M01-2010M12 period.
I use this error correction model to forecast out of sample (2011M01-2013M12), conditioning on actual US economic activity. The forecast is shown in Figure 3, with plus/minus two standard errors, compared against the actual.
Figure 3: Log actual WI coincident index (blue), forecast (red) and plus/minus two standard errors (pink). Forecast based on error correction model (Table 2). Dashed vertical line at beginning of Walker administration. NBER defined recession dates shaded gray. Source: Philadelphia Fed, NBER and author’s calculations.
Notice that the actual Wisconsin index under-performs the forecasted (what we would call the counterfactual, if the usual economic relationship between the US and WI economies prevailed into 2013) by a significant amount — by up to 2.5% (log terms) earlier in 2013, and 1.9% as of 2013M12. The fact that the blue line (actual) is outside of the plus/minus two standard error bands suggests that the under-performance did not happen by accident, at conventional significance levels.
The output gap remains large, even as the external sector supports growth; this outcome is partly due to excessively rapid fiscal consolidation
Economic Slack, Now and Next Year
The CBO released Budget and Economic Outlook on Tuesday. As part of the report, the CBO released its estimates of potential GDP in a manner consistent with the new GDP measures that incorporate intellectual property in the investment data. The output gap remains large and negative, using these updated estimates.
Figure 1: Log GDP (blue), mean forecast from January WSJ survey (red), and high and low forecasts from 20% trimmed sample (pink), and potential GDP (dark gray), all in billion of Ch.2009$, SAAR. NBER defined recession dates shaded gray. Source: BEA 2013Q4 advance release, CBO Budget and Economic Outlook (February 2014), Wall Street Journal January survey, NBER and author’s calculations.
Note that by 2013Q4, the output gap is -4.3% (log terms), and taking the mean WSJ survey response, the output gap at 2014Q4 is -2.9%. Even with the high estimate of GDP growth (after trimming the sample by 20%), the gap is -2.4% (Joseph Carson/Alliance Bernstein), whereas the low (after trimming) forecast indicates a -3.7% gap (Julia Coronado/BNP Paribas). The CBO projection (under current law, and based on data available as of December 2013 and the 2013Q3 second release) is 3.3%.
Fiscal Policy in Action
Part of the reason the progress in shrinking the output gap slowed in 2013 can be directly attributable to fiscal drag — in other words the prediction that the sequester would slow growth was realized.  (When the sequester deal was nearly finalized in
April February, Macroeconomic Advisers forecasted 2.6% growth for 2013 , with an sequester plus Fed offset scenario growth of 2.1%; q4/q4 growth turned out to be … 2.7%. )[Edits 2/7 8:25AM - MDC] Consider the shrinkage in the cyclically adjusted budget balance (expressed as a share of potential GDP). The adjusted deficit shrank substantially in Fiscal Year 2013.
Figure 2: Budget balance (blue) and cyclically adjusted budget balance (red), both as a share of potential GDP, in percentage points. Tan shaded areas denote forecasts. Source: CBO Budget and Economic Outlook (February 2014).
It’s perhaps easier to see the extent of fiscal consolidation by examining the change in the cyclically adjusted budget balance, as displayed in Figure 3.
Figure 3: Change in the cyclically adjusted budget balance as a share of potential GDP, in percentage points. Tan shaded areas denote forecasts. Source: CBO Budget and Economic Outlook (February 2014), and author’s calculations.
The External Environment
Net exports accounted for 1.33 of the 3.2 ppts of growth in 2013Q4 (SAAR); exports accounted for 1.48 ppts.
Figure 4: Contributions to real GDP growth from exports (light blue), imports (gray), and consumption, investment and government spending (dark blue). Source: BEA 2013Q4 advance release and author’s calculations.
The advance estimate relies upon forecasts of certain components, including December exports and imports (which will be released Thursday morning). One question is whether support from net exports will continue into the future.
Figure 5: Log real value of US dollar against broad basket of currencies (black, left axis), net exports (blue, right axis), net exports ex.-oil imports (red), and net exports ex.-oil products (dark red), as a share of GDP. NBER defined recession dates shaded gray. Source: Federal Reserve via FRED, BEA 2013Q4 advance release, BEA/Census, NBER, and author’s calculations.
In other words, there has been substantial improvement in the trade balance. Exports in particular have surged in the past couple quarters, despite the relative stability of the US dollar, in inflation adjusted terms.
Figure 6: Log real value of US dollar against broad basket of currencies (blue, left axis), and log exports of goods and services, in billions of Ch.09$ SAAR (red, right axis). NBER defined recession dates shaded gray. Source: Federal Reserve via FRED, BEA 2013Q4 advance release, and NBER.
