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Journal of Macroeconomics 32 (2010) 732–746
Contents lists available at ScienceDirect
Journal of Macroeconomics
journal homepage: www.elsevier.com/locate/jmacro
One nation under the fed? The asymmetric effects of US monetary policy
and its implications for the United States as an optimal currency area
David Beckworth *
McCoy College of Business, Texas State University, San Marco, Texas 78666, United States
a r t i c l e
i n f o
Article history:
Received 16 January 2009
Accepted 7 December 2009
Available online 23 December 2009
JEL classification:
E32
E52
E58
L16
R11
a b s t r a c t
Is the United States best served by a single currency? This question is explored in this paper
by looking at the regional effects of US monetary policy shocks through the perspective of
the optimal currency area framework. Using monthly state-level data for the period
1983:1–2008:3, this paper finds that some regions of the United States during this time
may have benefited from having their own currency.
Ó 2009 Elsevier Inc. All rights reserved.
Keywords:
Optimal currency area
Regional effects of monetary policy
US monetary policy
Vector autoregressions
‘‘[T]he weakening labour markets in the sunbelt states are being offset by sharply falling jobless claims in rustbelt states. . . This
poses a conundrum for the Fed as it determines interest rate direction: How do you interpret a split-personality labour market,
and its inconsistent impact on growth and inflation?”
David Parkinson in ‘‘Sunbelt vs. Rustbelt Tug-of-War”, July 7, 2007.
1. Introduction
Is the United States best served by a single central bank conducting countercyclical monetary policy? According to the
optimal currency area (OCA) criteria, the answer is yes if the various regions of the United States share similar business
cycles or have in place flexible wages and prices, factor mobility, fiscal transfers, and diversified economies.1 In the former
case, similar business cycles among the regions mean that a national monetary policy, which targets the aggregate business
cycle, will be stabilizing for all regions. In the latter case, dissimilar business cycles among the regions make a national monetary policy destabilizing—it will be either too simulative or too tight—for some regions unless they have in place the above
listed economic shock absorbers. Many, if not most, observers believe the United States fits these criteria and is a successful
* Tel.: +1 512 245 6067.
E-mail address: [email protected]
1
The seminal work in the OCA theory comes from Mundel (1961), McKinnon (1963), and Kenen (1969). See Mongelli (2002) for a survey of the OCA
literature.
0164-0704/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved.
doi:10.1016/j.jmacro.2009.12.001
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
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monetary union. As a result, the United States is often held up as a benchmark case of an OCA for other areas attempting to form
a monetary union (Eichengreen, 1990; Bayomi and Eichengreen, 1993; Feldstein, 1997; Bordo, 2004).
But is the United States truly an OCA? The above quote implies that the Rustbelt and the Sunbelt regions of the United
States may have benefited from having their own currencies and monetary policy in early-to-mid 2007. This suggestion that
the United States might benefit from the use of regional currencies is consistent with Rockoff (2000) who concludes that the
United States’ OCA status is tenuous at best. Other studies also raise questions about the OCA status of the United States.
Owyang et al. (2005) and Crone (2005, 2006) shows that business cycles continue to vary in non-trivial ways across US regions. Carlino and Defina (1998, 1999a, 1999b), Crone (2005) and Owyang and Wall (2006), meanwhile, find that monetary
policy shocks generate large asymmetric effects across US regions. These findings imply that certain regions of the United
States may fail to meet the OCA criteria. Kouparitsas (2001) examines this possibility directly and finds that three of the eight
Bureau of Economic Analysis (BEA) regions are not in the dollar OCA. Looking at state employment business cycles, Partridge
and Rickman (2005) similarly conclude the United States fails to meet the traditional criteria for an OCA. Far from being a
closed case, then, the United States as an OCA remains an open question.
This paper provides further evidence on the OCA status of the United States. It does so by taking another look at the
asymmetric effects of US monetary policy shocks on state economies. Previous studies in this literature have tried to explain the asymmetric effects of such shocks by examining whether the different transmission channels of monetary policy
were more prevalent in certain regions. This paper takes a different approach by explaining the asymmetric effects of monetary policy shocks by way of the OCA criteria. Specifically, it regresses the asymmetric effects against measures of the OCA
criteria and uses them to construct a decision rule that determines whether certain states would have benefited from having their own currency and monetary policy. The paper also looks at the source of economic shocks in each state and
whether they are dissimilar enough to justify monetary autonomy. This approach is different than Kouparitsas (2001)
who uses the response of regional economies to and the relative importance of common and idiosyncratic shocks to determine the dollar OCA.2 This paper is also unique in several other ways. First, it makes use of a monthly data set of real economic activity at the state level. The use of this higher frequency data allows for a more complete picture of each state’s
economic response to a monetary policy shock. Second, previous studies have been beset with the challenge of having to deflate nominal regional economic measures, but without having an appropriate deflator to do so. Using the above data and a
unique modeling approach this paper is able to overcome this challenge. Third, the modeling approach used in this paper allows for a consistent monetary policy shock to be estimated across all states. This paper, then, is able to show whether each
state economy responds differently in a statistically significant way from the national economy to the same monetary policy
shock.3 Collectively, these innovations provide for fresh insights into whether the United States truly is an OCA. To be clear,
though, this paper like most studies on the OCA only speaks to the potential gains for US regions having their own currency
and does not address the issue of increased transaction costs.
The remainder of the paper proceeds as follows. First, the paper reviews the previous research on the asymmetric effects
of US monetary policy on regional economies. Second, this paper formally outlines the estimation strategy and then uses it to
find the state-level effect of monetary policy shocks. Third, after finding that US monetary policy shocks generate asymmetric effects this paper explains this variation using measures of the OCA criteria. Fourth, the paper uses these findings to assess
what states of the United States may have benefited from having their own currency. Finally, the paper concludes with a
discussion of the implications from these findings.
2. Previous research on the regional effect of US monetary policy shocks
This paper follows a number of studies over the past decade that have examined the regional effects of monetary policy
shocks. Carlino and DeFina (1998, 1999a,b) provide the seminal papers in this literature. They estimate a series of vector
autoregressions (VARs) in these papers to determine the impact of monetary policy shocks on regional and state economies.
