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The origin and diffusion of shocks to regional interest rates in the United States, 1880–2002
Explorations in Economic History Explorations in Economic History 44 (2007) 487–500 www.elsevier.com/locate/eeh
The origin and diffusion of shocks to regional interest rates in the United States, 1880–2002 John Landon-Lane, Hugh Rockoff
*
Department of Economics, Rutgers University, 75 Hamilton Street, New Brunswick, NJ 08901, USA Received 27 July 2005 Available online 13 October 2006
Abstract This paper is about the behavior of regional interest rates in the United States from 1880 to 2002. The main concern is with the shocks to regional rates. Where did they originate? How did they diffuse? How did the pattern change over time? We show that in the late nineteenth century the main source of shocks to rates on the periphery were shocks originating on the periphery itself. This pattern continued through World War I and the Great Depression. After World War II, however, the importance of disturbances on the periphery diminished and shocks to rates in the Eastern financial centers became the main source of fluctuations in all regions. 2006 Elsevier Inc. All rights reserved. Keywords: Monetary union; Monetary policy; Interest rates
1. Introduction A substantial literature explores regional bank lending rates in the United States, including famous contributions by Davis (1965), Sylla (1969), James (1976a,b), and many others. Our work adds to previous work in three ways. First, we extend the regional bank lending rates to the present to provide a longer historical perspective. Second, we focus on the origin and diffusion of short-term disturbances, rather than on the long term trends in *
Corresponding author. Fax: +1 732 932 7416. E-mail addresses: [email protected] (J. Landon-Lane), rockoff@econ.rutgers.edu (H. Rockoff).
0014-4983/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.eeh.2006.08.002
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levels that have traditionally been the focus of the literature. Third, we use vector autoregressions to characterize the relationships among regional rates. We find an interesting pattern. In the late nineteenth century the main source of shocks to rates on the periphery (the Plains, the South, and the West) were shocks originating on the periphery itself. To be sure, shocks to rates in the Eastern financial centers (most importantly, of course, New York) affected the periphery, but shocks originating in the financial centers played a secondary role. Shocks to the core played a more prominent role on the periphery during the disturbed period encompassing the world wars and the depression, but shocks arising on the periphery itself remained important during this period. This is rather surprising because most students of the American capital market thought that regional markets had been fully ‘‘integrated’’ by 1900 or shortly afterwards. After World War II, however, shocks hitting rates in the eastern financial centers dominated rates in all regions. Our work was motivated by an attempt to understand the significance of the American experience for the debate over monetary unions. In popular discussions advocates of monetary unions often hold up the United States as a successful example. After all, the United States has had a single currency since 1790, with the exception of the Civil War era; the United States consists of several distinct regional economies; and the United States has prospered. In practice, of course, it is extremely hard to determine what the net contribution of our monetary union has been. There are, of course, many potential advantages and disadvantages for regions that unite to form a monetary union. The advantages include, but are not limited to, the reduction of transaction costs among members of the union and the increased flow of information about prices. The disadvantages include, but are not limited to, the difficulties encountered by the monetary authority in attempting to formulate a policy that is satisfactory for disparate group of regions. Our work addresses only this last issue. Our results also shed some light on American monetary history. The Federal Reserve Act included, at the insistence of the Wilson Administration, a considerable degree of decentralization. The Act created the separate district banks that we are familiar with, which could set their own discount rates. Our results help us understand the problems that the policy makers were trying to address: the regions of the United States at this time, we show, were being hit by region-specific as well as national shocks which would have made it difficult for a central monetary authority to achieve its policy goals with a single policy instrument. Here is the roadmap. Section 2 reviews the attempts by a number of scholars to analyze the U.S. monetary union and the relationship between our work and this literature. Section 3 takes a preliminary look at the data. Section 4 formally looks at the regional and national shocks hitting the financial markets from 1880 until 2002. These shocks are identified, and their relative importance over time are explored, using a set of vector autoregressions. Section 5 summarizes our results and suggests some implications. 2. The United States as a monetary union The importance of the United States as an example of a monetary union was recognized in the earliest papers that wrestled with these issues. In his famous essay ‘‘The Case for Flexible Exchange Rates’’ Milton Friedman (1953, p. 123) argued that although flexible rates were better between the United States and other nations, fixed rates were better within the United States. High mobility of goods, labor, and capital within the United States, according to Friedman, explained ‘‘why a central monetary authority is able to operate
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without producing serious sectional strains.’’ On the other hand, Robert Mundell (1961, p. 658) in his famous essay ‘‘The Theory Optimal Currency Areas’’ pointed to the United States as an example of a monetary union that had suffered from interregional balance of payments problems. A number of economic historians have explored the effectiveness of the U.S. monetary union in more detail. Rolnick et al. (2003) and Grubb (2003) explored the adoption of the U.S. dollar during the Revolution and under the Constitution. The subsequent development of the U.S. monetary union under the Second Bank of the United States has also drawn considerable interest. Fraas (1974) argued that the Second Bank of the United States established an effective monetary union in the United States in the 1820s, but that in the process of doing so it limited the supply of capital and the rate of growth on the frontier. Knodell (1998, 1988), focusing on inland exchange rates, also argued for an important role for the Second Bank in establishing and maintaining an effective monetary union. Se´ne´gas (2000) explored the convergence of regional inflation rates under the Second Bank. Shambaugh (2006) provided evidence that inland exchange rates were rather loose in the antebellum U.S. and evidence that some states deliberately tried to make use of the resulting monetary independence. Sheridan (1996) and Rockoff (2003) drew attention to the regional monetary conflicts during the postbellum era. The literature we have just cited deals explicitly with the United States as a monetary union. There is another literature that deals ostensibly with a different set of issues, but which we believe should be of interest to students of monetary unions. This is the large literature that focuses on regional interest rates in the United States. This literature can be traced to the classic paper by Davis (1965), although one could also cite R.M. Breckenridge (1898), which is still well worth reading. More recent contributions, just to mention a few of our favorites, include Sylla (1969), James (1976a,b), Smiley (1975), Keehn (1980), and Bodenhorn and Rockoff (1992). The main focus of this literature has been on how long it took regional interest rates to converge. However, this literature also provide important data and interpretations that can be used to enrich our understanding of the U.S. monetary union because interest rates are a sensitive indicator and (at times) instrument of monetary policy. Our work uses the data developed in the literature on regional interest rates to explore two issues that need to be addressed when we think about the United States as a monetary union. The first is can one observe now, or in the past, independent shocks to interest rates on the periphery? If there were independent shocks on the periphery, then it means that there were underlying shocks to the supply or demand for credit on the periphery that were not absorbed immediately by the national capital market. The presence of independent shocks on the periphery raises the question of whether a central monetary authority would need multiple instruments to reach its policy goals thus making the coordination of monetary policy more difficult. This leads to our second question. Did the shocks to the periphery become more congruent with the shocks hitting the core over time and did a single monetary instrument appear that could be used by the central monetary authority to enact its monetary policy goals? In other words, how long did it take for the capital market to reach the point where there was a single national shock that dominated the regional shocks? Our attempts to answer these questions are not definitive. In some cases alternative explanations of the data are possible. We believe, however, that we can shed some light on these questions by exploring the long-term behavior of regional bank lending rates.
