Federal Reserve officials are sometimes asked how monetary policy can help solve regional economic problems. The standard answer is straightforward: There is only one national monetary policy, and it is not designed to address purely regional issues. This does not mean, however, that monetary policy does not affect some regions of the country more than others. Business people, civic leaders, and government officials may want to know how much their region will be affected by changes in monetary policy relative to the rest of the country. We know that business cycles differ across states and regions, and over the past decade, a number of studies have examined what vole monetary policy may play--i.e., how monetary policy may affect regions differently and why. A review of these studies reveals that certain parts of the country are consistently more affected by monetary policy than others. So far, the only convincing explanation for these differences is the different mix of industries in the regions. But the search for other reasons is likely to continue. Identifying the reasons for regional differences in the effects of monetary policy may help us better understand how changes in monetary policy ripple through the economy. This article will review where the research has brought us so far.
BUSINESS CYCLES DIFFER ACROSS STATES AND REGIONS
It is widely recognized that there are differences in business cycles across states. In some cases, it is the depths of the recessions, and in others, it is the timing of recessions. Differences in cycles across multi-state regions in the U.S. are less pronounced than differences across individual states, but they are still discernible.
Two recent studies have used a newly developed set of coincident indexes for the 50 states to define and compare state recessions. In an earlier Business Review article, I used these indexes to examine recessions at the state level based on the traditional definition of a recession--a significant decline in economic activity that lasts for several months. Using the same set of indexes, in a second study, economists at the St. Louis Fed applied a standard technique, known as a Markov switching model, to identify different phases in each state's economic cycle. Both articles find that the 50 states have experienced different business cycles in terms of their number, timing, and severity.
Other studies have examined the issue from a different perspective. How closely are the cyclical movements in income or employment correlated across the states? In a study published in 2001, Christophe Croux and his co-authors proposed a new statistic, called a cohesion index, which measures the co-movement of regional economies over the business cycle. They apply the measure to personal income in the 50 states and find that while the correspondence among the states is higher than the correspondence among the European countries, it is not perfect. In a 2004 article, Gerald Carlino and Robert DeFina calculate the same statistic for employment in eight major industry groups across 38 states for which data are available. A value of one would indicate a perfect correlation of industry employment by state across business cycles. Thus, for an industry with a cohesion index of one, quarterly increases and decreases in employment due to the business cycle would be proportional across all the states. (1) The cohesion measures in the study range from 0.82 for manufacturing to 0.44 for mining. Thus, business cycles for the major industries differ across the states. The co-movement of income or employment among multi-state regions is stronger than the co-movement among the states, but again, it is not perfect. (2) In effect, grouping states together smooths out some of the individual features of business cycles, but it does not eliminate them.
Since business cycles differ across states and across regions in the U.S., it is natural to ask whether differential effects of monetary policy are a factor. Answering this question requires a consistent framework to measure the effect of monetary policy on the economies of states or regions.
ESTIMATING THE REGIONAL EFFECTS OF MONETARY POLICY
In recent years economists have turned to econometric models known as vector autoregression (VAR) models to measure the effects of changes in monetary policy on states and regions. A VAR is a system of equations for estimating the historical relationship between a variable, such as personal income in a region, by past values of that variable and by current and past values of other variables, such as the short-term interest rate targeted by the Federal Reserve (the fed funds rate). Using this type of model, we can estimate the effect of an unanticipated change in the fed funds rate on income in a state or region. These effects are known as impulse responses. Of course, the estimates will differ depending on what variables are included in the model and what assumptions are made. For example, do changes in monetary policy affect income in the current period or only in later periods? And do shocks to one region's economy spill over directly to the economies of other regions?
The recent studies differ some-what in their assumptions. But all of the studies include in their models three key variables: personal income in each region, the fed funds rate, and some measure of oil prices or commodity prices in general. Some of the models add other variables to this list, such as the rate on 10-year Treasury bills. In each study, the regional effects of monetary policy are measured by the response over time of the region's personal income to an unanticipated change in the fed funds rate. All of the models assume that unanticipated changes in the fed funds rate affect personal income with a lag of at least one quarter.
Ideally, we would like to estimate the effects of monetary policy on each of the 50 states in a single model. But VAR models are suitable only for a limited number of variables, not the 50 plus …