Muddled Monetary Policy and Negative Money Multipliers

A deviation from the textbook to a complex world of shadow banking and collateral chains

An effective monetary regime requires a functioning concrete mechanism. For as much as Nick Rowe discusses nonlinear chains of causality or the chuck Norris expectations model of monetary policy, the concerns of those of the concrete steppes cannot be totally ignored. A recent Voxeu paper by Manmohan Singh and Peter Stella provides a cautionary tale for those who want to leap off the concrete steppes without a closer look. What's the problem? A potential monetary policy negative money multiplier.


In the traditional Econ 101 explanation of monetary policy, the federal reserve expands the money supply by buying treasuries, thereby expanding the monetary base. Banks use that cash by lending it to businesses that subsequently invest the money. This spending makes its way back to the banks via deposits, thereby adding to the stock of demand deposits and the money supply. A fraction of the deposits, as determined by the reserve ratio, is held in reserve by the banks; the rest is lent out again. This is one of the marvels of fractional reserve banking:The "money" that the fed creates in its open market purchases multiplies itself throughout the money supply through this lending/re-lending process.

Singh and Stella emphasize a different channel of money multiplication: collateral chains. Their fundamental argument is that, in a world of shadow banking, repos, and financial derivatives, a lot of credit creation takes place by pledging the same collateral over and over again, a process otherwise known as rehypothecation. High quality collateral, "safe assets", serves the role deposits serve in the textbook explanation of monetary policy. Safe assets, instead of being deposited like cash, are used over and over again by different firms to obtain financing, thereby expanding the "shadow money supply". Banks who were burned in the past want to limit their loans and try to deleverage. But in this process, banks shorten the collateral chains, make credit less available, and contract the shadow money supply. Central banks can compound the problem by reducing the supply of safe collateral by purchasing the assets in open market operations. As a result, traditional purchases of US treasuries become contractionary. While they may increase the base, they prevent collateral chains from forming and facilitating more credit creation. The cash that financial institutions get from open market operations can't be rehypothecated, and therefore fails to expand credit supplies. Instead, collateral chains contract and this "shadowy" money supply grows more limited. So yes, the Fed can raise prices to any level by printing money. But no, rehypothecation and collateral chains prevent quantitative easing from being fully effective.

Collateralization also brings up the possibility that the Fed could ease not by Quantitative Easing, but rather by Qualitative Easing. Instead of buying up collateral and replacing it with uncollateralizable cash, the Fed could buy up risker assets (mortgage backed securities again?) and replace them with safer treasuries. While it may not expand the size of the Fed's balance sheet, it would help with the collateral chains and expand credit. Fiscal policy then gains new traction, as increases in government debt can 

1) Increase available collateral, thereby directly expanding credit

2) Limit the contractionary effect of the Fed's buying of collateral

An interesting corollary of this is that if fiscal policy becomes more effective, the Federal Reserves interest rate forward guidance becomes more powerful. As I've written before, the interest rate guidance would lose its indeterminancy, and change from Delphian to Odyssean. Low interest rates no longer represent low expectations for NGDP, rather they represent a committment to a temporary period of faster than trend NGDP growth. Interest rates aren't low because of low NGDP, but rather in spite of it.

Meanwhile, David Beckworth's NGDP targeting - safe assets story becomes murkier. David argues that, in a world of stable NGDP growth, private sector safe asset creation goes up significantly. So if the Fed commits to a stable NGDP growth path, the collateral chain problem goes away as there's enough private sector safe assets for rehypothecation. But if the NGDP growth path is uncertain, Quantitative Easing won't help. While the initial treasury purchases may marginally increase lending, the credit contraction from collateral chains may cause the policy to be net contractionary. However, the story is still not that simple. Much like currency depreciation in a small economy, there exists a sufficiently large change that would boost growth. Quantitative Easing would only have an effect once it has collapsed collateral chains to their minimum. After that point "printing money and buying assets" would undoubtedly raise NGDP. This leads to a peculiar result when we take Nick Rowe's concepts of nonlinear causality and expectations into account. If the scale of the initial asset purchases is perceived as credible enough to shape future NGDP expectations, even though initial purchases would shorten collateral chains, the supply of collateral would expand as the private sector created more safe assets. 