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3. Regional bank lending rates For regional interest rates we used estimates from Bodenhorn (1995) for the period 1880–1960 and estimates that we have constructed based on data reported by the Federal Deposit Insurance Corporation for the period after 1960. As far as we know, this is the first study of regional rates to use time series covering the whole of 1880–2002. The appendix presents the data and discusses it in more detail. Fig. 1 plots the bank lending rates for each region and the commercial paper rate for the period 1880–2002 and the Federal Funds rate from 1955 to 2002. In the postwar era the regional bank lending rates are clearly moving together. In the late nineteenth century, however, the picture is far messier. But the nature of the problem in the early period is far from obvious. Was it merely a problem of lags? Or were shocks originating in different regions? The behavior of lending rates during the contraction from 1929 to 1933, is especially perplexing: rates in the Plains and the West rose, even though rates in the Northeast fell. This is unexpected because even the most pessimistic historians of the integration of U.S. financial markets have argued that the process was completed early in the twentieth century. Fig. 2, which uses data from Smiley (1981) for the interwar years, provides further clarification. Smiley’s data is available for a shorter interval than Bodenhorn’s, but available for more regions and semiannually. The figure shows the commercial paper rate; the lending rate for banks in New York City; the lending rate for an average of all banks except those in New York City and Chicago; and the rates in two extreme cases, New England city banks and the Southwest country banks. Lending rates at New York City and New England city banks appear tied together. Their rates reflect, in part the movements in the commercial paper rate. The average for all banks, however, shows that in much of the country the response was muffled. It is possible that as the economy declined banks viewed the risk attached to their average loan rising, so that risk-adjusted rates may have been falling more rapidly. In any case, it is likely that the typical bank borrower did not benefit from the rapid decline in rates in the financial centers. The rate for the Southwest country banks, the other extreme case, seems to be following the beat of a different drummer, rising when other rates were falling. There is an old adage ‘‘When Wall Street 18 16 14
percent
12
Commercial Northeast Plains South West Fed Funds
10 8 6 4 2 0 1880
1900
1920
1940
1960
Year
Fig. 1. Interest rates 1880–2002.
1980
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10 9 8 7 6 5 4 3 2 1 0 1926
New York City New England City Average Commercial paper South West Country 1928
1930
1932
1934
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Fig. 2. Regional bank lending rates, 1926–1937.
Sneezes, Main Street Catches a Cold.’’ But when it came to interest rates, the opposite was true: ‘‘When Wall Street Caught a Cold, Main Street Sneezed.’’ Looking at the rates over time is instructive and reveals some interesting divergences in the reactions of rates on the periphery compared with rates in the core. However, looking at these simple co-movements between rates during selected periods is simply scratching the surface. With this approach one cannot differentiate between movements that are due to one core shock operating through a complex dynamic system that affects regions differently, or multiple regional shocks hitting at the same time. What we need to do is identify and separate the peripheral shocks from the core shocks, analyze how these shocks reverberate through the system, and measure their relative influence on the observed rates. In the next section we do this by using the simple but powerful vector autoregression modeling approach. 4. Formal analysis of the sources of interest rate shocks In this section we use the standard vector autoregression (VAR) machinery—impulse response functions and forecast error decompositions—to explore the impact of the shocks hitting the core interest rates and the peripheral interest rates for various sub-periods of our sample. In later periods the core shock can be interpreted as a monetary policy shock. In the earliest period the core shock can be interpreted as the shock that a monetary authority, had it existed, would have had some influence over. This section presents a summary of our main findings, with an emphasis on the historical forecast error variance decompositions. The full details, including the estimation results, the impulse response functions and a description of the data and its sources can be found in the appendix. The set of interest rates modeled included each of the regional bank-lending rates and a ‘‘national rate,’’ the latter being a potential (nineteenth century) or actual (post World War II) instrument of monetary policy.1 We divided the sample period, based on our 1 The national rates that we use are the 3 month New York commercial paper rate, obtained from the Global Financial Database (http://www.globalfindata.com), and the federal funds rate obtained from the FRED II database (http://research.stlouisfed.org/fred2/).
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reading of the historical literature, into three segments: 1880–1913, 1914–1943, and 1955– 2002. This division, we believe, would appear natural to most financial historians. The first segment extends from the start of our data in 1880 to 1913 when the Federal Reserve was established. The second segment includes the disturbed middle decades of the twentieth century: The two world wars and the Great Depression. The last segment begins in 1955, when our data on the Federal Funds rate begins. Given the relatively small samples, we wish to model the data using as parsimonious a time series model as possible. For (covariance-) stationary time series we know that there is a fundamental vector MA(1) representation of the vector of time series and under some regularity conditions this representation can be well approximated by a low-order VAR(p) model. Let yt = (y1t, . . ., ynt)0 represent the vector of covariance-stationary time series to be modeled. Then the (reduced form) VAR(p) model is yt ¼ l þ
p X
Bj y tj þ t ;
ð1Þ
j¼1
where N(0, R) and the coefficients l, Bj, and R are n · 1 and n · n matrices, respectively. If the times series are not stationary then we need to transform the data to make them all stationary first and then model the stationary data appropriately. If the data are all integrated of order one, I(1), then the appropriate transformation is to take the first difference of the data. The next problem we have is to determine how to appropriately model the differenced time series. If the data are cointegrated, that is if there exists a linear combination of the levels of the time series that is stationary, then the appropriate model to use is a vector error correction model (VEC) which is Dy t ¼ c þ ab0 y t1 þ
p1 X
C j Dy tj þ t ;
ð2Þ
j¼1
where the coefficients c, a, b, and Cj are n · 1, n · k, n · k, and n · n matrices, respectively, with k representing the number of cointegrating relationships between the time series that make up yt. If the data are not cointegrated then the appropriate model to use is a VAR in the first differences of the data (DVAR), which is Dy t ¼ c þ
p1 X
C j Dy tj þ t ;
ð3Þ
j¼1
where c, and Cj are n · 1 and n · n matrices, respectively. Hence, before we identify structural shocks for each period of our sample we need to (1) test for non-stationarity of the individual time series, (2) if we find the series to be non-stationary we then need to test whether they are cointegrated, and then (3) determine the appropriate order of the VAR/VEC/DVAR to estimate. We describe in more detail the tests used for (1–3) above and the results below but first we describe how we identify the structural shocks from each of the models described above. As it is a straightforward extension to construct structural shocks for models (2) and (3) with identical implications to structural shocks for model (1) we will concentrate on describing the process of identifying structural shocks for the levels VAR given in (1). The VAR described in (1) is the reduced form version of a structural VAR of the form
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~þ A0 y t ¼ l
p X
Aj y tj þ ut ;
493
ð4Þ
j¼1
where the structural error, ut, consists of a set of orthogonal shocks such that E(utu0 t) = In for all time periods. These shocks have a structural interpretation whereas the shocks that hit the reduced form version, (1), are non-linear combinations of the structural shocks and have little structural or economic interpretation. In fact there is a one-to-one relationship ~, Bj ¼ A1 between the parameters in (1) and the parameters in (4) given by l ¼ A1 0 l 0 Aj for 1 j = 1, . . ., p, and t ¼ A0 ut . The parameters of (1) can be consistently estimated by maximum likelihood methods but the above set of non-linear equations cannot be solved for the parameters of (4) without making some identifying restrictions. Our identifying restrictions focus on the contemporaneous impact matrix, A1 0 . We assume a recursive relationship between the variables of our model by imposing the restriction that the contemporaneous impact matrix is lower triangular. This allows us to exactly identify the parameters given in (4) and so allows us to identify a set of orthogonal structural shocks that hit the financial system during our sample. One implication of this identification scheme is that our results are, potentially, going to be sensitive to the ordering of the time series in yt. The ordering we used is as follows: (1) the national rate—the commercial paper rate or the Federal Funds rate, (2) the Northeast rate, (3) the Plains rate, (4) the Southern rate, and (5) the Western rate. This ordering was dictated partly by our concern with monetary policy: by ordering the monetary policy variable first we are assuming that the monetary authority is forward-looking. This is opposed to being reactive if we were to order the monetary policy variable after any of the regional rates. The first of the regional rates is the Northeast regional rate. This region contained the eastern financial centers: New York (by far the most important), Boston, and Philadelphia. The order to be chosen for the peripheral regions is less clear-cut. The order we usually worked with was (after the Northeast) the Plains states, the South, and the West. This ordering reflects a nineteenth century view of things; today we would be more likely to put the West second and, perhaps, the Plains last.2 Given our ordering of the variables, the identifying restrictions imposed means that we can interpret the shocks in the following way: the national shock is the shock that hits the commercial paper rate or the Federal Funds rate. This shock is likely to contain shocks to the commercial paper rate or the Federal Funds rate that are national in origin and certainly for the Federal Funds rate should include monetary policy shocks. The Northeast shock is the component of the northeast residual that is orthogonal to the national shock, that is, the Northeast shock consists of shocks hitting the Northeast regional bank rate that are not monetary policy shocks or national shocks. The Plains shock is the component of the plains residual orthogonal to both the national shock and the Northeast shock. The South shock is the component of the South residual that is orthogonal to the National, Northeast, and Plains residuals. Finally, the West shock is the shock that hits the West that is orthogonal to all the other shocks. 2
While we report results for the ordering in the periphery of the Plains first, the South second and the West last we have done a sensitivity analysis by permuting these last three regions in the VAR. In all cases, the national rate is ordered first and the Northeast rate is ordered second. The results, available from the authors upon request, show that the results presented are not sensitive to the ordering of the variables.