When looking at the state-level response (1999a,b), they specifically estimate 48 VARs for the 48 contiguous states for the
period 1958:Q1–1992:Q4. In each VAR they include the following variables: real personal income for the state being considered, real personal income for the remainder of the BEA region containing the state being considered, real personal income
for the seven other BEA regions, energy prices, core CPI, the index of leading indicators, and the federal funds rate. Carlino
and Defina use the VARs to estimate cumulative impulse response functions (IRFs) of each state’s real personal income from
a shock to monetary policy (i.e. a shock to the federal funds rate) and find great variation among the states IRFs. In particular,
the Great Lakes region is found to be the most adversely affected by monetary policy shocks while its impact on states in the
Southwest and Rocky Mountain regions is the least affected of any area in the Unites States. Crone (2005) creates a new set of
economic regions based on the similarities in state business cycles for the period 1959:Q1–1993:Q1 and uses them in a VAR
identical to the one used in Carlino and DeFina (1998). Crone similarly finds the Great Lakes area to be the most adversely
affected from a monetary policy shock while the area he calls the energy belt—comprised of portions of the BEA’s Southwest
2
Kouparitsas (2001) does estimate the effect of monetary policy shocks. His emphasis, however, is on common and idiosyncratic shocks in general and he
does not attempt to systematically explain the asymmetric effects of monetary policy as is done in this paper.
3
Kouparitsas (2001) also estimates a consistent monetary policy shock, but as noted earlier he does not try to explain the asymmetric effects of this shock.
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
and Rocky Mountain regions—is the least affected. Owyang and Wall (2006) estimate the impact of monetary policy shocks
on 19 sub-BEA regions. These regions consist of two to four states each and are all estimated in one VAR. Here, the VAR
includes real personal income, the CPI, the federal funds rate, the 10-year Treasury rate, and commodity prices. They estimate the VAR for the periods 1960:Q1–1978:Q4, 1983:Q1–2002:Q4. They look at the regular impulse response functions
and find sub-regions in the Midwest-Great Lakes area to be the most harmed by monetary shocks for all periods. They
too report that the sub-regions in the Rocky Mountain, Southwest, and Far West areas tend to be the least affected. Lastly,
they show that the effects of monetary policy shocks on the sub-regions lessened in the latter period. Finally, Kouparitsas
(2001) estimates a VAR that includes the real personal income of the 8 BEA regions, oil prices, and the federal funds rate
for the period 1969:Q1–2002:Q1. Using identifying restrictions, he is able to come up with an income shock common to
all regions, regional or idiosyncratic shocks, an oil price shock, and a monetary policy shock. He uses these shocks and
the responses they create in the regions to determine which areas make up the dollar OCA. His results similarly show differing responses among the regions. He specifically finds the Southwest to be the least affected while the Rocky, Plains, and
Great Lakes are the most adversely affected by monetary policy shocks.
While this previous research points to US monetary policy shocks generating asymmetric effects, there are some challenges for these studies in estimating the effect of monetary policy when it comes to the state level. One challenge is that
the previous studies use real personal income as a measure of state-economic activity. This measure has to be constructed from the available nominal personal income using a price deflator. There are, however, no state price level measures that are at a high enough frequency or are available back far enough to be useful. As a consequence, the national
CPI is used in these studies to deflate state personal income. Dowd and LeSage (1997) have shown, however, that using a
national price level measure requires ignoring important variation in state price levels. Another challenge is that in order
to compare the effects of a monetary policy shock across states there needs to be a consistent measure of a monetary
policy shock. However, to estimate a consistent measure in a standard VAR requires all states be included in the VAR
which leads to a serious degrees of freedom problem. Carlino and DeFina (1998, 1999a,b) sidestep this issue by estimating for each state a separate VAR that includes the rest of the state’s BEA region and the other remaining seven BEA regions. While this strategy allows the VAR to capture the dynamics of the whole US economy, each VAR has a different set
of equations and may be estimating different monetary policy shocks. Owyang and Wall (2006) tackle this problem differently by estimating 19 sub-regions that consist of two to four states each. This leads to a consistent measure of a monetary policy shock within the sub-regions, but still results in potentially different monetary policy shocks among regions.
These challenges, then, motivate the need for a better estimation strategy. The next section presents just such an estimation strategy.
3. Estimating the state-level effect of monetary policy shocks
Given the degrees of freedom problem noted above, a key issue is how to properly estimate a large VAR that has both
macroeconomic and state-economic variables. In order to address this issue, the estimation strategy adopted here is to impose restrictions on the model such that the state economies in the VAR cannot influence the rest of the VAR—other than the
state economies bordering them—but themselves can be influenced by the macroeconomic portion.4 Doing so allows a macroeconomic shock such as a monetary policy innovation to influence a state economy upon impact and afterwards but not vice
versa. This approach, therefore, not only solves the degree of freedom problem but also makes reasonable restrictions on the
relationship between the state and national economy.
As shown by Lastrapes (2004, 2006), this approach can be formally demonstrated by beginning with the autoregressive
structural model of the form
A0 zt ¼ A1 zt1 þ þ Ap ztp þ ut ;
ð1Þ
where A0 ; . . . ; Ap are n n structural parameters matrices, zt is a n 1 vector of endogenous variables, and ut is a n 1 vector
of uncorrelated structural shocks that are assumed to be multivariate
normal with mean zero and unit variance. Next, par
z1t
where z1t contains the macroeconomic variables or the ‘‘comtition the endogenous vector of variables so that zt ¼
z2t
mon factors” while the z2t contains the state-economy variables. Here, z1t consist of a national real economic activity
measure, the price level, commodity prices, and the federal funds rate while z2t consists of a state real economy activity measure for the 48 states. To avoid the degrees of freedom created by this 52-variable VAR, two sets of overidentifying restrictions can be imposed on the VAR: (1) z1t is block exogenous with respect to z2t and (2) the state-economy variables in z2t are
mutually independent, except for the border economies, after conditioning on z1t . The block-exogeniety creates zero restrictions in the Ai matrices, where i = 0, 1, . . . , p, and require the macroeconomic variables to be determined independent of the
state-economy variables. The second set of restrictions imposes separately for each state zeroes in the Ai matrices for the
other states that do not border it and non-zero weights on states that do border it. The non-zero weights are set equal to
4
Other studies that have taken a similar approach include Barth and Ramey (2001), Davis and Haltiwanger (2001), Frantantoni and Schuh (2003), Lastrapes
(2004, 2006), and Irvine and Schuh (2005).