A major problem with this scenario is that the market response function becomes highly nonlinear. With no policy action the market contracts. With some policy action the market contracts further. Only with outsize policy action are private agents convinced of a stable future NGDP target, and do collateral chains continue to expand. In this situation, it would be hard for a central banker to tell how many asset purchases "would be enough". Consequently, a credible NGDP target becomes even more important. First, it facilitates private safe asset creation. Second, it assures the market that the Fed won't end up in the middle where collateral chains contract and monetary expansion fails to raise output or prices. This is a critical argument in favor of NGDP targeting in an increasingly complex world. In spite of all of the complications in modern finance and banking, stable nominal expectations can help smooth those problems over and maintain the processes of safe asset creation.

Another lesson we can learn from the debt rehypothecation/shadow money supply story is that nominal GDP targeting has a critical role to play in limiting the negative growth effects of financial regulation. If debt chains serve a function to expand the shadow money supply, then banning debt in a move to a more Black Swan free society can have severe monetary effects, significantly hampering growth. But if the central bank targets nominal GDP (ideally through a mechanism not as bank or debt-centric as treasury purchases), this would help ease in the structural adjustments to build macroeconomic resilience.

Monetary policy is muddled, adjustments are painful, but stable expectations can help. While Singh and Stella's story may not be perfectly applicable now, it is an interesting example of the peculiar nexus of modern finance and modern monetary policy. These uncertainties will only get worse in the future, so it's even more important to find a credible, robust, stable regime now.

Levels and Rates to Fill the NGDP Data Gap

An implementation of levels and rates to quickly estimate NGDP growth

An important problem with NGDP targeting is data frequency. NGDP data only comes in every quarter, and is also subject to large revisions. This is one of the stronger arguments against NGDP targeting, as the lack of data makes it hard for the market to check that policy makers are hitting their targets. Expectations don't always match reality, so it's important to have a concrete and frequently updated data source to monitor the economy.

Evan, in his musings on level and rate targeting, offers a theoretically robust alternative in the current world of flexible inflation targeting. From his post (my emphasis):

Third, given a mixed rate/level targeting regime, the Fed has what should be the rate and what should be the level backward. In the long run, the Fed has almost no control over the unemployment rate, yet almost total control over the price level; in the short run, it does have some control over real variables such as unemployment. Given those constraints, it makes far more sense to level-target the variable which the Fed controls in the long and short runs, i.e. the price level, and to rate-target the variable over which the Fed has some control in the short run, i.e. change in nonfarm payroll employment or quarterly real output growth.:

As I commented before, such an arrangement would be aligned with Okun's law, which states that year over year falls in unemployment is approximately equal to year over year real GDP growth minus 3. As a result, in Evan's formulation, the combination of change in unemployment and the change in the price level would approximate an NGDP target. His other alternative, nonfarm payroll employment, is what I want to test in this post.

Armed with the requisite FRED data, I looked to see if I could find a simple econometric relationship between YoY NGDP growth, YoY nonfarm employment growth, and YoY Headline CPI growth. In other words, I tested the relationship:

NGDP Growth = a*Inflation + b*%ΔEmployment + Constant + Error

As I'm interested in how the rule would help guide policy in the most recent "Great Recession", I calibrated the data on pre-crisis, 1948-2006  data and then saw how the model predicted NGDP growth "out of sample" for 2007-2012. It turns out that nonfarm payroll, inflation, and NGDP do form a rather tight relationship (note that I use headline inflation. Core inflation does not substantially change the conclusions). The precise rule that I get from the calibration is:

NGDP Growth = 0.517*Inflation + 1.083*%ΔEmployment + 2.968

But if we're looking for a more general, simpler rule of thumb, we can approximate it to:

NGDP Growth = 0.5*Inflation + 1*%ΔEmployment + 3

From here, we can compare the time series of the simple approximation to actual NGDP growth.

