The breakdown of the Philips curve relationship between inflation and unemployment has been among the major casualties of recent macroeconomic tumult. It has led to demands that central banks replace the current New Keynesian (NK) models involving a demand equation, a policy rule and a Phillips curve to calculate interest rates.
Over the past few years, Roger Farmer and co-authors have shown that an alternative, the Farmer Monetary (FM) Model, which replaces the Phillips curve with a new equation, the belief function, outperforms the NK model by a large margin when used to forecast the US data for the period from 1954-2007. He writes,
The belief function captures the idea that psychology, aka animal spirits, drive aggregate demand. It is a fundamental equation with the same methodological status as preferences and technology. To operationalise the belief function, we assumed that people make forecasts of future nominal income growth based on observations of current nominal income growth... Conventional dynamical systems have a stable steady state that acts as an attractor. The economy will converge to that steady state, no matter where it starts. The FM model does not share that property. Although the economy follows a unique path from any initial condition, the FM model has a continuum of possible steady states and which one the economy ends up at depends on initial conditions. The FM model explains the data better than the NK model because the unemployment rate in US data does not return to any single point...
The unemployment rate, the inflation rate and the interest rate are so persistent in US data that they are better explained as co-integrated random walks than as mean-reverting processes. The FM model captures that fact. The NK model does not. What does it mean for two series to be co-integrated? I have explained that idea elsewhere by offering the metaphor of two drunks walking down the street, tied together with a rope. The drunks can end up anywhere, but they will never end up too far apart. The same is true of the inflation rate, the unemployment rate and the interest rate in the US data... the NK model is wrong and there has been no stable Phillips curve in the data of any country I am aware ever since Phillips wrote his eponymous article in 1958.
If the recent Peterson Institute conference on Rethinking Macroeconomic Policy is any indication, Farmer and Co may have more work to do. As Matthew Klein writes in FT, the conference co-Chair, Olivier Blanchard, reaffirmed his faith in the Phillips Curve,
I have absolutely no doubt that if you keep interest rates very low for long enough the unemployment rate will go to 3.5, then 3, then 2.5, and I promise you at some point that you will have the rate of inflation that you want.
Translation - keep the monetary accommodation on and unemployment rates will keep going down till they stoke off inflationary pressures "at some point"! Coming as it does from someone who was till recently the Chief Economist of IMF, this is a staggering level of obduracy. And he was leading a conference on "Rethinking Macroeconomic Policy"!
Klein decomposes the Blanchard view in terms of four propositions - jobless rate influences wage bargaining; workers bargain over nominal and not real incomes; workers spend extra money on consumer products, driving up prices; and businesses in the aggregate can raise pay or hike prices. The last three, especially the last, stand on tenuous foundations and limited empirical evidence.
Thankfully, the rest of the conference appears to have been more receptive to change. The most useful reminder for macroeconomists came, as usual, from Dani Rodrik,
The ratio of redistribution to efficiency gains is not only very large, it rises to ridiculous heights as the tax/policy distortion that is removed gets smaller.
He shows that redistribution per dollar of aggregate gain (in favour of the well-off or entrenched) increases sharply as the tax and tariff rates get ever smaller.
Jason Furman called for an end to the debate on whether inequality is good or bad for growth and on its impact on the average of incomes, both irrelevant for policy makers. Instead, he proposes more attention on specific policies that increase or decrease inequality and their impact on indicators that reflect more broader social welfare functions than simple averages (like the impact on specific population categories). His suggestion for developed and developing countries,
In advanced economies a lexicographic framework that focuses exclusively on distributional analysis and then only to growth when the distribution of different policies is the same is generally likely to be appropriate under a broad range of social welfare functions. This is because the distributional effects of many policies are orders of magnitudes larger than the growth effects. In developing economies, however, the scope for policy- and institutionally-induced variations in growth rates is much larger and thus the lexicographic approach is unlikely to be as widely appropriate.
Gita Gopinath puts to rest the orthodoxy about the superiority of freely floating exchange rates and argues in favour of managed floats for emerging market (EM) economies. Channeling BIS economists and Helene Rey, who claim the impossibility of monetary policy independence irrespective of the exchange rate regime, once capital flows are allowed, Gopinath points to the recent work of Obstfeld and Co which shows the persistence of an attenuated version of the trilemma. They show that for EM countries the correlation of a bunch of macroeconomic indicators and global investor risk aversion is lowest for those with intermediate exchange rate regimes.