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
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the bordering state-economy’s share of the total border economy and allow for interdependencies among border state economies.5 In short, the restrictions imposed on z2t mean that state economies cannot influence each other unless they are physically contiguous.
As noted by Lastrapes (2004, 2006), these overidentifying restrictions make it possible to estimate the large VAR in a twostep procedure: first, the macroeconomic portion of the VAR is estimated and second, the state economies conditional on the
macroeconomic variables, the bordering economies, and themselves can be individually estimated. The estimation strategy
adopted in this paper is to effectively do this by estimating the following vector of endogenous variables by SUR for each
state:
zt ¼ ð yusa
Þ;
cpit ; ppit ; ffrt ; ystate
; yborder
t ;
t
t
ð2Þ
where yusa
is the national real economic activity measure, cpit is the price level, ppit is the commodity price index, and ffr t is
t
is the state real economic activity measure, and yborder
, is the border economy measure. This last
the federal funds rate, ystate
t
t
variable is a composite measure created by taking the weighted average of all state real economies bordering the state being
estimated and reflects the non-zero weighting discussed above.6 SUR estimation is used here since the block exogeneity
restrictions are imposed by not allowing the two state-economy measures to enter the right-hand side of the macroeconomic
. This ordering in conjunction with a
variable equations.7 The z2t restrictions are effectively imposed by only including yborder
t
Choleski decomposition of the covariance matrix for the entire vector of endogenous variables allows for the recursive identification strategy outlined by Christiano et al. (1999) whereby the federal funds rate—the instrument of monetary policy—is able
to respond instantaneously to shocks in the other macroeconomic variables but can only affect them with a lag. Any change in
the federal funds rate not forecasted by the VAR is interpreted as a shock to monetary policy.8 Given the block exogeneity
restrictions and the recursive identification strategy—which imposes contemporaneous restrictions—the state variables in no
way affect the macroeconomic variables, both upon impact and afterwards. As a result, only one macroeconomic system is being
estimated and hence, only one set of monetary policy shocks is being estimated. The same monetary policy shocks, therefore,
get applied across all state economies as they are individually estimated.9
Once the model is estimated and the structural monetary policy shocks are identified, innovation accounting can be done
to see the effect of these shocks on real economic activity. In particular, cumulative impulse response functions (IRFs) are
used to show the cumulative dynamic response of both the US and state economies to a one standard deviation monetary
policy shock. To the extent that there are statistically significant differences among these IRFs, monetary policy is generating
asymmetric effects.
The measure of real economic activity for the United States and the 48 states is the monthly coincident indicator produced the Philadelphia Federal Reserve bank. The coincident indicator is based upon Stock and Watson (1989) and is designed to be a summary measure of real economic conditions in each state. It is derived using nonfarm payroll
employment, average hours worked in manufacturing, the unemployment rate, wage and salary disbursements deflated
by the consumer price index (US city average), and trend GDP growth rate for each state and the nation. Owyang et al.
(2005) use this measure in their study of state-level business cycles. Since this measure is already in real terms, the problem of having to find a proper deflator is avoided. The consumer price index and the producer price index of commodities
are used as well. These latter variables plus the federal funds rate come from the St. Louis Federal Reserve bank’s Fred
database. All variables are in a monthly frequency and have observations running from 1983:1 to 2008:3. This time period
is used to account for the well-documented structural break in monetary policy that occurred in the early 1980s (Boivin
and Giannoni, 2006).10 All variables are transformed into log form and first differenced—except for the federal funds rate
which was just first differenced—since standard unit root test indicate nonstationarity in the levels of the variables and because there was no convincing evidence found for cointegrating relationships among the variables.11 The VARs are estimated
using 13 lags since the Ljung-Box Q test indicates that this many lags are sufficient to whiten the residuals of any serial
correlation.
5
The weights are specifically constructed by dividing a bordering state’s personal income in current dollars by the all bordering state economies’ personal
income in current dollars for each quarter. The quarterly data is then linearly interpolated into a monthly frequency. The state personal income comes from the
BEA and is available only on a quarterly basis.
6
Specifically, the weights explained in footnote 5 are multiplied by the state real economy measure for each of the bordering states and these are then
summed up into the border economy composite measure.
7
Other than this restriction, the rest of the system is set up like a standard VAR with lags of all the endogenous variables entering the right-hand side of the
equations. This reduced-form VAR is estimated and turned into a structural VAR using the recursive identification procedure outlined above.
8
Given its dynamic nature and the inclusion of four standard macroeconomic variables, the VAR forecast of the federal funds rate can be viewed as the
expected path of monetary policy.
9
It is worth nothing that since the equations for the macroeconomic variables have identical right-hand side variables and cannot be influenced by the state
variables, the SUR estimation of the macroeconomic portion of the system amounts to the same thing as estimation by least squares equation-by-equation.
However, this is not true for the state-economy variables since they are allowed to be influenced by the macroeconomic variables and are estimated in a system
of equations that does not have the same number of right-hand side variables.
10
An interesting exercise would be to examine this OCA question before the 1980s. However, the coincident indicator measure only goes back to 1979 and,
thus, the analysis is limited to the post 1979 period.
11
The augmented Dicker–Fuller and the Phillips–Perron unit root test were used. First differencing the data was sufficient to remove the unit root. The twostep Engle–Granger cointegration test was used to test for cointegrating relationships.
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
Alabama
0.2
Arizona
-0.0
-0.2
-0.2
-0.4
-0.4
-0.6
-0.6
-0.8
-1.0
-0.8
10
20
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Colorado
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
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20
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40
50
Conneticut
-0.0
0.25
0.00
-0.6
-0.25
-0.8
-0.50
30
40
50
Georgia
0.2
-0.2
-0.4
-0.6
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Iowa
-0.2
-0.4
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-0.8
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Idaho
-1.0
30
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20
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Illinois
-0.2
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-0.4
-0.4
-0.6
-0.6
-0.8
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40
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30
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40
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Indiana
0.2
-0.0
-1.0
10
20
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40
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10
20
Louisiana
0.75
0.50
0.25
-0.4
0.00
-0.6
-0.25
-0.8
50
10
-0.0
-0.2
40
30
-0.8
20
Kentucky
30
50
-0.4
-0.0
20
40
-0.6
Kansas
10
30
-0.2
10
50
20
Florida
-0.0
0.2
30
10
Delaware
-1.0
10
0.2
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.0
-0.8
20
-1.00
10
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.0
-0.4
-0.6
-0.75
-1.2
20
-0.2
0.50
-0.4
California
0.2
-0.0
10
-0.2
-1.0
10
Arkansas
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.0
-0.50
10
20
30
40
50
10
20
30
Fig. 1. IRF to a standard deviation monetary policy shock.