We can easily see that the measure does quite well throughout the historical period. I've already divided the graph as Marcus Nunes does for each central banking "regime", and it's clear that the composite measure exhibits the same trends as Marcus shows in his NGDP graphs.

What you do see is that, even in the out of sample period, the time series match up well. Around the end the composite measure does spike to 6% while actual NGDP growth is around 4%. However, in spite of that differential, the rule still makes very good out of sample predictions for the 2007-2012 time period:

Interestingly enough, the regression coefficient has been around one, which means there's an approximate one to one relationship between actual NGDP growth and the composite measure. About 80 percent of the variance in the composite measure is explained by the variance in NGDP growth. Of course, the relationship is not perfect, but it's really quite amazing how well the out of sample prediction holds up. So before the Fed establishes an NGDP futures targeting regime to help with the data availability problem, it can still use monthly available statistics such as CPI and changes in nonfarm payroll to approximate an NGDP target. When actual NGDP data comes out, that quarterly data can be checked against the composite measure. The higher data frequency would help cement in the Fed's credibility as the regime could be checked, thereby smoothing and enabling a transition to a full fledged NGDP level targeting regime.

P.S. While playing with the data, a statistically significant relationship between inflation and employment growth emerges. 95 confidence interval on the slope returns (0.04, 0.22). Pseudo-Philips Curve, anyone?

Middle Kingdom, Middling Growth

...packed with the asymmetric possibility of a catastrophe

A recent FT alphaville overview on the flurry of bad news on China. PMI's are also looking quite poor. Josh Brown, in his review of the Barron's article, touched on a key quote:

"A falloff in demand for steel, cement, and copper would lead to heavy layoffs. He reckons that some 25% of all Chinese steel consumption goes into residential real estate."

This is very similar to what I noted about a month ago with the varied, unknown connections that make the Chinese economy so fragile. Besides the direct job effects, copper prices have a dangerous relationship with collateral for financing, thereby creating the possibility for a financial mini-crisis in commodities along with the brewing housing debt problems. The Chinese economy is in a set of asymmetric straits. And for as much as the Chinese government is a "pragmatic" one, the serious suits at the top of the Politiburo are not phasing out state owned enterprises in favor of more efficient channels for growth, At such a juncture, perhaps central government easing can restore growth to above 8% and hold it relatively steady. But in the process, fault lines are deepened, and catastrophe looms. You know how good it can get, but you have no idea how bad it can become. So China will be fine, or it will go through crisis. And you never know where a single mistake can lead you.

"Dammed" Chinese (De)regulation

A look at liberalization failure in Sichuan water dam regulation

Around the beginning of the year, Obama made an appeal for government consolidation and to reorganize the bureaucracies in a more logical manner. At first pass, this seems like a reasonable approach. But we must beware of what Karl Smith calls "liberalization failure". While current regulation might be frustrating, badly done deregulation can result in even more finger pointing instead of actual work. Perhaps we can draw a lesson from an interesting anecdote about Sichuan river hydropower stations. 

Hydropower ventures are interesting to regulate because they do carry an externality. Besides changing the distribution of water, the dams carry a risk of collapsing. When this happens, any tunnels downstream can get flooded and, in severe situation, workers are killed as their houses are destroyed. As a result, before 2000, the construction of the dams was regulated by both a commerce agency and a water resource agency. The first would determine whether a dam could be built, while the second inspected the dams to make sure the engineering was of good quality. The collapse externality is the market failure. The inspection regime is the non-market based solution, and can be thought of as the government failure.

So where's the liberalization failure? To give you an idea of the problem, Sichuan dams are supposed to be inspected within three years of construction. Only after the inspection can they officially start producing commercial electricity. But in the past 10 years, of the over 4000 new hydropower stations constructed, a considerable number of them have never been inspected. Yet they have been "testing" their generators and selling the electricity on the market. Sichuan is one of China's largest hydropower provinces; why does it have so many regulatory problems?