It remains to be seen whether export growth will continue at this pace given the turmoil in emerging markets and the tentative nature of the recovery in Europe. In particular, depreciating emerging market currencies  could result in a stronger dollar — the daily nominal index has recently exceeded levels in early July and in August. For its part, CBO projects a relatively stable dollar in 2014, while Deutsche Bank forecasts a twelve-month 8% appreciation (from 12/18).
Update, noon 2/6: December trade figures released today indicated a $38.7 billion trade deficit, slightly larger than the Bloomberg consensus of $36 billion. Jim Dorsey at IHS-Global Insight as well as Macroeconomic Advisers estimate that 2013Q4 growth will be 0.4 ppts lower (q/q SAAR) as a consequence of these figures.
Nominal exports are shown in Figure 7 below.
Figure 7: Nominal exports of services (blue) and services (red), in millions of dollars, per month, seasonally adjusted. Source: BEA/Census.
“[I]n the aftermath of the financial crisis, U.S. policies and a dysfunctional international monetary system have paradoxically strengthened the dollar’s importance.”
This is from a description of Eswar Prasad’s important new book, The Dollar Trap (Princeton). From the website:
The U.S. dollar has reigned supreme as the world’s dominant reserve currency for nearly a century. But now the dollar’s dominance is under threat.
The near-collapse of the U.S. financial system in 2008, the high and rising level of U.S. government debt, and political gridlock in Washington have shaken confidence in the U.S. economy and the safety of its government debt. Other currencies such as China’s renminbi are staking their own claims to become reserve currencies, adding to speculation that the dollar’s glory days are coming to an end.
The Dollar Trap overturns this conventional wisdom. The book shows how, in the aftermath of the financial crisis, U.S. policies and a dysfunctional international monetary system have paradoxically strengthened the dollar’s importance.
In international finance, it turns out, everything is relative. Ultimately, there is no good answer to this question: If not the dollar, then what? That is why, despite all its flaws, the dollar will remain the ultimate safe haven currency.
Bitcoin is a digital currency for which no government, bank, or corporation takes responsibility. Like many others, I was curious to learn how it works and why it seems to be succeeding.
Instead of having a sum (in dollars) in an account with a bank, you could have a sum (in Bitcoins) that you hold in an account that is kept track of by a network of individuals with a public record of where all the sums reside. The mechanics of being able to transfer an entry from one Bitcoin account to another are based on advances in cryptology that use open-architecture algorithms to convert one string of data into another. You can see one in operation here. You enter one string of characters, and out comes another string. Although the formulas by which the output is calculated are totally open and public, it is essentially infeasible to do the operation in reverse. If you only know the string that came out as a result of the operations, about the only way you can guess what went in is by trying every possible input string, a very time-consuming process even for the fastest computers. On the other hand, if you tell me the input you used and I already know the output, I can readily verify whether the input string was indeed as you reported.
The output of a Bitcoin transaction is based on combining some private code associated with your holdings, which only you know, with the full history of previous transactions, which everyone knows. If you supply the correct private code, other users can verify that you indeed were the owner of that sum because your code together with the public history correctly solves a known math problem. In this way, your participation is required to transfer your sum to a new owner, with security of the system maintained by the difficulty of anyone simply guessing the code.
OK, so let’s grant for the time being that the technology exists for you to securely order the transfer of some of your Bitcoin holdings to somebody else without any government or bank needing to be involved. Why does the stuff have value in the first place? The answer is that it would be very helpful to many buyers and sellers of real goods and services if they were able to pay for transactions in this way. We can think of any form of money as an asset that provides liquidity services, which refers to the tangible benefit to its holder coming from the ability of the asset to facilitate certain transactions. The value of money, that is, the value of real stuff you’d be willing to give up to hold money, can be thought of as the present value of the stream of these future liquidity services.
Bitcoin has two potential advantages over credit cards for providing such liquidity services. First, the supporting network only needs to verify that the private code is valid, which is less costly than verifying that you are indeed the rightful owner of a credit card and are ultimately going to deliver good funds. With a conventional credit card, the merchant needs to pay the card company a significant fee for the transaction which in an economic sense results from that high cost of verifying everybody’s compliance. That’s why many merchants are embracing Bitcoin. Your Bitcoin gets deposited into the account of a third party that the merchant specifies. That third party then gives the merchant dollars, confident they will be able to get dollars in turn from somebody else who will want the Bitcoins to pay for some other transaction.
Second, Bitcoins are relatively more anonymous than credit cards. In this respect, they enjoy some of the same advantages as cash. It’s striking that of the $1.2 trillion currency in circulation, three-quarters is held in the form of $100 bills, which many of us never carry. The use of Bitcoin for illicit transactions may have been part of what helped it initially develop into something that had a dollar value. But people like Charlie Shrem can tell you the internet isn’t nearly as anonymous as it seems. Marc Andreessen explains:
Much like email, which is quite traceable, Bitcoin is pseudonymous, not anonymous. Further, every transaction in the Bitcoin network is tracked and logged forever in the Bitcoin blockchain, or permanent record, available for all to see. As a result, Bitcoin is considerably easier for law enforcement to trace than cash, gold or diamonds.