4. Empirical findings
Figs. 1–3 report the estimated cumulative IRFs for each state’s coincident indicator from a positive one standard deviation
shock to the federal funds rate. Since the IRFs are cumulative they can be interpreted as the percent change in the level of the
real economy. For each state, the black line shows the IRF, the gray line shows the IRF for the United States, and the dashed
line shows the simulated standard error bands to account for the precision of the estimates and to show which states are
significantly different than the point estimates of the United States.12
Using the United States as a benchmark, these figures show that most of the economic decline from a positive one unit
federal funds rate shock occurs by 24 months. For the United States, the decline is 0.25% by that time. Figs. 1–3 also show
wide variation among the states’ IRFs. Looking at those states whose standard error bands fall outside the US IRF for at least
25 months, these figures show 12 states—Colorado, Idaho, Louisiana, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, and Wyoming—did significantly better than the United States and eight states—Connecticut, Maryland, Michigan, New Jersey, Ohio, Pennsylvania, South Carolina, and West Virginia—that did worse. Consistent
with Crone (2005), all the states that did better are in the Energy belt part of the country, running from Texas up to Montana,
while most of those that did worse mostly fall into the Rustbelt. These results alone suggest there are vast regions of the
United States not in the dollar OCA. To help summarize the information in these figures, Table 1 reports the 12-month,
24-month, and 36-month IRF for each state relative to the United States since this is the region targeted by US monetary
policy. The states are ranked in descending order according to the size of their decline. This table makes plain the stark contrast between some of the states. Michigan, for example, declines relative to the United States 0.97% at the 36-month horizon
while Wyoming increases 1.60%. Other states like Arkansas, Maine, New York, and Iowa, on the other hand, have declines
very similar to the United States. Two key insights emerge from Figs. 1–3 and this table. First, US monetary policy shocks
do generate asymmetric effects among the states. Second, there appears to be a regional pattern to those state economies
whose response to such shocks is significantly better or worse than the United States. The next section attempts to systematically explain these findings.
5. Explaining the asymmetric effects of monetary policy
Prior studies (Carlino and DeFina, 1998, 1999a,b; Owyang and Wall, 2006; Owyang et al., 2005) have tried to explain
monetary policy’s asymmetric effects by looking at three monetary policy transmission channels: the interest rate channel,
12
The standard error bands are technically fractiles that come from using Monte Carlo integration techniques to estimate the posterior density of the
response coefficients. Sims and Zha (1999) recommend with this approach, which characterizes the likelihood shape, the use of a 68% posterior probability.
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
Maine
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
10
20
Maryland
-0.0
30
40
-0.2
-0.0
-0.4
-0.2
-0.6
-0.4
-0.8
-0.6
-1.0
-0.8
-1.2
-1.0
50
10
Minnesota
20
30
40
50
Mississippi
-0.0
-0.0
-0.2
-0.2
-0.4
-0.4
-0.6
-0.6
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-0.8
Massachusetts
0.2
Michigan
0.00
-0.50
-1.00
-1.50
-2.00
10
20
30
40
50
10
Missouri
0.2
20
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Montana
1.2
-0.0
-0.2
0.8
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0.4
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10
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Nebraska
0.2
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Nevada
0.50
0.1
0.00
-0.1
-0.25
-0.2
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New Mexico
0.3
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0.1
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New Hamsphere
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-1.0
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North Dakota
0.4
0.3
0.2
0.1
-0.0
-0.1
-0.2
-0.3
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-0.8
10
10
50
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50
50
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North Carolina
40
40
-0.6
-0.6
30
30
-0.2
-0.2
20
20
New Jersey
-0.0
-0.0
10
10
-0.4
New York
0.1
-0.0
-0.1
-0.2
-0.3
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40
20
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10
0.25
0.00
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-0.75
-1.00
-1.25
-1.50
-1.75
0.25
-0.0
0.0
-0.8
10
20
30
Fig. 2. IRF to a standard deviation monetary policy shock.
Ohio
0.2
-0.0
-0.2
-0.4
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-1.0
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20
30
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Rhode Island
-0.0
-0.4
-0.6
-0.8
-1.0
-1.2
20
30
40
50
Texas
0.2
-0.0
-0.2
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20
30
40
Washington
0.6
0.2
0.0
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-0.4
-0.6
10
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50
20
30
40
50
South Dakota
0.2
-0.0
-0.2
-0.3
-0.4
20
30
40
50
Utah
10
20
30
40
50
20
30
50
30
40
10
50
30
20
Virigina
-0.0
-0.2
-0.4
-0.6
-0.8
10
20
30
40
10
50
West Virgina
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
40
20
20
Tennessee
Vermont
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
Wisconsin
10
10
10
0.2
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.1
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
0.4
30
Pennsylvania
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
0.1
10
50
20
Oregon
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
South Carolina
0.3
0.2
0.1
-0.0
-0.1
-0.2
-0.3
-0.4
0.4
10
10
0.2
-0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-0.2
10
Oklahoma
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
20
Wyoming
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
10
20
30
40
50
10
20
Fig. 3. IRF to a standard deviation monetary policy shock.
the broad credit channel, and the narrow credit channel. These studies regress the asymmetric effects against measures of
these channels for each state. Only the first channel, measured by the share of interest rate-sensitive industries in each state,
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
Table 1
Summary of the IRFs. Relative response of state economy to a standard deviation monetary policy shock.