A lot of the problem boils down to what the government tried to do starting in 2000 to liberalize the development of dams on the rivers. Remember the commerce and water resource agencies? In 2000's, the commerce agency took over the supervision of basic engineering from the water resource agency. But this transfer of power took place without any transfer of real resources. While the water resource department had over 150 people working on the issue of supervising construction, the commerce agency only had a few. Although their was a joint memo discussing inter-agency cooperation, was steadily ignored over the years. By 2003, the water resource agency gradually lost all of its regulatory power to the economic development agency, but still had to bear the burden of fixing engineering problems when they threatened the public safety. An official for the water resource agency summed up the problem very nicely:

"If we try to regulate, we're not the right agency. If we try to not regulate, they come and hold us responsible for the accidents on the river"

There were accidents aplenty. In 2006, because of a half-done shoddy job by a contractor, a dam broke, flooding workers houses with over a thousand cubic meters of water, killing eight people and injuring six. The manager of the dam skimped on some safeguards, jeopordizing the stability of the plant. Many of the contractors were uncertified, and there was never a third party inspector to make sure the work met standards. In 2011, large floods interrupted dam construction and flooded work tunnels, trapping thirteen workers and killing all but one of them.

And who's responsible? In the 2011 case, the government noted that better engineering would have saved the workers, but ended up putting all the blame on a "natural disaster". Even when the government takes responsibility, it is incredibly vague. A translation from a 2012 policy paper titled "Suggestions for increasing engineering and design oversight for hydropower plants of less than 25 megawatts"

After many years of hard work, hydropower plants have become a primary energy source for Sichuan's rural counties, especially for the minority and mountainous areas, and they hold the potential to support the province's economic and social development as well as its citizen's livelihoods; however, in this process of development many problems have been revealed, especially in the areas of regulatory coordination, inspection procedures, engineering oversight, environmental protection, as well as other areas that are still pending increased attention and improvement.

Note there's not a single line on who is doing this work. Agencies are hinted to later on in the paper, but not with any specificity. Merely that building safety issues should be taken up by all relevant agencies". This is especially problematic when "inspection procedures, engineering oversight, and environmental protection" are all divided over rmultiple agencies that are governed over many levels of government. Some are local, whereas others are run by the province, and even others are run by the central government.

As a result, the locus of liberalization failure centers around this problem with agency responsibility. Responsibility is never clear, and work is not delegated efficiently across the agencies. This is a key point about deregulation and attempts to make policy more "market friendly". What you may end up with is an incomplete transfer of power, thereby giving people the regulatory power but not the expertise, or vice versa. The separation of these powers may not result in check and balances; rather, it can create perverse systems of incentives in which the mistake of one agency is paid for by the work of another. In the Sichuan case, the water resource agency has to bear the burden of the commerce agency's engineering mistakes. The commerce agency could pursue their goal of economic development without too much concern, because if they went overboard it was the water resource agency's problem to solve. So in the regulation of an externality, the institutional structure actually promotes externalities within the government.

Another more philosophical note is that the government can never be truly thought of as a unified entity, but rather is better conceptualized in terms of individual agencies. These entities have their own missions, own internal incentive structures, and own responsibilities to outside agencies. As Mankiw's principles remind us, incentives matter, in both private and public life. And this is the sad truth on why the dam deregulation is likely to stay dammed. The incentive structure for change is not there, as those with the power have to shoulder none of the responsibility. 

While the above may just be a small story of a small power industry in Sichuan, the power of two "I's", institutions and incentives, should never be underestimated. Failed partial liberalization can corrupts the first; failed regulatory regimes can arise from the bad design of the second. This process is incredibly important for the design of microeconomic issues, and appears to be a fruitful investigation for the future.