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4.1. Unit root and cointegration tests Before we estimate our models for each sub-period we tested each interest rate time series for the presence of a unit root and if we find that all the series contain a unit root we then tested for cointegration. We perform a battery of unit root tests for each interest rate series. The tests used were the standard augmented Dickey Fuller (ADF) test (Dickey and Fuller, 1979), the GLS de-trended version of the ADF test (ADF-GLS) (Elliott et al., 1996), and the ADF with a structural break (Perron, 1989). All these tests have as their null hypothesis the hypothesis that the time series contains a unit root. Given the small sample size and the fact that these unit root tests are known to have small power we also performed the unit root test suggested by Kwiatkowski et al. (1992), otherwise known as the KPSS test. This test has as its null hypothesis the hypothesis that the time series do not contain a unit root. The overall result was that we could only reject the hypothesis that the interest rate series contain a unit root in the first sub-period. On economic grounds this seems reasonable. In this period real rates would be anchored by the productivity of capital and time preference, and the inflation premium would be held in check by the gold standard. However, in the postwar era when the inflation premium becomes an issue, a unit root in nominal rates would be more likely. The results of these tests suggest that we can estimate a VAR in levels for the first subperiod but for the second and third sub-period we need to estimate a VAR in differences. This brings into play the issue of co-integration. If there is co-integration present the appropriate model to estimate is the Vector Error Correction model which is just the DVAR (VAR in differences) with error correction terms added to each equation. To test for cointegration we used the method of Johansen (1988, 1992) using the sample size corrected critical values suggested by MacKinnon et al. (1999). The detailed results of these unit root and cointegration tests can be found in Table 1 of the appendix. Our results were that in the second sub-period (1914–1943) there was no evidence of cointegration. In the third sub-period there is evidence of one cointegrating relationship between the interest rates in our model. Given these results we proceeded as follows: (1) For period 1880–1913 we estimated a VAR model in levels, (2) for the period 1914– 1943 we estimated a VAR in first differences, and (3) for the period 1955–2002 we estimated two VEC models, one with the commercial paper rate as the ‘‘national’’ rate, and a second with the Federal funds rate as the ‘‘national’’ rate. 4.2. Model specification The last task left for us to do was to determine the number of lags to include for each model. Given that we have small samples in each of our sub-periods we used an information criterion approach to choose the lag length. We used the Schwarz Bayesian Information Criterion (SBIC) to choose the lag length for each sub-period as this information criterion consistently estimates the correct lag length if the true model is a VAR and does so for stationary and non-stationary models alike. In all cases, the SBIC was minimized for p = 1. A DVAR and a VEC with one lag included are equivalent to a levels VAR with two lags included. To be consistent across sub-periods we checked whether a VAR(2) in levels was appropriate for the first sub-period by performing a likelihood ratio test of whether the second lag of the endogenous
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variables are jointly 0 across all equations of the VAR. The result of this test was mixed in that the p-value of the likelihood ratio test was 0.06. Thus we could not reject the hypothesis that the coefficient matrix for a VAR(2) at the 5 percent level but we could reject at the 10 percent level. Given the small sample size and the fact that the penalty for adding extraneous variables is only a loss in efficiency compared to the penalty for omitting a relevant variable being estimation bias we decided to estimate a VAR(2) model for the first sub-period rather than a VAR(1). Thus we estimate a VAR(2) for the first sub-period, a DVAR(1) for the second sub-period, and a VEC(1) model for the third sub-period.3 4.3. Results All models were estimated using a maximum likelihood estimator. The system wide Wald test that all of the coefficients in each model are jointly 0 can be rejected for all reasonable test sizes. The equation wide Wald tests also show, except for a few equations, that the joint hypothesis that all coefficients, excluding the constant, are jointly 0 can be rejected as well. While there are significant individual coefficients in each model, we find it more instructive to look at the impulse response functions and forecast error variance decompositions implied by our estimates as a check the model’s validity because the impulse response functions and forecast error decompositions have natural economic interpretations. To conserve space we concentrate here on the forecast error variance decomposition results. In our view, these variance decompositions distill the results from the VAR analysis succinctly and in a way that addresses issues that are important to economic historians. The full estimation results and impulse response functions can be found in the appendix. In each of the three sub-periods we computed orthogonalized interest rate impulse response functions according to our identification outlined in Section 4. The impulse response functions show that the national interest rate shock has a positive and significant impact on all regional rates in all periods and that for the second and third sub-period this impact was permanent. This suggests that the markets were integrated in the sense that shocks that occurred in one region reverberated through the system. Shocks to rates in the eastern financial markets did affect rates on the periphery. This is a natural definition of integration, although somewhat different, we should note, than the traditional definition in the literature on regional interest rates that identifies integration with convergence of interest rates in the long run. We also find, for the first and second sub-period, that shocks to the periphery are affecting other rates in the periphery. For example shocks to the South and the West positively affect the Plains while shocks to the Plains appear to positively affect the West. This is an interesting result because it shows that even after the Fed arrived it did not have complete control of regional rates of the sort that we see in the post WWII era. In the third sub-period we found that the only significant impacts of the peripheral shocks are on their own rates. And even in this case the impacts are small relative to the monetary policy shock and appear to be temporary. This is in contrast to the earlier 3
The main result of this paper is robust to the choice of lag length. Using different model selection criteria can lead to different model choices but in all cases that we have tried the main results always hold: the influence of the national shock on the periphery is getting stronger over time and the peripheral shocks have important effects in the first two periods but negligible effects in the last period.
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periods where it appeared that some of the peripheral shocks had significant and long-lasting effects on other rates in the periphery. The crucial question here is does the central monetary authority have access to an instrument that dominates interest rates in the periphery? To get at this question we construct forecast error variance decompositions. The variance decompositions show the contribution of each structural shock to the non-forecastable components of each time series (i.e., the random component of each time series once we account for the trend, level, and the relationship to past values of the series). Variance decompositions are more useful for this purpose than impulse–response functions because variance decompositions reflect the size and frequency of the shocks as well as their impact on other variables as described by the VAR equations. The impulse–response chart does not show the size or the frequency of the shocks, but merely what the effect of a standardized shock would be. The forecast error variance decompositions are reported in detail in the appendix. The National shock contributes by far the largest amount to the FEVD of the National rate with the Northeast shock being the next largest contributor. This is true for all sub-periods. Together the two core shocks, as might be expected, contributed almost all of the forecast error variance observed for the core rates. The role of the peripheral shocks is very small with only the Plains and West shocks having some, albeit small, contribution in the first sub-period. A very different story emerges when we look at the FEVD for the peripheral shocks. We report in Figs. 3–5 the contribution of each shock to the forecast error variance for each of the peripheral interest rate in our model. Each line on the sub-figures represents a time period with CR representing the results using the Commercial Paper rate and FF representing the results using the Federal Funds rate. The change in the impact of each shock National Shock 100 80 60 40 20 0
N.E. Shock 100 80 60 40 20 0
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
Plains Shock 100 80 60 40 20 0
South Shock 100 80 60 40 20 0
1 2 3 4 5 6 7 8 9 10
1 2 3 4 5 6 7 8 9 10
West Shock 100 80 60 40 20 0
1880-1913 1914-1943 1955-2002(CR) 1955-2002(FF) 1 2 3 4 5 6 7 8 9 10
Fig. 3. Forecast error variance decomposition: Plains rate.