State
Levels %D 12 Months out
Levels %D 24 Months out
Levels %D 36 Months out
Significantly different than US > 24 Months
Michigan
West Virginia
South Carolina
Maryland
New Hampshire
Connecticut
Rhode Island
Pennsylvania
New Jersey
Ohio
Oregon
Missouri
Vermont
Alabama
Massachusetts
Illinois
Virginia
Indiana
Mississippi
Georgia
Minnesota
Florida
Arizona
North Carolina
Kentucky
California
Delaware
Arkansas
Maine
New York
Iowa
Wisconsin
Tennessee
Kansas
Washington
Utah
Nebraska
South Dakota
Nevada
New Mexico
North Dakota
Texas
Oklahoma
Colorado
Louisiana
Idaho
Montana
Wyoming
0.56
0.30
0.20
0.20
0.12
0.18
0.16
0.12
0.22
0.15
0.10
0.09
0.09
0.13
0.07
0.03
0.11
0.02
0.10
0.06
0.06
0.03
0.05
0.06
0.05
0.02
0.04
0.01
0.02
0.01
0.02
0.02
0.06
0.07
0.12
0.08
0.08
0.12
0.09
0.12
0.06
0.05
0.15
0.24
0.12
0.32
0.37
0.37
0.83
0.39
0.37
0.36
0.34
0.34
0.33
0.31
0.30
0.28
0.25
0.21
0.19
0.18
0.15
0.13
0.13
0.12
0.11
0.11
0.10
0.10
0.09
0.09
0.07
0.06
0.03
0.01
0.00
0.00
0.01
0.05
0.06
0.15
0.15
0.16
0.17
0.21
0.23
0.24
0.24
0.33
0.33
0.40
0.44
0.80
0.82
1.02
0.97
0.37
0.40
0.41
0.50
0.36
0.33
0.35
0.30
0.30
0.28
0.25
0.24
0.19
0.09
0.19
0.14
0.14
0.09
0.14
0.10
0.09
0.10
0.07
0.11
0.12
0.03
0.01
0.06
0.00
0.00
0.04
0.10
0.14
0.28
0.23
0.19
0.24
0.28
0.28
0.35
0.39
0.49
0.58
0.50
0.94
1.13
1.60
Y
Y
Y
Y
N
Y
N
Y
Y
Y
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Relative response is the state IRF minus the US IRF.
has been consistently successful in explaining some of the asymmetric effects.13 What these studies find is that those states
with a relatively high share of their state economy in interest sensitive industries, particularly manufacturing, are more susceptible to negative monetary policy shocks. Although these studies are not clear why, they also find that those states with relatively high shares of their economy in extractive industries are less affected by such shocks. These findings are consistent with
the geographical patterns of the asymmetric effects found in the previous section. Although interesting, these prior studies have
only found differences in industry mix to explain the asymmetric effects of monetary policy shocks. None of them have viewed
this issue through the perspective of the Optimal Currency Area (OCA) framework. The OCA is useful here because it provides
criteria to determine whether multiple regions are best served by a single monetary union. Although there is still some debate, a
common set of criteria that comes out of the OCA literature is that if one monetary authority will be conducting countercyclical
monetary policy for multiple regions then the regions must either share similar business cycles or have in place the economic
shock absorbers of factor mobility, flexible prices, federal fiscal transfers, and a diversified economy.14 If the regions share the
13
See Crone (2007) for a survey of these studies.
The seminal work in the OCA literature comes from Mundel (1961), McKinnon (1963), and Kenen (1969). See Mongelli (2002) for a survey of the OCA
literature. There are other OCA criteria in the literature, specifically economic openness, political commitment, financial integration, and similarity of assets
used as money as noted by Swofford (2000). They, however, are ignored here since they are not an issue for the United States.
14
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
739
same business cycle then monetary policy, which typically targets the aggregate business cycle or an anchor region’s business
cycle, should be stabilizing for all regions. If, on the other hand, there are regional economic shocks generating dissimilar business cycles among the regions, then one monetary policy will be destabilizing for some of the regions unless the above mentioned economic shock absorbers are in place. Consider, for example, a region in a currency union whose economy is not welldiversified and is slowing down because of a series of negative shocks to its primary industries. If the monetary authorities in
this currency union decide to tighten because the other regional economies are expanding too fast then the region slowing
down needs price flexibility, labor mobility, and federal fiscal transfers in order to offset the effects of the contractionary monetary policy. If these economic shock absorbers are absent, then this region would find this tightening of monetary policy to be
further destabilizing to its economy.15 In general, the greater the dissimilarity of a region’s business cycle with the rest of the
currency union the more important these economic shock absorbers become for the region to be a successful part of an OCA.
This understanding implies that how a region responds to countercyclical monetary policy relative to the other regions
provides a summary measure of whether that region shares a similar business cycle with the rest of the monetary union or
has in place the appropriate economic shock absorbers, the very criteria defining an OCA. One implication, then, is that the
asymmetric effects of monetary policy found in the previous section may be the result of some states not being a part of the
dollar OCA. Fig. 4 provides some evidence on this interpretation.
This figure plots for the three time horizons used in Table 1 the correlation between the state and the national economies
against the absolute value of the relative IRF from a standard deviation monetary policy shock. The correlation among the
state and the US economies is measured using the coincident indicator measure from the previous section and is based
on the entire sample period.16 Here again, the relative IRF is used since it shows the asymmetry of the state responses relative
to that of the region targeted by monetary policy, the United States. Like Carlino and DeFina (1998) the absolute value of this IRF
(AIRF) is used since the objective is to see whether the asymmetric responses, regardless of sign, can be explained by the OCA
criteria. Two important findings emerge from this figure. First, many state economies were not highly correlated with the US
economy over the sample period. Specifically, 24 states had economies that were correlated with the national one at a rate less
than a 70%. This finding is consistent with those of Owyang et al. (2005) and Crone (2005, 2006) who similarly show wide variation in US regional business cycles. Second, the greater the asymmetric impact of a US monetary policy shock on a particular
state the less correlated that state’s economy was with the US economy. This relationship becomes more pronounced the longer
the horizon, with as much as 44.43% of the variation explained at the 36-month horizon.17 One interpretation of this finding is
that US monetary policy significantly exacerbated business cycles in state economies that were not in sync with the national
one and lacked the economic shock absorbers needed to offset the monetary policy shocks. Some states, then, may not have
been a part of the dollar OCA over the 1983:1–2008:3 period.