Housing Equity as Price Regulation

Price ceilings with Chinese (housing) characteristics

Chinese housing prices are still very high. For as much as there's concern that the whole situation may implode, Shanghai housing prices are still in the neighborhood of 20,000 yuan per square meter. A recent high-end Beijing housing development, Ten-Thousand Willows, is shooting for about 24,000 yuan per square meter. Guangzhou just had an auction to build housing at a floor price of 32,967 yuan per square meter. In one day, an auction company sold three parcels of land, and each sale beat the previously set record. One day. Three sales. Three new records. The government response? It censured the auction firm "promoting incorrect market expectations." With this rapid growth in housing prices, the Beijing government is trying to take action to limit the price competition for the Ten-Thousand Willow land purchase. Its tool of choice? A price ceiling on the bidding prices for land, coupled with competition on the basis of housing equity.

How does this work? First, the government sets a price ceiling, which is not yet publicly disclosed, on the maximum bid that residential development companies can bid. Firms have no obligation to bid up to this price, but they can bid no more than the ceiling. Beyond this price, companies can compete in a different dimension: they can sell housing equity back to the government. Ideally, the land bids won't go that high, but the government is ready to clamp down on price growth once it hits the ceiling. At the ceiling, a company can offer to sell "repurchase housing" back to the government at about 10,000 yuan per square meter, less than half of the market price. In effect, instead of bidding more for the land, the government is forcing firms to sell equity call options to compete. The government can use these houses to provide low cost lodging to the poor, or it could even sell the houses later to fund welfare programs or state owned enterprises.This move has forced companies to re-evaluate their bids as they need to adapt to a new system that, critically, changes the way volatility and price growth affect company and government finances.

If housing prices continue to rise, repurchase housing will cut hard into company profits. The companies are losing a large part of the return on the land bid. For the government, this is also a very good deal to make sure housing prices don't rise too fast. First, the ceiling allows the government to signal their commitment to lowering prices. Then, if housing prices are still going to rise and the land is still worth more than the ceiling value, the government can capture some of the upswing through equity call options. This also arguably prevents "fragility exporting" in the system, as the companies get less of the upside risk while still bearing the downside risk. The government also gets some upside risks while still having to deal with the possible costs of future stimuli to ease a Chinese housing contraction. I still don't have faith in Chinese housing policy, and I'm concerned how this game of musical chairs will play out. However, the use of a price ceiling coupled with call options strikes me as a particularly innovative way to limit price growth. Capitalism with Chinese characteristics indeed.

Monetary Policy and Inequality

The recent Stiglitz commentary on monetary policy and inequality has provoked several responses from Karl Smith and Tyler Cowen. I find their responses to be broadly correct, and get to the fact that monetary policy is not supposed to be a panacea; rather, it helps restabilize conditions to allow other adjustments to be implemented with less pain. Given Evan Soltas' recent post on monetary policy and poverty, it seems dubious that monetary policy, in and of itself, is the root cause of inequality and the skyboxing of society.

Funnily enough, as I was flipping through new NBER working papers, there was a paper testing the empirics of the monetary policy-inequality hypothesis. What do the authors find?

We study the effects and historical contribution of monetary policy shocks to consumption and income inequality in the United States since 1980. Contractionary monetary policy actions systematically increase inequality in labor earnings, total income, consumption and total expenditures. Furthermore, monetary shocks can account for a significant component of the historical cyclical variation in income and consumption inequality. Using detailed micro-level data on income and consumption, we document the different channels via which monetary policy shocks affect inequality, as well as how these channels depend on the nature of the change in monetary policy.

Et tu, Stiglitz?

A Currency Union by any Other Name

...still suffers from the same adjustment problems

Recently there's been a spurt of discussion on the part of Simon Johnson and Paul Krugman on Optimum Currency Areas and productivity growth, and Nick Rowe is still a bit confused. Why should productivity growth differentials matter? Nick lists out a variety of thought experiments and finds no satisfactory answer. As he says, "There must be something else. Some other hidden (to me) assumption they are making. What is it?" With some thought, I believe the "something hidden" is something that market monetarists (including Nick Rowe) have written extensively about: nominal GDP.

Why nominal GDP? Because it's the "something" that can link "productivity differentials, current account deficits, and exchange rate regimes".