J. Landon-Lane, H. Rockoff / Explorations in Economic History 44 (2007) 487–500 National Shock
N.E. Shock
100
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West Shock 100 1880-1913 1914-1943 1955-2002(CR) 1955-2002(FF)
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Fig. 4. Forecast error variance decomposition: South rate.
National Shock
N.E. Shock
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West Shock 100 1880-1913 1914-1943 1955-2002(CR) 1955-2002(FF)
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Fig. 5. Forecast error variance decomposition: West rate.
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over time is stark. For the Plains rate (Fig. 3) the impact of the peripheral shocks is quite large in the first period. Over 50 percent of the forecast error variance can be attributed to shocks from the periphery in the first sub-period. In the last sub-period the total impact of all peripheral shocks has fallen to less than 10 percent of total forecast error variance. Moreover, the impact of the core shocks on the forecast error variance on the Plains rate has dramatically increased from less than 50 percent in the early period to close to 90 percent in the third period. The same effects can be seen on the South rate (Fig. 4) and the West rate (Fig. 5). In the South the contribution of the peripheral shocks to the forecast error variance in the first period is large (over 50 percent) while in the second sub-period the contribution is moderately large (around 25 percent). However for the third sub-period the contribution of the peripheral shocks to the forecast error variance is negligible (less than 5 percent). The story for the West is very similar. Over 50 percent of the forecast error variance can be attributed to peripheral shocks in the first and second sub-period for the West rate (Fig. 5). This falls to less than 10 percent for the last sub-period. Thus in the pre-war and interwar years it appears that the core interest rates would not have been a good instrument for monetary policy in that it would have been hard for a monetary authority to use the core rates to counteract shocks in the periphery. This is in contrast to the most recent period where, as might be expected, shocks to the national rate, whether measured by the commercial paper rate or the Federal Funds rate, and the Northeast rate appear to explain most (at least 90 percent) of the variance of the forecast errors for all interest rates. Shocks in the periphery have a very small impact on rates in the periphery and these effects do not linger very long. Thus in the latter period it appears that the monetary authority has access to an instrument that dominates rates from all regions of the country. The results from the impulse response functions and the variance decompositions point to the emergence of a strong central instrument that dominates regional shocks. There are two possible interpretations for this. The first is that capital markets have deepened so that regional shocks are quickly absorbed in the national market. An alternative, though not mutually exclusive, explanation for the decline in the role of shocks on the periphery is that the regional shocks have become more correlated over time.4 We favor the capital-deepening story for the following reasons: (1) the impulse response functions suggest that the peripheral shocks have little impact on the periphery in the third sub-period and when these shocks are significant they dissipate immediately. This is in contrast to the two earlier sub-periods where the peripheral shocks dissipate slowly, if at all. (2) The history of post-WWII banking reform, for example the appearance of interstate branching, suggests the emergence of a more nationally oriented banking market. We cannot, however, rule out the second interpretation, and believe that it probably played a role. 5. Summary and conclusions We explore regional interest rates in the United States and their relationship with rates in the nation’s money markets over the period 1880 to 2002. We find that in the late nineteenth century the main source of shocks to rates on the periphery were shocks originating on the periphery itself. Shocks to rates in the Eastern Financial centers affected the 4
We thank an anonymous referee for pointing this out. Because of the identification of the shocks into orthogonal components, highly correlated regional shocks will be attributed to the national shock.
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periphery, but shocks originating in the financial centers did not dominate. This pattern changed during the disturbed period that encompasses the world wars and the Great Depression. In this period the role of core shocks became more important, but the shocks originating on the periphery remained important. Only in our final period did core shocks dominate. Our results may have some role to play in the debate over monetary unions. In a monetary union in which regions are hit by independent shocks, as was the case in the United States prior to World War II, the coordination of monetary policy across regions is an issue. Once financial markets become deeper and national shocks dominate regional shocks, the coordination across regions becomes less pressing. Our results show that, at least for the United States, the time for the emergence of a dominating central shock was quite long. Although the U.S. monetary union was reintroduced in 1880, it was not until the postwar period that national interest rate shocks dominated. Was this long road peculiar to the American monetary union or is it to be expected in all monetary unions? Clearly, the unique institutional history of American banking has much to do with the slow development of banking system as a mechanism for diffusing regional shocks. Until well into the post WWII era each state had its own banking system. As Breckenridge (1898), Davis (1965), and others have noted, this created artificial barriers to the movement of short-term funds. So the path followed by the United States will not be followed in detail in other monetary unions. Nevertheless, the possibility that political forces will prevent the unification of financial markets as existing monetary unions are extended or new ones created cannot be ruled out. Our results also have some bearing, we believe, on the discussion of what is meant by the ‘‘integration’’ of financial markets. These discussions are often shaped more by the available statistical techniques than we may at first imagine. When data are plotted it is natural to identify integration with the long-run convergence of interest rates. When simple correlations or regressions are computed it is natural to identify integration with increasing correlations. Modern time series techniques such as vector autoregression help us see that the key questions about a financial market may be, where do shocks arise and how are they propagated through the system. Finally, our results have some implications for how we interpret American monetary history. The system of Federal Reserve District banks, for example, was designed, in part, to tailor monetary policy to individual districts. Our results suggest that this was a response to a real problem. It made sense to policymakers in the early years of the twentieth century to attempt to devise an institutional structure—regional Federal Reserve Banks that could set their own discount rates—that could respond to independent shocks arising on the periphery. Appendix A. Supplementary data Supplementary data associated with this article and the full estimation results can be found, in the online version, at doi:10.1016/j.eeh.2006.08.002. References Bodenhorn, H., 1995. A more perfect union: regional interest rates in the United States, 1880–1960. In: Bordo, M.D., Sylla, R. (Eds.), Anglo-American Financial Systems: Institutions and Markets in the Twentieth Century. Irwin Professional Pub., Burr Ridge, Illinois, pp. 415–454.