Further evidence on this issue can be found in Table 2. This table reports the estimates from a series of regressions where
measures of the economic shock absorbers plus the same correlation measure are regressed against the AIRF used above. For
robustness and to be consistent with the previous analysis, the regressions are run using the 12-month, 24-month, and 36month AIRF as the dependent variable. The various OCA criteria are measured as follows
First, wage flexibility is measured by each state’s relative manufacturing wage while price flexibility is measured by each
state’s relative inflation rate. The relative manufacturing wage is constructed by taking the average percent deviation of a
state’s hourly manufacturing wage from the US hourly manufacturing wage for the period 1983:1–2008:3. The data comes
from the Bureau of Labor Statistics’ Current Employment Statistics survey. The state’s inflation rate is constructed by taking
the Bureau of Economic Analysis’ (BEA) nominal state GDP growth rate minus the real state GDP growth rate for the years
1983–2006.18 It is turned into a relative inflation rate by subtracting from it the US inflation rate and taking the average difference. Since the wage and inflation measures used here are rates of change relative to the national average, they should have a
mean of zero over the long-run if they are perfectly flexible and the law of one price holds. However, if a state has wages or
prices that are persistently higher than the national average then its wages and prices are downwardly sticky and less flexible
compared to the nation. If so, the state’s economy will be more susceptible to a negative monetary policy shock.
Second, following Eichengreen (1990), labor mobility is measured by looking at the relative persistence in a state to an
unemployment shock.19 The measure is constructed by taking the difference between the IRF 5 years out from a 1 unit shock
to a state’s monthly unemployment rate and a similar shock to the US monthly unemployment rate for period 1983:1–2008:3. A
simple 13-lag autoregressive model is estimated to get the IRFs.20 Here, the larger the IRF 5 years out the greater the unemployment persistence and the greater the labor immobility.
15
This region would have benefited at this time from having monetary autonomy through which it could have eased monetary policy and engineered a
competitive devaluation of its currency.
16
Correlations are calculated using the monthly growth rate of the coincident indicator.
17
All relationships were significant at the 1% level.
18
In both the manufacturing wage and state price level measures there is a break in the series due to the US government switching from the Standard
Industrial Classification (SIC) system to the North American Industrial Classification System (NAICS). However, both of these measures are constructed relative
to the US value making this break a non issue.
19
Only labor is considered for factor mobility since capital mobility is not an issue among the states. Kalemli-Ozcan et al. (2005) has shown that capital flows
among states are effectively frictionless.
20
Arguably, some of the persistence in the unemployment rate could come from sticky wages rather than labor immobility alone. However, in the regression
that follows the possibility of sticky wages is controlled for by relative manufacturing wage measure.
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D. Beckworth / Journal of Macroeconomics 32 (2010) 732–746
Fig. 4. Monetary policy shocks and regional business cycles. Note: AIRF = absolute value of state IRF minus US IRF. The correlation between the state
economy and US economy is based on the coincident indicator growth rates for states and the United States.
Third, for the diversification of each state’s economy, two measures are used in the analysis. The first one is a measure of
how similar the state and US economy are diversified among economic sectors. The more similar the state economy’s diversification is to that of the US economy, the more similar should be the state and US economies response to a monetary policy
shocks. This form of diversification is measured by taking the share of a state economy in a particular economic sector minus
0.103
(0.90)
Constant
54.75
46.83
(3.60)***
0.061
(0.12)
12.219
(2.34)**
(3.10)***
0.095
(0.30)
6.188
(2.09)**
44.60
34.90
3.543
59.25
52.12
(3.19)***
0.334
(0.50)
17.921
(2.54)**
5.636
0.142
(1.90)*
0.065
(0.40)
0.850
(2.00)*
11.112
(1.11)
0.126
(0.50)
(3)
36-Month
response
65.01
57.83
(3.38)***
(1.60)
47.30
36.49
0.477
(1.38)
0.178
(0.40)
13.865
(2.71)***
1.491
0.112
(2.60)**
0.003
(0.02)
0.613
(2.60)**
7.606
(1.04)
0.298
(1.40)
(5)
24-Month
response
0.120
(1.05)
0.155
(0.48)
6.603
(2.18)**
0.572
0.053
(2.67)**
0.083
(1.22)
0.469
(2.18)**
4.352
(1.31)
0.012
(0.10)
(4)
12-Month
response
Note: The dependent variable is the AIRF. *, **, and *** indicate 90%, 95%, and 99% significance levels, respectively.
R2 (%)
Adjusted R2 (%)
Similar business cycle
Coincident indicator
correlation
Industry portfolio volatility
Relative share: manufacturing
Economic diversification
Relative share: extractive
industries
1.089
0.136
(2.34)**
0.059
(0.46)
Factor mobility and fiscal transfers
Unemployment persistence
0.059
(2.56)**
Fiscal transfers
0.098
(1.39)
Relative inflation rate
0.688
(2.00)*
7.452
(0.99)
0.061
(0.30)
(2)
24-Month
response
0.488
(2.18)**
4.313
(1.46)
Wage/price flexibility
Relative manufacturing wage
(1)
12-Month
response
Variables
Table 2
Using the OCA criteria to explain the asymmetric effects of monetary policy shocks.
71.22
65.31
(4.03)***
0.706
(3.21)*
0.020
(0.04)
20.354
(3.21)***
2.603
0.106
(1.99)*
0.026
(0.19)
0.739
(2.53)**
11.340
(1.25)
0.406
(1.54)
(6)
36-Month
response
42.43
37.07
(3.07)**
0.160
4.330
(2.17)**
0.061
(2.69)***
0.416
(1.93)*
0.149
(2.98)***
(7)
12-Month
response
62.51
59.02
(5.50)***
0.552
13.235
(3.11)***
0.115
(2.84)***
0.651
(3.22)***
0.365
(3.57)***
(8)
24-Month
response
67.89
64.90
(4.55)***
0.871
20.668
(3.26)***
0.113
(2.06)**
0.791
(2.59)***
0.495
(4.17)***
(9)
36-Month
response
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the share of the US economy in that economic sector. The closer to zero this measure is for a state economy, the closer it is to
being diversified along the lines of the US economy. The second type of diversification measure is based on the idea that the
state economy should be allocated among economic sectors in a way that minimizes the volatility of its economic portfolio.