Yet another group helping to establish Bitcoin’s value are people who have an ideological passion for a system of exchange that is independent of any government or bank, perhaps some of the same individuals who want to hold their wealth in the form of gold, despite the volatile value of both. And like gold, the fact that Bitcoin has been appreciating in value relative to the U.S. dollar brings in some who assume the trend has to continue. Much of the current value of a Bitcoin, just like much of the current value of an ounce of gold, could well turn out to be a speculative bubble.
At a current price of $944 per Bitcoin and 12.3 million Bitcoins in circulation that gives a total market value of $11.7 B. That’s a little more than the $10.3 B worth of U.S. one-dollar bills that were in circulation as of the end of 2012.
Will it keep up? The value of the liquidity services that something like Bitcoin could provide is certainly quite tangible. Bitcoin’s functionality relies on the security of the underlying cryptology, and I am no one to judge whether better algorithms might develop for hacking the code or usurping the network on which the system depends. Another detail I am unclear about is whether a peer-to-peer network can continue to be relied on to provide verification to merchants at minimal cost. Currently the system creates new Bitcoins that are credited to entities on the network who successfully solve sets of new verification problems, giving individuals an incentive to maintain and update the system of accounts as well as ensure that the number of Bitcoins grows at a fixed rate over time. But the system is set up so that the maximum number of Bitcoins could not exceed 21 million, a ceiling that we are already more than halfway toward. Could a variant of the system continue to survive after we reach peak Bitcoin?
Hard to know where this is all going to lead. But one thing is clear– we have added a very interesting new chapter in the history of money.
From the Wall Street Journal:
The Bureau of Economic Analysis announced today that U.S. real GDP grew at a 3.2% annual rate in the fourh quarter. That’s two quarters in a row now of above average growth. Given recent experience, that sounds pretty good.
The brightest star was U.S. exports of goods and services, which contributed 1.5 percentage points to the 3.2% total. Nearly half of the export gains came from the foods, feeds, and beverages category, in which agricultural goods shipped to China figured prominently. Another important contribution came from exports of nondurable industrial materials. This includes exports of refined petroleum products, which are one byproduct of growing domestic production of crude oil.
The biggest drag came from the public sector, resulting in part from the federal shutdown in October. Lower government purchases of goods and services subtracted 0.9 percentage points from the fourth-quarter GDP growth rate. Bill McBride believes that public spending and private investment should make positive contributions in the current year:
[Residential investment] should make a positive contribution in 2014, the drag from the Federal Government should diminish, state and local governments should make a small positive contribution again this year, and investment in equipment and software and non-residential structures should also be positive in 2014.
Two quarters of above-average GDP growth have brought our Econbrowser Recession Indicator Index down to 3.3%, which is a very favorable reading. Note that in calculating this index we allow one quarter for data revision and trend recognition. Thus the latest value, although it uses the GDP numbers released today, is actually an assessment of the state of the economy as of the end of 2013:Q3. However, our index is never revised, so that the numbers plotted in the graph below since 2005 are exactly the values as they were reported one quarter after each indicated historical date on Econbrowser.
To sum up: things are looking good, and are likely to get even better.
Is growth really collapsing?
Reader Steve Kopits doubts the veracity of the current reading on Chinese GDP growth. He writes:
The oil stats say China’s growth has been decelerating for a year, and current GDP growth is in the 0-3% range. Is there another historical example of a major GDP driver like China growing at 7.5% and the currencies of its major vendors collapsing? Do we really believe China’s 7.5% reported GDP growth? In volume terms, that would be as much as 10% GDP growth in 2005. And in 2005 there were all sorts of stories in the press about China’s booming energy sector, housing, manufacturing, luxury goods, exports, infrastructure, etc. Most of what I read about China lately is air pollution, ghost cities and South China Sea tensions. [Are we] sure this economy is growing at 7.5%?
Well, I cannot claim to be an expert on Chinese national accounts, although I know enough to say the level is probably off, and that the composition is probably mis-measured. But I am not sure the mis-measurement is any greater than it was in earlier times, so I am dubious q/q annualized real GDP growth is in the 0-3% range. Here is some data and econometric evidence to buttress my assertion.
First consider what is the reported q/q real GDP growth (non-annualized in the figure below):
Figure 1 from Trading Economics, accessed 1/28/2014.
Note that 1.8% q/q is equivalent to 7.4% SAAR (specifically, 1.0184 = 1.074).
Now, what about the indicators like electricity that we referred to in the 1990′s?
Figure 2: Industrial product and electricity. Source: China Economic Quarterly 17(4) (December 2013), GK Dragonomics.