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Bodenhorn, H., Rockoff, H., 1992. Regional interest rates in antebellum America. In: Goldin, C., Rockoff, H. (Eds.), Strategic Factors in Nineteenth Century American Economic History: A Volume to Honor Robert W. Fogel. University of Chicago Press, pp. 159–187, a National Bureau of Economic Research Conference Report. Breckenridge, R.M., 1898. Discount rates in the United States. Political Science Quaterly 13 (1), 119–142. Davis, L., 1965. The investment market, 1870–1914: the evolution of a national market. The Journal of Economic History 25 (3), 355–399. Dickey, D., Fuller, W., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74 (366), 427–431. Elliott, C., Rothenberg, T.J., Stock, J.H., 1996. Efficient tests for an autoregressive unit root. Econometrica 64 (4), 813–836. Fraas, A., 1974. The second bank of the United States: an instrument for an interregional monetary union. The Journal of Economic History 34 (2), 447–467. Friedman, M., 1953. The case for flexible exchange rates. In: Essays in Positive Economics. University of Chicago Press, Chicago, pp. 157–203. Grubb, F., 2003. Creating the U.S. dollar currency union, 1748–1811: a quest for monetary stability or a usurpation of state sovereignty for personal gain? American Economic Review 93 (5), 1778–1798. James, J.A., 1976a. Banking market structure, risk, and the pattern of local interest rates in the United States, 1893–1911. The Review of Economics and Statistics 58 (4), 453–462. James, J.A., 1976b. The development of the national money market, 1893–1911. Journal of Economic History 36 (4), 878–897. Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12 (2–3), 231–254. Johansen, S., 1992. Determination of cointegration rank in the presence of a linear trend. Oxford Bulletin of Economics and Statistics 54 (3), 383–397. Keehn, R.H., 1980. Market power and bank lending: some evidence from Wisconsin, 1870–1900. The Journal of Economic History, The Tasks of Economic History 40 (1), 45–52. Knodell, J., 1988. Interregional financial integration and the banknote market: the old northwest, 1815–1845. The Journal of Economic History, The Tasks of Economic History 48 (2), 287–298. Knodell, J., 1998. The demise of central banking and the domestic exchanges: evidence from antebellum Ohio. The Journal of Economic History 58 (3), 714–730. Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationary against the alternative of a unit root. Journal of Econometrics 54 (1-3), 159–178. MacKinnon, J.C., Haug, A.A., Michelis, L., 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics 14 (5), 563–577. Mundell, R.A., 1961. A theory of optimum currency areas. American Economic Review 51 (4), 657–665. Perron, P., 1989. The great crash, the oil price shock and the unit root hypothesis. Econometrica 57 (6), 1361– 1401. Rockoff, H., 2003. How long did it take the United States to become an optimal currency area? In: Capie, F.H., Wood, G.E. (Eds.), Monetary Unions: Theory, History, Public choice. Routledge, London, pp. 70–103. Rolnick, A., Weber, W., Smith, B., 2003. In order to form a more perfect monetary union. Federal Reserve Bank of Minneapolis Quarterly Review 17 (4), 2–9. Se´ne´gas, M.-A., 2000. The early years of the second bank of the United States: an historical perspective on the transition to EMU. International Journal of Finance and Economics 5 (1), 57–75. Shambaugh, J.C., 2006. An experiment with multiple currencies, the American monetary system from 1838–60. Explorations in Economic History 43 (4), 609–645. Sheridan, J., 1996. The deja vu of EMU: considerations for Europe from nineteenth century America. Journal of Economic Issues 30 (4), 1143–1161. Smiley, G., 1975. Interest rate movement in the United States, 1888–1913. The Journal of Economic History 35 (3), 591–620. Smiley, G., 1981. Regional variation in bank loan rates in the interwar years. The Journal of Economic History 41 (4), 889–901. Sylla, R., 1969. Federal policy, banking market structure, and capital mobilization in the united states. The Journal of Economic History 29 (4), 657–686.
The origin and diffusion of shocks to regional interest rates in the United States, 1880–2002 John Landon-Lane, Hugh Rockoff
*
Department of Economics, Rutgers University, 75 Hamilton Street, New Brunswick, NJ 08901, USA Received 27 July 2005 Available online 13 October 2006
Abstract This paper is about the behavior of regional interest rates in the United States from 1880 to 2002. The main concern is with the shocks to regional rates. Where did they originate? How did they diffuse? How did the pattern change over time? We show that in the late nineteenth century the main source of shocks to rates on the periphery were shocks originating on the periphery itself. This pattern continued through World War I and the Great Depression. After World War II, however, the importance of disturbances on the periphery diminished and shocks to rates in the Eastern financial centers became the main source of fluctuations in all regions. 2006 Elsevier Inc. All rights reserved. Keywords: Monetary union; Monetary policy; Interest rates
1. Introduction A substantial literature explores regional bank lending rates in the United States, including famous contributions by Davis (1965), Sylla (1969), James (1976a,b), and many others. Our work adds to previous work in three ways. First, we extend the regional bank lending rates to the present to provide a longer historical perspective. Second, we focus on the origin and diffusion of short-term disturbances, rather than on the long term trends in *
Corresponding author. Fax: +1 732 932 7416. E-mail addresses: [email protected] (J. Landon-Lane), rockoff@econ.rutgers.edu (H. Rockoff).
0014-4983/$ - see front matter 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.eeh.2006.08.002
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levels that have traditionally been the focus of the literature. Third, we use vector autoregressions to characterize the relationships among regional rates. We find an interesting pattern. In the late nineteenth century the main source of shocks to rates on the periphery (the Plains, the South, and the West) were shocks originating on the periphery itself. To be sure, shocks to rates in the Eastern financial centers (most importantly, of course, New York) affected the periphery, but shocks originating in the financial centers played a secondary role. Shocks to the core played a more prominent role on the periphery during the disturbed period encompassing the world wars and the depression, but shocks arising on the periphery itself remained important during this period. This is rather surprising because most students of the American capital market thought that regional markets had been fully ‘‘integrated’’ by 1900 or shortly afterwards. After World War II, however, shocks hitting rates in the eastern financial centers dominated rates in all regions. Our work was motivated by an attempt to understand the significance of the American experience for the debate over monetary unions. In popular discussions advocates of monetary unions often hold up the United States as a successful example. After all, the United States has had a single currency since 1790, with the exception of the Civil War era; the United States consists of several distinct regional economies; and the United States has prospered. In practice, of course, it is extremely hard to determine what the net contribution of our monetary union has been. There are, of course, many potential advantages and disadvantages for regions that unite to form a monetary union. The advantages include, but are not limited to, the reduction of transaction costs among members of the union and the increased flow of information about prices. The disadvantages include, but are not limited to, the difficulties encountered by the monetary authority in attempting to formulate a policy that is satisfactory for disparate group of regions. Our work addresses only this last issue. Our results also shed some light on American monetary history. The Federal Reserve Act included, at the insistence of the Wilson Administration, a considerable degree of decentralization. The Act created the separate district banks that we are familiar with, which could set their own discount rates. Our results help us understand the problems that the policy makers were trying to address: the regions of the United States at this time, we show, were being hit by region-specific as well as national shocks which would have made it difficult for a central monetary authority to achieve its policy goals with a single policy instrument. Here is the roadmap. Section 2 reviews the attempts by a number of scholars to analyze the U.S. monetary union and the relationship between our work and this literature. Section 3 takes a preliminary look at the data. Section 4 formally looks at the regional and national shocks hitting the financial markets from 1880 until 2002. These shocks are identified, and their relative importance over time are explored, using a set of vector autoregressions. Section 5 summarizes our results and suggests some implications. 2. The United States as a monetary union The importance of the United States as an example of a monetary union was recognized in the earliest papers that wrestled with these issues. In his famous essay ‘‘The Case for Flexible Exchange Rates’’ Milton Friedman (1953, p. 123) argued that although flexible rates were better between the United States and other nations, fixed rates were better within the United States. High mobility of goods, labor, and capital within the United States, according to Friedman, explained ‘‘why a central monetary authority is able to operate
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without producing serious sectional strains.’’ On the other hand, Robert Mundell (1961, p. 658) in his famous essay ‘‘The Theory Optimal Currency Areas’’ pointed to the United States as an example of a monetary union that had suffered from interregional balance of payments problems. A number of economic historians have explored the effectiveness of the U.S. monetary union in more detail. Rolnick et al. (2003) and Grubb (2003) explored the adoption of the U.S. dollar during the Revolution and under the Constitution. The subsequent development of the U.S. monetary union under the Second Bank of the United States has also drawn considerable interest. Fraas (1974) argued that the Second Bank of the United States established an effective monetary union in the United States in the 1820s, but that in the process of doing so it limited the supply of capital and the rate of growth on the frontier. Knodell (1998, 1988), focusing on inland exchange rates, also argued for an important role for the Second Bank in establishing and maintaining an effective monetary union. Se´ne´gas (2000) explored the convergence of regional inflation rates under the Second Bank. Shambaugh (2006) provided evidence that inland exchange rates were rather loose in the antebellum U.S. and evidence that some states deliberately tried to make use of the resulting monetary independence. Sheridan (1996) and Rockoff (2003) drew attention to the regional monetary conflicts during the postbellum era. The literature we have just cited deals explicitly with the United States as a monetary union. There is another literature that deals ostensibly with a different set of issues, but which we believe should be of interest to students of monetary unions. This is the large literature that focuses on regional interest rates in the United States. This literature can be traced to the classic paper by Davis (1965), although one could also cite R.M. Breckenridge (1898), which is still well worth reading. More recent contributions, just to mention a few of our favorites, include Sylla (1969), James (1976a,b), Smiley (1975), Keehn (1980), and Bodenhorn and Rockoff (1992). The main focus of this literature has been on how long it took regional interest rates to converge. However, this literature also provide important data and interpretations that can be used to enrich our understanding of the U.S. monetary union because interest rates are a sensitive indicator and (at times) instrument of monetary policy. Our work uses the data developed in the literature on regional interest rates to explore two issues that need to be addressed when we think about the United States as a monetary union. The first is can one observe now, or in the past, independent shocks to interest rates on the periphery? If there were independent shocks on the periphery, then it means that there were underlying shocks to the supply or demand for credit on the periphery that were not absorbed immediately by the national capital market. The presence of independent shocks on the periphery raises the question of whether a central monetary authority would need multiple instruments to reach its policy goals thus making the coordination of monetary policy more difficult. This leads to our second question. Did the shocks to the periphery become more congruent with the shocks hitting the core over time and did a single monetary instrument appear that could be used by the central monetary authority to enact its monetary policy goals? In other words, how long did it take for the capital market to reach the point where there was a single national shock that dominated the regional shocks? Our attempts to answer these questions are not definitive. In some cases alternative explanations of the data are possible. We believe, however, that we can shed some light on these questions by exploring the long-term behavior of regional bank lending rates.