Following Conroy (1975), the volatility of the state economic portfolio can be measured as follows,
r2P ¼
N X
N
X
i¼1
wi wj rij ;
ð3Þ
j¼1
where wi and wj are the proportion of the state economy in economic sectors i and j, and rij is the covariance between returns in the two sectors. The square root of Eq. (3) is taken to get volatility in terms of the portfolio’s standard deviation.
Here, the more diversified the state economy is, the lower is its volatility. Lower volatility, then, should be associated with
fewer asymmetric effects arising from a monetary policy shock. With the first form of diversification, two specific measures
are constructed for each state: the relative share of manufacturing industries and the relative share of extractive industries.
The relative shares of manufacturing and extractive industries come from dividing their share of state personal income over
total state personal income and subtracting from the same ratio for the United States for the years 1983–2007.21 These latter
two measures are used given the importance previous studies have given them in explaining the asymmetric effects of monetary policy. For the latter form of diversification, one measure is constructed for each state: the volatility of a state economic
portfolio divided among the 10 major SIC industries making up personal income for the period 1983–2001.22 Here, sectoral returns are measured by the personal income generated in the 10 SIC industries.
Finally, for fiscal transfers this paper turns to the data provided by The Tax Foundation. This organization collects data on
net fiscal transfers between states and the federal government. Here, their ‘‘deficit neutral federal expenditures per dollar of
federal taxes” is used as the measure of fiscal transfers for the years 1983–2005.23 This measure captures the net federal inflow of funds into a state controlling for any implicit changes in each state’s tax liability arising from changes in the federal
budget deficit.
The first three columns of Table 2 report the regression results where only the economic shock absorbers are used to explain the 12-month, 24-month, and 36-month horizon of the AIRF.24 Only wage flexibility, labor mobility, extractive industry
diversification, and industry portfolio diversification are statistically significant. The signs for these estimates are as expected:
the higher the relative manufacturing wage (i.e. the lower the wage flexibility), the greater the unemployment persistence (i.e.
the less is labor mobility), the larger the share of extractive industries relative to the US economy, and the greater the industry
portfolio volatility the greater are the absolute responses of the state economies to a negative monetary policy shock. Interestingly, the estimated coefficients on these variables and the overall explained variation get larger the farther out the horizon.
Columns (4) through (6) repeat these regressions but now include the correlation measure. The same variables plus the correlation measure are statistically significant now except that the extractive industry share only becomes significant at the 36month horizon. These regressions indicate that variation in the amount of the economic shock absorbers and the correlation
among state and US economies can explain much of US monetary policy’s asymmetric effects. Columns (7) through (9) report
the best fitting models. These models explain between 42.43% at the 12-month horizon and 67.89% at the 36-month horizon.
This amount of explained variation in the AIRF is notably more than Owyang and Wall (2006), the only other study to look at the
post-1983 period, who are able to explain only about a third of the variation using the monetary policy transmission channels
approach. Table 3 reports on the economic significance of the OCA criteria in the best fitting models. Here, the standard deviation or typical change of each OCA variable across the states is multiplied by its regression estimate. This product can then be
compared against the average AIRF to see if the OCA variables individually are economically meaningful. The table also reports
the semi-partial R2 for each OCA variable. This table indicates that there are economically meaningful contributions made by all
the OCA variables. Their importance, however, changes over the horizons. Wage flexibility and labor mobility are more important during the first two horizons while the industry portfolio volatility and the correlation measure become more important
during the latter two horizons. Overall, then, the OCA criteria appears to provide a good framework for understanding the asymmetric effects of US monetary policy shocks. These findings suggest that some states of the United States may have gained from
having their own currency and monetary policy. This possibility is further explored in the next section of the paper.
6. States that may have benefited from having their own currency
The previous section found that a significant portion of the asymmetric effects generated by monetary policy shocks can
be explained by the OCA criteria. This suggests that the 20 states that had IRFs significantly different than the United States
21
Like before, the switch from the SIC to NAICS is not an issue since the relative manufacturing and relative extractive industry shares are being used. Instead
of splicing the series together at a particular year, though, the average of the relative shares calculated from both the SIC and NAICS are used for those years
where there is overlap (i.e. 1990–2001).
22
The 10 major SIC industries are as follows: agriculture; mining; construction; manufacturing; transportation and utilities; wholesale trade; retail trade;
financial insurance and real estate; service; and the public sector. Here, SIC and NAICS observations cannot be combined since relative values are not being
used.
23
2005 is the latest year of data availability.
24
All regressions were checked for heteroskedasticity using the Breusch–Pagan Test. Those regressions found to be plagued by heteroskedasticity were
reestimated with robust standard errors.
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Table 3
Economic significance of OCA criteria.
12-Month response
Relative manufacturing wage
Unemployment persistence
Industry portfolio variation
Coincident indicator correlation
Average AIRF
24-Month response
36-Month response
Standard
deviation OCA
estimates
Semi-partial
R2 (%)
Standard
deviation OCA
estimates
Semi-partial
R2 (%)
Standard
deviation OCA
estimates
Semi-partial
R2 (%)
0.045
0.035
0.022
0.036
0.122
15.32
8.18
3.72
9.14
–
0.070
0.065
0.067
0.124
0.245
9.06
7.02
8.41
26.39
–
0.085
0.064
0.105
0.195
0.302
7.14
3.64
10.94
35.03
–
Fig. 5. Variance decomposition of the state-specific shock.
for 25 months or more may have benefited from having monetary autonomy and their own currencies over the sample period. To see if this implication is robust, two more lines of analysis are considered: a decision rule based on the regression
results and an examination of the state-specific variance decomposition (VDC).
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Table 4
States that could gain from having their own currency.
State
Significantly different
Cum. IRF
Sufficient variation
explained by OCA criteria
Idiosyncratic
shocks > rest
of US svg.
Other considerations
Gains from own
currency?