First, let's talk about the relationship between higher productivity growth and higher nominal GDP. At least within the United States, productivity growth has a strong correlation with nominal GDP growth. A 1 percentage point in YoY multifactor productivity predicts about a 0.66 percentage point increase in nominal GDP growth. This observation is likely compounded by the Balassa-Samuelson effect, which would imply higher inflation in the regions with higher productivity growth. In terms of the AS/AD model, if the AD curve has a price elasticity of greater than 1, positive AS shocks boost nominal GDP. Fiscal policy is not an answer, as politicians are rarely going to pull back during a boom. And if monetary policy intervenes to cool down the growth, this puts the brakes on nominal GDP growth for the rest of Europe, thus compounding the asymmetric shock problem. Either way, a permanent productivity differential can lead to permanent nominal GDP growth differentials, even if the central bank targets mean nominal GDP growth.

Nominal GDP also can help explain exchange rate regimes and current account deficits. As Scott often reminds us, "You decide whether a currency is under or overvalued by looking at whether aggregate demand is at an appropriate level." And what measures aggregate demand? Nominal GDP. Consequently, if we shift discussion to nominal GDP growth rate differentials, we can talk more intelligently about exchange rate issues. The rate of nominal depreciation of one country's currency vis-a-vis another should equal the difference between the first country's NGDP growth rate and the second country's NGDP growth rate. Thus, if one country has lower NGDP growth than the second, the currency of the first should also depreciate. However, this cannot be the case in the present environment. As Evan Soltas has noted, the Euro has recreated the world of the gold standard. The Euro prevents currency depreciation. Instead of depreciating currencies, we have falling nominal GDP. Countries like Spain and Greece are forced to grind through internal devaluations, imposing massive costs on their citizens.

By refocusing on nominal GDP, a lot of the questions Nick and others are asking become much clearer. Why does a permanent differential in productivity growth matter? Because it implies a permanent differential in NGDP growth, which causes pressures for an exchange rate to change. But because the euro pegs the exchange rates all together, the pressure manifests itself as internal devaluation, which carries very severe negative consequences on count of sticky wages/prices and safe asset shortages. Why does a permanent differential in productivity levels not matter as much? Because if productivity growth rates are the same, there is no differential in nominal GDP. Therefore there's no pressure for currency adjustment. Productivity growth may not force trade deficits, but it can still cause nominal GDP differentials.

Why do fiscal transfers matter? Because in a gold standard world, fiscal transfers between regions (countries) can affect regional NGDP growth. Fiscal transfers are a crude way of "taking" aggregate demand in one region and putting it in another. Fiscal transfers do what domestic central banks can not; stabilize country level NGDP growth. This is politically justifiable if shocks are temporary and distributed among the countries. But when one country has permanently higher real growth and nominal GDP growth, that country will be permanently paying transfers to the others. This is the political problem to which Krugman and Johnosn refer. These permanent NGDP differentials necessitate a one-sided transfer union, which we do not have. As a result, we see the painful process of internal devaluation in periphery countries.

A narrative in terms of nominal GDP also subsumes discussions about unit labor costs. By the New Keynesian business cycle model, lower nominal GDP growth rates arise because of higher real wages or labor costs. So the lower nominal GDP growth in the periphery raises the real wage, rendering the periphery uncompetitive. This is then a more concrete reason why internal devaluation is the only option in a world without transfers or national currencies. Unit labor (and capital) costs need to adjust.

We can now try to answer Nick's thought experiment on a country filled with both low-productivity "Greeks" and high-productivity "Germans", If you separated them, you would observe that the Greeks would have lower growth in nominal production relative to the Germans. This would manifest itself in a growing income gap over time. But what keeps countries together? First, there's "labor mobility". There's no reason  why the Greeks have to stay Greek; maybe they can intermarry and become high-productivity Germans. Second, there's "transfer payments", along the lines of social welfare. High productivity Germans are taxed to pay transfers to the low productivity Greeks. And as in the currency union, the Germans will likely complain about having to subsidize the laziness of the Greeks. If the Greeks can't fight for these policies in the sober halls of Parliament, they can threaten an "exit".This is the basic story behind nationalist secession.