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3. Regional bank lending rates For regional interest rates we used estimates from Bodenhorn (1995) for the period 1880–1960 and estimates that we have constructed based on data reported by the Federal Deposit Insurance Corporation for the period after 1960. As far as we know, this is the first study of regional rates to use time series covering the whole of 1880–2002. The appendix presents the data and discusses it in more detail. Fig. 1 plots the bank lending rates for each region and the commercial paper rate for the period 1880–2002 and the Federal Funds rate from 1955 to 2002. In the postwar era the regional bank lending rates are clearly moving together. In the late nineteenth century, however, the picture is far messier. But the nature of the problem in the early period is far from obvious. Was it merely a problem of lags? Or were shocks originating in different regions? The behavior of lending rates during the contraction from 1929 to 1933, is especially perplexing: rates in the Plains and the West rose, even though rates in the Northeast fell. This is unexpected because even the most pessimistic historians of the integration of U.S. financial markets have argued that the process was completed early in the twentieth century. Fig. 2, which uses data from Smiley (1981) for the interwar years, provides further clarification. Smiley’s data is available for a shorter interval than Bodenhorn’s, but available for more regions and semiannually. The figure shows the commercial paper rate; the lending rate for banks in New York City; the lending rate for an average of all banks except those in New York City and Chicago; and the rates in two extreme cases, New England city banks and the Southwest country banks. Lending rates at New York City and New England city banks appear tied together. Their rates reflect, in part the movements in the commercial paper rate. The average for all banks, however, shows that in much of the country the response was muffled. It is possible that as the economy declined banks viewed the risk attached to their average loan rising, so that risk-adjusted rates may have been falling more rapidly. In any case, it is likely that the typical bank borrower did not benefit from the rapid decline in rates in the financial centers. The rate for the Southwest country banks, the other extreme case, seems to be following the beat of a different drummer, rising when other rates were falling. There is an old adage ‘‘When Wall Street 18 16 14
percent
12
Commercial Northeast Plains South West Fed Funds
10 8 6 4 2 0 1880
1900
1920
1940
1960
Year
Fig. 1. Interest rates 1880–2002.
1980
2000
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10 9 8 7 6 5 4 3 2 1 0 1926
New York City New England City Average Commercial paper South West Country 1928
1930
1932
1934
1936
1938
Fig. 2. Regional bank lending rates, 1926–1937.
Sneezes, Main Street Catches a Cold.’’ But when it came to interest rates, the opposite was true: ‘‘When Wall Street Caught a Cold, Main Street Sneezed.’’ Looking at the rates over time is instructive and reveals some interesting divergences in the reactions of rates on the periphery compared with rates in the core. However, looking at these simple co-movements between rates during selected periods is simply scratching the surface. With this approach one cannot differentiate between movements that are due to one core shock operating through a complex dynamic system that affects regions differently, or multiple regional shocks hitting at the same time. What we need to do is identify and separate the peripheral shocks from the core shocks, analyze how these shocks reverberate through the system, and measure their relative influence on the observed rates. In the next section we do this by using the simple but powerful vector autoregression modeling approach. 4. Formal analysis of the sources of interest rate shocks In this section we use the standard vector autoregression (VAR) machinery—impulse response functions and forecast error decompositions—to explore the impact of the shocks hitting the core interest rates and the peripheral interest rates for various sub-periods of our sample. In later periods the core shock can be interpreted as a monetary policy shock. In the earliest period the core shock can be interpreted as the shock that a monetary authority, had it existed, would have had some influence over. This section presents a summary of our main findings, with an emphasis on the historical forecast error variance decompositions. The full details, including the estimation results, the impulse response functions and a description of the data and its sources can be found in the appendix. The set of interest rates modeled included each of the regional bank-lending rates and a ‘‘national rate,’’ the latter being a potential (nineteenth century) or actual (post World War II) instrument of monetary policy.1 We divided the sample period, based on our 1 The national rates that we use are the 3 month New York commercial paper rate, obtained from the Global Financial Database (http://www.globalfindata.com), and the federal funds rate obtained from the FRED II database (http://research.stlouisfed.org/fred2/).