CO
CT
ID
LA
MD
MI
MT
NE
NJ
NM
ND
OH
OK
PA
SC
SD
TX
UT
WV
WY
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
N
N
Y
Y
N
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Large sectoral
–
–
Large sectoral
–
Rustbelt
Large sectoral
–
Rustbelt
Large sectoral
Large sectoral
Rustbelt
Large sectoral
Rustbelt
–
–
Large sectoral
Large sectoral
Rustbelt
Large sectoral
Possibly
Possibly
Y
Y
Possibly
Y
Y
Y
Possibly
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
share of extractive industries
First, information from columns (7) through (9) from the above regressions allows for a simple decision rule as to which
of the 20 significantly different responses can be attributed to the OCA criteria. In particular, those states for which the
regression models predict an AIRF that as a percent of their actual AIRF are equal to or exceed the R2 from the regression
model should be counted as a state that may have gained from having its own currency. For example, since 67.89% of the
variation in the 36-month AIRF can be explained by the OCA criteria, then a particular state’s predicted AIRF should be at
least be that much as a percent of its actual AIRF at that horizon if it is to be meaningfully explained by the OCA criteria.
Since there are three 2 horizons, the decision rule is based on the average over the three horizons as follows: if
P R
PN ðPAIRFi =AIRFi Þ
P Ni¼1 Ni , then the state’s AIRF is explained sufficiently by the OCA criteria. Here, PAIRF is the predicted AIRF
i¼1
N
and i = 1, 2, and 3 are the regressions for the three horizons. Using this decision rule all the states except for Colorado and
South Carolina had AIRFs that could be sufficiently explained by the OCA criteria.
Second, the variance decomposition (VDC) of each state’s coincident indicator growth rate attributed to economic shocks
originating from within the state can be examined to see how similar it is with other states. This measure shows the relative
contribution (out of a 100%) of the state-specific shocks to the mean squared forecast error (MSFE) generated by the VAR. The
larger the VDC number, the greater the percentage of MSFE that is explained by the state-specific shock. In turn, the more a
state’s MSFE is dissimilar with the rest of the states the more likely it would have benefited from having its own currency.
Fig. 5 presents the VDCs for the 20 states and for ease of reading splits them into Eastern and Western regions. This figure
shows that state-specific shocks explain most of the MSFE early on, but then declines until about 2 years out. The thick, solid
black shows the weighted average state-specific shock VDC for the other 28 states.25 This line shows that for the rest of the
United States the state-specific shock explains on average about 31% of the MSFE two years out. Except for West Virginia, this
response is roughly the same for the rustbelt states in the Eastern region. It is however, noticeably greater than the other Eastern
states of Connecticut, Maryland, New Jersey, and South Carolina. This suggests these other Eastern states may actually be in the
dollar OCA. On the other hand, all of the Western states have far more of their MSFE explained by the state-specific shock indicating there may have gained from having monetary autonomy. Interestingly, the states in the Western region were the ones
that significantly better than US AIRF while the ones in the Eastern region did worse.
Together, these findings lend support to the view that parts of the United States may have benefited from having their
own currency. Table 4 summarizes these findings along with the results. The table also makes note of other pertinent considerations. For many of the states, it is noted that they are either part of the Rustbelt or have a large share of their economy
is in the extractive industries compared to the US average.26 The last column in the table forms a tentative conclusion as to
whether the state would have gained from having monetary autonomy and its own currency. A state was determined to be in
this group if it either (1) met all three criteria listed in the table or (2) met two of the three criteria but had a sufficiently strong
justification for being included anyways. These criteria resulted in 15 states falling outside the dollar OCA: Idaho, Louisiana,
Michigan, Montana, Nebraska, New Mexico, North Dakota, Ohio, Oklahoma, Pennsylvania, South Dakota, Texas, Utah, West Virginia, and Wyoming. Of these states, only Michigan was counted under the latter criteria given it had the largest negative IRF in
the nation. These 14 states can be divided into those that fall in the Western and Eastern region or what Crone (2005) calls the
25
26
The states are weighted by their size of their economies.
Industry shares are calculated using sectoral shares of personal income.
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Energy Belt and the Rustbelt. While the findings of this paper can only speak to the potential gains from a state having its own
currency, it does suggest that if these gains were greater than the increased transaction costs that came with such a move to
regional monetary autonomy then the Energy Belt, which did better than the US economy, and the Rustbelt, which did worse
than the US economy, would most likely form two separate currency areas distinct from the dollar union.
7. Conclusion
This paper has shown that the regional asymmetric effects generated by US monetary policy over the period 1983:1
through 2008:3 can be attributed, in part, to regions of the United States being constrained by the dollar currency union
and its monetary policy. These findings imply that some states may have benefited from having their own currency at this
time. Two regions in particular were found to be likely beneficiaries of such monetary autonomy: the Energy Belt and the
Rustbelt. The Rustbelt may have benefited over the past decade and a half from a depreciated currency coming from a looser
monetary policy that would have made its manufacturing exports more competitive. Instead, it was stuck with the US dollar
that until 2002 was appreciating. The Energy Belt, on the other hand, may have benefited from having a slightly tighter monetary policy. Although this paper speaks only to the potential gains of breaking up the US monetary union and not to the
added transaction costs, it does imply there may be some benefit to regional monetary autonomy. A move to regional monetary policies may seem radical, but it is not something new to the United States. Between 1838 and 1860 states were able to,
and in some cases did, undertake state-level monetary policy (Shambaugh, 2006).27 During the US Civil War and up through
1879 there were two currencies and thus two regional monetary policies. Yellowbacks—gold-backed dollars—circulated in the
far West and Greenbacks—fiat dollars—were everywhere else (Rockoff, 2000). In 1913, the original framers of Federal Reserve
designed the system so that discount lending and open market operations could be independently conducted at the district
banks. The Federal Reserve System, therefore, originally allowed for monetary policy to be determined at the district level (Meltzer, 2003).28 While regional currencies in the United States presently seem unlikely, limited regional monetary autonomy could
still emerge if Federal Reserve district banks were allowed more discretion in running their discount windows. Doing so would
allow for regional changes in liquidity to offset national monetary policy deemed inappropriate for the regional economy.29 A
final implication of this paper is that since the United States is most likely not an OCA, policies should be promoted that keep
wages and output prices flexible, labor mobile, and fiscal transfers meaningful and targeted appropriately. Together, these two
policies should serve to bring the United States closer to becoming a true OCA.
Acknowledgement
I would like to thank William Lastrapes, Danny Hughes, Lenny Gashugi, Jim Swofford, and several anonymous referees for
helpful comments and suggestions. Of course, all remaining errors are mine.
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