1. Putting The Pieces Together: Monetary Policy and Supply Shocks Recall two tools we used when discussing the production of real goods and services, Okun's law: (a) AU = -a(g - b) and our accounting identity linking employment, output, and productivity growth: (b) %Aemployment = %output - %Aproductivity Also recall what the parameters (a and b) in Okun's Law are meant to reflect. A major focus of macroeconomic policy debates about the 1990's was the period's unusually strong growth in labor productivity. (It might be helpful to go on FRED and look up unemployment rates and inflation rates over this decade!) Describe how conventional monetary policy would respond to this unexpected rise in productivity. That is, what are first the effects of rising labor productivity, what actions would the central bank take in response, and what would the effects of those actions be? Then, based on your answer, how effective do you think monetary policy is expected to be in short-run terms versus long-run terms?A linear equation is one in which the variables are only added or subtracted. None of the variables are multiplied by each other, and none are raised to a power (that is, there are no expressions like 2?). Linear equations are generally easier to work with, so it's convenient to be able to change other kinds of equations to linear ones if possi- ble. One useful tool for making linear equations is: If a = b a: c then percentage change in a 2 percentage change in b + percentage change in c (1) Similarly, if a = b/C then percentage change in a a percentage change in b percentage change in c (2) The Greek letter A (delta) is often used to mean the change in a variable. 50 to save space, I will write \"7.11). when I mean "percent change in...\" For example " %A employment" means "percent change in employment." 50 we can rewrite Equation 1 as: %Aa \"A: 90A!) + 9036' This is linear - the variables are simply added. Whereas a = b a: c is not linear, since the variables are multiplied. Note that the original equation described the levels of the variables, while the new, linear one describes the changes in them. Labor productivity is defined as output divided by employment. When economists talk about "productivity", they mean either labor productivity or total factor productivity. Labor productivity is the output produced by a given amount of labor; total factor productivity is the amount of output produced by a given amount of labor and capital. Total factor productivity is important for economic theory, but it is hard to apply in practice, since measuring capital is difficult and you need to make additional assumptions about how the labor and capital are combined. For most practical purposes, labor pro- ductivity is more relevant. Whenever I refer to "productivity" here, I mean labor productivity. Labor productivity is defined as output divided by the amount of labor used. Labor can be measured either in hours of work, or number of people employed. Here we will measure it by number of people employed. So labor productivity is defined by: productivity = output employment (3) We can measure productivity for the economy as a whole, for an industry or sector, or for a single business. If we are measuring it for the economy as a whole, then "output" is GDP; for an industry or business, it is value added. When we are talking about changes in productivity, we normally measure output in real (or inflation- adjusted) terms. We can rearrange Equation 3 to get Figure 1: US labor productivity growth rates, 5-year moving averages. In recent output = employment * productivity years, productivity has grown at less than 1.5 percent per year, which is lower than in much of the postwar In other words, total production in an economy (or an industry period. or business) is equal to the number of people employed, times the average amount produced by each one. The change in employment over some period of time is equal to the change in output minus the change in labor productivity. We can analyze changes in employment in terms of changes in out- put and productivity. Using Equation 2, we can write: % Aemployment ~ % Aoutput - %Aproductivity (4)The percent change in employment is equal to the percent change in output minus the percent change in employment. For example, in 2014, total employment in the US rose by 2.2 percent, output rose by 2.9 percent, and productivity rose by o.7 percent. (This is an excep tionally low rate by historical standards.) We can apply this equation to a change over one year or over several years. But if we apply it to a very long period (say, 50 years), the approximation may be less accurate. Equation 4 is an accounting identity: it is true by definition. But it still shows us a couple of things that might not be obvious. First of all, changes in employment can be due to either changes in total output, or to changes in productivity - that is, either changes in how much is produced, or in how much labor is used for a given amount of production. Over short periods, changes in output growth are much more important. For example, Table 1 shows the average annual change in employment during the expansion of 2002-2007 and the recession of 2008-2009- Period Employment Output Productivity Table 1: Average Annual Change in 2002-2007 0.8% 2.7%% 1.9% Employment, Output and Productivity 2008-2009 -3.1% -1.5% 1.6% Employment grew at an average rate of o.8 percent per year over 2002-2007, and fell at a rate of 3.1 percent per year during 2008-2009. This difference is entirely explained by the fact that output was rising during the first period, and falling in the second period. As you can see, labor productivity actually grew somewhat slower during the recession than during the expansion, but the change is quite small compared with the changes in output and employment growth. Over long periods, faster labor productivity growth could contribute to slower employment growth. This is called "technological unemployment," but it is not clear that it is a real problem. Over longer periods, however, changes in the speed of labor pro- ductivity growth may be more important. The second thing that Equation 4 tells us is that if output is growing at a constant rate, than faster productivity growth must men slower growth in employment. If productivity grew fast enough, you might even see a situation where output continued to grow while employment fell.Output and employment are linked via Okmr's law. Okun's law says that when output grows rapidly, unemployment will fall, and when output grows more slowly or falls, unemployment will rise. The exact relationship varies between countreis, but within countries it seems to be quite stable over time. If we write the change in unemployment as All and the real {ination-adjusted) growth rate of output as 3, then for the US Okun's law is: All = 0.5(g 2.5) In other words, the change in unemployment is equal to negative 0.5 times the percentage growth rate minus 2.5. So it takes around 2.5 points of real GDP growth to hold unemployment constant. For example, i! GDP grew by 4.5% in one year, we would expect the change in the unemployment rate to be 0.5[4.5 2.5) = D.5 a: 2 = 1 we would expect unemployment to fall by one point. On the other hand, if real GDP were to fall by one point, we would expect the change in unemployment to be 0.5(i 2.5) = 0.5 i- 3.5 = 1.75 we would expect the unemployment rate to increase by 1.75 point. Note that this equation doesn't say what the change in the un- employment rate (All) or the growth rate (3) actually are. Rather, it describes a function linking the two. It says that if growth is high, unemployment is probably falling; and If growth is low or negative, unemployment is probably rising. So if you have an idea about what will happen to one of the variables, you can make a good guess about what must happen to the other. The numbers that appear in a func- tion like this are called its parameters. In the case of Okun's law, while The overwhelming consensus among economists and policy- makers today is that the macroeconomy is not stable. Economists disagree on many questions. But only a tiny minority believe that economic outcomes would stay within acceptable bounds without a central bank actively managing the availability of credit. While very few economists believe that full employment and price stability are possible without active management of the economy by central banks,there are more economists, especially since 2007, who believe that the tools normally used by central banks are inadequate for this purpose. One concern is that central banks cannot reliably control the terms on which banks lend to the private sector. Another is that interest rates don't have a strong enough effect on business investment. A third concern is that central bank intervention may do more harm than good. This concern is based on the idea, expressed by Milton Friedman among others, is that there are "long and vari- able lags" in the effects of _ As a result, by the time the central bank's actions inuence the real economy, conditions may have changed so much that the bank may be pushing in the wrong direction. In this view, the negative feedback loop from investment, to output, to inflation, to the interest rate, to investment, produces cycles rather than convergence to equilibrium. In the US, the Federal Reserve tends to follow a policy rule called the Thylar rule, which gives equal Wight to divergences of ination