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reading of the historical literature, into three segments: 1880–1913, 1914–1943, and 1955– 2002. This division, we believe, would appear natural to most financial historians. The first segment extends from the start of our data in 1880 to 1913 when the Federal Reserve was established. The second segment includes the disturbed middle decades of the twentieth century: The two world wars and the Great Depression. The last segment begins in 1955, when our data on the Federal Funds rate begins. Given the relatively small samples, we wish to model the data using as parsimonious a time series model as possible. For (covariance-) stationary time series we know that there is a fundamental vector MA(1) representation of the vector of time series and under some regularity conditions this representation can be well approximated by a low-order VAR(p) model. Let yt = (y1t, . . ., ynt)0 represent the vector of covariance-stationary time series to be modeled. Then the (reduced form) VAR(p) model is yt ¼ l þ
p X
Bj y tj þ t ;
ð1Þ
j¼1
where N(0, R) and the coefficients l, Bj, and R are n · 1 and n · n matrices, respectively. If the times series are not stationary then we need to transform the data to make them all stationary first and then model the stationary data appropriately. If the data are all integrated of order one, I(1), then the appropriate transformation is to take the first difference of the data. The next problem we have is to determine how to appropriately model the differenced time series. If the data are cointegrated, that is if there exists a linear combination of the levels of the time series that is stationary, then the appropriate model to use is a vector error correction model (VEC) which is Dy t ¼ c þ ab0 y t1 þ
p1 X
C j Dy tj þ t ;
ð2Þ
j¼1
where the coefficients c, a, b, and Cj are n · 1, n · k, n · k, and n · n matrices, respectively, with k representing the number of cointegrating relationships between the time series that make up yt. If the data are not cointegrated then the appropriate model to use is a VAR in the first differences of the data (DVAR), which is Dy t ¼ c þ
p1 X
C j Dy tj þ t ;
ð3Þ
j¼1
where c, and Cj are n · 1 and n · n matrices, respectively. Hence, before we identify structural shocks for each period of our sample we need to (1) test for non-stationarity of the individual time series, (2) if we find the series to be non-stationary we then need to test whether they are cointegrated, and then (3) determine the appropriate order of the VAR/VEC/DVAR to estimate. We describe in more detail the tests used for (1–3) above and the results below but first we describe how we identify the structural shocks from each of the models described above. As it is a straightforward extension to construct structural shocks for models (2) and (3) with identical implications to structural shocks for model (1) we will concentrate on describing the process of identifying structural shocks for the levels VAR given in (1). The VAR described in (1) is the reduced form version of a structural VAR of the form
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~þ A0 y t ¼ l
p X
Aj y tj þ ut ;
493
ð4Þ
j¼1
where the structural error, ut, consists of a set of orthogonal shocks such that E(utu0 t) = In for all time periods. These shocks have a structural interpretation whereas the shocks that hit the reduced form version, (1), are non-linear combinations of the structural shocks and have little structural or economic interpretation. In fact there is a one-to-one relationship ~, Bj ¼ A1 between the parameters in (1) and the parameters in (4) given by l ¼ A1 0 l 0 Aj for 1 j = 1, . . ., p, and t ¼ A0 ut . The parameters of (1) can be consistently estimated by maximum likelihood methods but the above set of non-linear equations cannot be solved for the parameters of (4) without making some identifying restrictions. Our identifying restrictions focus on the contemporaneous impact matrix, A1 0 . We assume a recursive relationship between the variables of our model by imposing the restriction that the contemporaneous impact matrix is lower triangular. This allows us to exactly identify the parameters given in (4) and so allows us to identify a set of orthogonal structural shocks that hit the financial system during our sample. One implication of this identification scheme is that our results are, potentially, going to be sensitive to the ordering of the time series in yt. The ordering we used is as follows: (1) the national rate—the commercial paper rate or the Federal Funds rate, (2) the Northeast rate, (3) the Plains rate, (4) the Southern rate, and (5) the Western rate. This ordering was dictated partly by our concern with monetary policy: by ordering the monetary policy variable first we are assuming that the monetary authority is forward-looking. This is opposed to being reactive if we were to order the monetary policy variable after any of the regional rates. The first of the regional rates is the Northeast regional rate. This region contained the eastern financial centers: New York (by far the most important), Boston, and Philadelphia. The order to be chosen for the peripheral regions is less clear-cut. The order we usually worked with was (after the Northeast) the Plains states, the South, and the West. This ordering reflects a nineteenth century view of things; today we would be more likely to put the West second and, perhaps, the Plains last.2 Given our ordering of the variables, the identifying restrictions imposed means that we can interpret the shocks in the following way: the national shock is the shock that hits the commercial paper rate or the Federal Funds rate. This shock is likely to contain shocks to the commercial paper rate or the Federal Funds rate that are national in origin and certainly for the Federal Funds rate should include monetary policy shocks. The Northeast shock is the component of the northeast residual that is orthogonal to the national shock, that is, the Northeast shock consists of shocks hitting the Northeast regional bank rate that are not monetary policy shocks or national shocks. The Plains shock is the component of the plains residual orthogonal to both the national shock and the Northeast shock. The South shock is the component of the South residual that is orthogonal to the National, Northeast, and Plains residuals. Finally, the West shock is the shock that hits the West that is orthogonal to all the other shocks. 2
While we report results for the ordering in the periphery of the Plains first, the South second and the West last we have done a sensitivity analysis by permuting these last three regions in the VAR. In all cases, the national rate is ordered first and the Northeast rate is ordered second. The results, available from the authors upon request, show that the results presented are not sensitive to the ordering of the variables.
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4.1. Unit root and cointegration tests Before we estimate our models for each sub-period we tested each interest rate time series for the presence of a unit root and if we find that all the series contain a unit root we then tested for cointegration. We perform a battery of unit root tests for each interest rate series. The tests used were the standard augmented Dickey Fuller (ADF) test (Dickey and Fuller, 1979), the GLS de-trended version of the ADF test (ADF-GLS) (Elliott et al., 1996), and the ADF with a structural break (Perron, 1989). All these tests have as their null hypothesis the hypothesis that the time series contains a unit root. Given the small sample size and the fact that these unit root tests are known to have small power we also performed the unit root test suggested by Kwiatkowski et al. (1992), otherwise known as the KPSS test. This test has as its null hypothesis the hypothesis that the time series do not contain a unit root. The overall result was that we could only reject the hypothesis that the interest rate series contain a unit root in the first sub-period. On economic grounds this seems reasonable. In this period real rates would be anchored by the productivity of capital and time preference, and the inflation premium would be held in check by the gold standard. However, in the postwar era when the inflation premium becomes an issue, a unit root in nominal rates would be more likely. The results of these tests suggest that we can estimate a VAR in levels for the first subperiod but for the second and third sub-period we need to estimate a VAR in differences. This brings into play the issue of co-integration. If there is co-integration present the appropriate model to estimate is the Vector Error Correction model which is just the DVAR (VAR in differences) with error correction terms added to each equation. To test for cointegration we used the method of Johansen (1988, 1992) using the sample size corrected critical values suggested by MacKinnon et al. (1999). The detailed results of these unit root and cointegration tests can be found in Table 1 of the appendix. Our results were that in the second sub-period (1914–1943) there was no evidence of cointegration. In the third sub-period there is evidence of one cointegrating relationship between the interest rates in our model. Given these results we proceeded as follows: (1) For period 1880–1913 we estimated a VAR model in levels, (2) for the period 1914– 1943 we estimated a VAR in first differences, and (3) for the period 1955–2002 we estimated two VEC models, one with the commercial paper rate as the ‘‘national’’ rate, and a second with the Federal funds rate as the ‘‘national’’ rate. 4.2. Model specification The last task left for us to do was to determine the number of lags to include for each model. Given that we have small samples in each of our sub-periods we used an information criterion approach to choose the lag length. We used the Schwarz Bayesian Information Criterion (SBIC) to choose the lag length for each sub-period as this information criterion consistently estimates the correct lag length if the true model is a VAR and does so for stationary and non-stationary models alike. In all cases, the SBIC was minimized for p = 1. A DVAR and a VEC with one lag included are equivalent to a levels VAR with two lags included. To be consistent across sub-periods we checked whether a VAR(2) in levels was appropriate for the first sub-period by performing a likelihood ratio test of whether the second lag of the endogenous
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variables are jointly 0 across all equations of the VAR. The result of this test was mixed in that the p-value of the likelihood ratio test was 0.06. Thus we could not reject the hypothesis that the coefficient matrix for a VAR(2) at the 5 percent level but we could reject at the 10 percent level. Given the small sample size and the fact that the penalty for adding extraneous variables is only a loss in efficiency compared to the penalty for omitting a relevant variable being estimation bias we decided to estimate a VAR(2) model for the first sub-period rather than a VAR(1). Thus we estimate a VAR(2) for the first sub-period, a DVAR(1) for the second sub-period, and a VEC(1) model for the third sub-period.3 4.3. Results All models were estimated using a maximum likelihood estimator. The system wide Wald test that all of the coefficients in each model are jointly 0 can be rejected for all reasonable test sizes. The equation wide Wald tests also show, except for a few equations, that the joint hypothesis that all coefficients, excluding the constant, are jointly 0 can be rejected as well. While there are significant individual coefficients in each model, we find it more instructive to look at the impulse response functions and forecast error variance decompositions implied by our estimates as a check the model’s validity because the impulse response functions and forecast error decompositions have natural economic interpretations. To conserve space we concentrate here on the forecast error variance decomposition results. In our view, these variance decompositions distill the results from the VAR analysis succinctly and in a way that addresses issues that are important to economic historians. The full estimation results and impulse response functions can be found in the appendix. In each of the three sub-periods we computed orthogonalized interest rate impulse response functions according to our identification outlined in Section 4. The impulse response functions show that the national interest rate shock has a positive and significant impact on all regional rates in all periods and that for the second and third sub-period this impact was permanent. This suggests that the markets were integrated in the sense that shocks that occurred in one region reverberated through the system. Shocks to rates in the eastern financial markets did affect rates on the periphery. This is a natural definition of integration, although somewhat different, we should note, than the traditional definition in the literature on regional interest rates that identifies integration with convergence of interest rates in the long run. We also find, for the first and second sub-period, that shocks to the periphery are affecting other rates in the periphery. For example shocks to the South and the West positively affect the Plains while shocks to the Plains appear to positively affect the West. This is an interesting result because it shows that even after the Fed arrived it did not have complete control of regional rates of the sort that we see in the post WWII era. In the third sub-period we found that the only significant impacts of the peripheral shocks are on their own rates. And even in this case the impacts are small relative to the monetary policy shock and appear to be temporary. This is in contrast to the earlier 3
The main result of this paper is robust to the choice of lag length. Using different model selection criteria can lead to different model choices but in all cases that we have tried the main results always hold: the influence of the national shock on the periphery is getting stronger over time and the peripheral shocks have important effects in the first two periods but negligible effects in the last period.