Trends in these indicators are not consistent with a near zero growth. China economy watchers typically look at a variety of indicators now. One popular summary measure is the Li Keqiang indicator (thus coined when he was the vice premier), which is a composite of electricity production, rail cargo shipments, and loan disbursements. Here the evidence also seems counter to the argument of 0-3% growth.
Figure 3: “Li Keqiang” index. Source: World Economics, accessed 1/28/2014.
PMI’s throught 2013 indicated continued growth, although the first reading for 2014 indicated a possible contraction in industry output (the Markit/HSBC measure indicated contraction while the market expectation of official measure is just above 50). , .
Now it’s possible that the relationship between the Le Keqiang indicator and GDP has broken down. However, John Fernald, Israel Malkin, and Mark Spiegel, economists at the San Francisco Fed, have recently assessed this proposition in “On the Reliability of Chinese Output Figures” (March 2013). They concluded:
…the 2012 reported output and industrial production figures are consistent both with alternative Chinese indicators of the country’s economic activity, such as electricity production, and trade volume measures reported by non-Chinese sources. These alternative domestic and foreign sources provide no evidence that China’s economic growth was slower than official data indicate.
The figure below highlights this relative stability in the relationship.
The authors also assess the correlation with a broad index which includes an index of consumer sentiment, construction of new floor space, an index of raw materials usage, air passenger volume, and the nominal value of new residential real estate construction.
So we have some confidence that through 2013 growth continued. Now, it is conceivable that q/q output growth in the first quarter of 2014 could drop to 0-3% (annualized). It’s important to remember, however, that the manufacturing PMI applies to only part of the Chinese economy; the weighted average of the manufacturing and non-manufacturing PMI is still above 53, so forward looking indicators (which can be wrong) still point to expansion.
For more on Chinese GDP, and why we expect — and want — some slow down in growth, see here.
Addendum: Here is a up-to-date graph of the growth rate in the Li Keqiang index. Note that each institution uses slightly different weights (SF Fed uses principal components).
Figure 4. Source: JP Morgan Asset Management, Market Insights: Guide to Markets: Asia, 1Q2014.
Looks like positive growth when expressed in terms of first derivative.
Expectations of central bank policies are only part of the story
The rapid decline in emerging market currency values has been quite remarkable:
Figure 1: Log exchange rates against USD for India (dark blue), South Africa (red), Turkey (green), Brazil (teal), Thailand (chartreuse), and Argentina (purple), all normalized to 2013M05=0. January data is for 1/22. Source: St. Louis Fed FRED, Pacific Exchange Services, and Financial Times.
The movements at first glance would seem to be attributable to revisions to expectations regarding future central bank policies — both quantitative/credit easing as well as the path of short rates (discussed in this post, as well as this June post). As Steven Englander (Citi) noted on Friday:
The sell-off in high-beta currencies, particularly in EM, is driven by an abrupt change of tone among G10 central banks with respect to liquidity provision. The fear is that the Fed, BoE, and even the BoJ will become less dovish more quickly than had been though even a few weeks ago. The question is whether one or more of these CBs will act to reverse this view, given that central banks talk all day about cooperation, but act only when disruptions abroad threaten national economies.
The Fed and the BoJ look most likely to signal that market fears of an abrupt pullback from abundant liquidity provision are overdone. Recent BoJ comments are stoking the fire, but there is unwelcome collateral damage to USDJPY and the Nikkei. The Fed is likely to be obsessed next week with domestic policy considerations rather than global repercussions. Most likely outcome is that we see more market friendly comments, but no indication of substantive change in policy.
But as Englander observes, G-3 central bank policies are not the only drivers; other global factors as well as domestic political factors are also weighing down on some currencies. This proposition is expanded upon in posts by Kaminsky at FT and Fernholz at Quartz.
The Other Factors
Regarding the China effect, the IMF has some interesting work on the subject. Figure 4 (reproduced below from IMF (2013)) provides some summary results:
Figure 4 from IMF (2013).
The graph is described thus:
Figure 4 illustrates rough estimates of the impacts of a slowdown in Chinese growth from an average of 10 percent during the previous decade to an average of 7½ percent over the coming decade. The numbers shown in the figure are the declines in net revenues (as a percentage of GDP, adjusted for Purchasing Power Parity) for various commodity exporters as a result of lower Chinese demand.
Of these, Brazil and South Africa have experienced noticeable losses over the past month, as shown in Figure 1.
One remark about Argentina. The extreme drop in the Argentine currency is associated with the central bank’s decision to cease intervention. This is a reminder that one cannot infer the extent of the shocks (arising from foreign monetary policy, economic uncertainty, terms of trade, and political uncertainty) from observed currency movements, as much depends on the central bank reaction function — which in turn depends in part on the size of foreign exchange reserves (Argentina’s are fast declining ). More on that in this post.
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