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periods where it appeared that some of the peripheral shocks had significant and long-lasting effects on other rates in the periphery. The crucial question here is does the central monetary authority have access to an instrument that dominates interest rates in the periphery? To get at this question we construct forecast error variance decompositions. The variance decompositions show the contribution of each structural shock to the non-forecastable components of each time series (i.e., the random component of each time series once we account for the trend, level, and the relationship to past values of the series). Variance decompositions are more useful for this purpose than impulse–response functions because variance decompositions reflect the size and frequency of the shocks as well as their impact on other variables as described by the VAR equations. The impulse–response chart does not show the size or the frequency of the shocks, but merely what the effect of a standardized shock would be. The forecast error variance decompositions are reported in detail in the appendix. The National shock contributes by far the largest amount to the FEVD of the National rate with the Northeast shock being the next largest contributor. This is true for all sub-periods. Together the two core shocks, as might be expected, contributed almost all of the forecast error variance observed for the core rates. The role of the peripheral shocks is very small with only the Plains and West shocks having some, albeit small, contribution in the first sub-period. A very different story emerges when we look at the FEVD for the peripheral shocks. We report in Figs. 3–5 the contribution of each shock to the forecast error variance for each of the peripheral interest rate in our model. Each line on the sub-figures represents a time period with CR representing the results using the Commercial Paper rate and FF representing the results using the Federal Funds rate. The change in the impact of each shock National Shock 100 80 60 40 20 0
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Fig. 3. Forecast error variance decomposition: Plains rate.
J. Landon-Lane, H. Rockoff / Explorations in Economic History 44 (2007) 487–500 National Shock
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over time is stark. For the Plains rate (Fig. 3) the impact of the peripheral shocks is quite large in the first period. Over 50 percent of the forecast error variance can be attributed to shocks from the periphery in the first sub-period. In the last sub-period the total impact of all peripheral shocks has fallen to less than 10 percent of total forecast error variance. Moreover, the impact of the core shocks on the forecast error variance on the Plains rate has dramatically increased from less than 50 percent in the early period to close to 90 percent in the third period. The same effects can be seen on the South rate (Fig. 4) and the West rate (Fig. 5). In the South the contribution of the peripheral shocks to the forecast error variance in the first period is large (over 50 percent) while in the second sub-period the contribution is moderately large (around 25 percent). However for the third sub-period the contribution of the peripheral shocks to the forecast error variance is negligible (less than 5 percent). The story for the West is very similar. Over 50 percent of the forecast error variance can be attributed to peripheral shocks in the first and second sub-period for the West rate (Fig. 5). This falls to less than 10 percent for the last sub-period. Thus in the pre-war and interwar years it appears that the core interest rates would not have been a good instrument for monetary policy in that it would have been hard for a monetary authority to use the core rates to counteract shocks in the periphery. This is in contrast to the most recent period where, as might be expected, shocks to the national rate, whether measured by the commercial paper rate or the Federal Funds rate, and the Northeast rate appear to explain most (at least 90 percent) of the variance of the forecast errors for all interest rates. Shocks in the periphery have a very small impact on rates in the periphery and these effects do not linger very long. Thus in the latter period it appears that the monetary authority has access to an instrument that dominates rates from all regions of the country. The results from the impulse response functions and the variance decompositions point to the emergence of a strong central instrument that dominates regional shocks. There are two possible interpretations for this. The first is that capital markets have deepened so that regional shocks are quickly absorbed in the national market. An alternative, though not mutually exclusive, explanation for the decline in the role of shocks on the periphery is that the regional shocks have become more correlated over time.4 We favor the capital-deepening story for the following reasons: (1) the impulse response functions suggest that the peripheral shocks have little impact on the periphery in the third sub-period and when these shocks are significant they dissipate immediately. This is in contrast to the two earlier sub-periods where the peripheral shocks dissipate slowly, if at all. (2) The history of post-WWII banking reform, for example the appearance of interstate branching, suggests the emergence of a more nationally oriented banking market. We cannot, however, rule out the second interpretation, and believe that it probably played a role. 5. Summary and conclusions We explore regional interest rates in the United States and their relationship with rates in the nation’s money markets over the period 1880 to 2002. We find that in the late nineteenth century the main source of shocks to rates on the periphery were shocks originating on the periphery itself. Shocks to rates in the Eastern Financial centers affected the 4
We thank an anonymous referee for pointing this out. Because of the identification of the shocks into orthogonal components, highly correlated regional shocks will be attributed to the national shock.
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periphery, but shocks originating in the financial centers did not dominate. This pattern changed during the disturbed period that encompasses the world wars and the Great Depression. In this period the role of core shocks became more important, but the shocks originating on the periphery remained important. Only in our final period did core shocks dominate. Our results may have some role to play in the debate over monetary unions. In a monetary union in which regions are hit by independent shocks, as was the case in the United States prior to World War II, the coordination of monetary policy across regions is an issue. Once financial markets become deeper and national shocks dominate regional shocks, the coordination across regions becomes less pressing. Our results show that, at least for the United States, the time for the emergence of a dominating central shock was quite long. Although the U.S. monetary union was reintroduced in 1880, it was not until the postwar period that national interest rate shocks dominated. Was this long road peculiar to the American monetary union or is it to be expected in all monetary unions? Clearly, the unique institutional history of American banking has much to do with the slow development of banking system as a mechanism for diffusing regional shocks. Until well into the post WWII era each state had its own banking system. As Breckenridge (1898), Davis (1965), and others have noted, this created artificial barriers to the movement of short-term funds. So the path followed by the United States will not be followed in detail in other monetary unions. Nevertheless, the possibility that political forces will prevent the unification of financial markets as existing monetary unions are extended or new ones created cannot be ruled out. Our results also have some bearing, we believe, on the discussion of what is meant by the ‘‘integration’’ of financial markets. These discussions are often shaped more by the available statistical techniques than we may at first imagine. When data are plotted it is natural to identify integration with the long-run convergence of interest rates. When simple correlations or regressions are computed it is natural to identify integration with increasing correlations. Modern time series techniques such as vector autoregression help us see that the key questions about a financial market may be, where do shocks arise and how are they propagated through the system. Finally, our results have some implications for how we interpret American monetary history. The system of Federal Reserve District banks, for example, was designed, in part, to tailor monetary policy to individual districts. Our results suggest that this was a response to a real problem. It made sense to policymakers in the early years of the twentieth century to attempt to devise an institutional structure—regional Federal Reserve Banks that could set their own discount rates—that could respond to independent shocks arising on the periphery. Appendix A. Supplementary data Supplementary data associated with this article and the full estimation results can be found, in the online version, at doi:10.1016/j.eeh.2006.08.002. References Bodenhorn, H., 1995. A more perfect union: regional interest rates in the United States, 1880–1960. In: Bordo, M.D., Sylla, R. (Eds.), Anglo-American Financial Systems: Institutions and Markets in the Twentieth Century. Irwin Professional Pub., Burr Ridge, Illinois, pp. 415–454.
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