Regional house price dynamics and voting behavior in the FOMC.
Eichler, Stefan ; Lahneft, Tom
I. INTRODUCTION
Interest rate decisions by the Federal Open Market Committee (FOMC)
are not always made unanimously. Different individual views about the
appropriate monetary policy stance may lead to the result that members
cast dissenting interest rate votes in FOMC meetings, that is, they opt
for a higher or lower interest rate than proposed by the Chairman of the
FOMC. Several studies find that FOMC members have different views about
the appropriate monetary policy when casting interest rate votes (Belden
1989; Chappell and McGregor 2000; Chappell, McGregor, and Vermilyea
2005, 2008, 2012; Gildea 1990, 1992; Havrilesky and Gildea 1991, 1995;
Havrilesky and Schweitzer 1990; Meade and Sheets 2005; Meade 2010;
Tootell 1991, 1996). An important source of such dissenting votes may be
the regional bias of the FOMC member, which is constituted by its
regional affiliation. Several studies have examined the impact of the
regional unemployment rate on the interest rate votes of FOMC members.
While Gildea (1992), Meade and Sheets (2005), and Chappell, McGregor,
and Vermilyea (2008) find that a higher regional unemployment rate
relative to the national rate increases (decreases) FOMC members'
preference for monetary easing (tightening), Tootell (1991) finds no
significant evidence. (1)
In this article, we analyze whether FOMC members align their
interest rate voting behavior with house price developments of the
Federal Reserve district they represent (in addition to the regional
unemployment rate studied so far). In this way, we can test whether
regional house prices and/or regional unemployment rates have a
significant impact on the voting behavior of FOMC members and we examine
whether there are systematic differences between regional Bank
presidents and Board members.
Various studies have examined the link between monetary policy and
asset price developments. According to Gilchrist and Leahy (2002), there
are some good reasons why central banks should align their monetary
policies with asset prices. First, the consumer price index (CPI) or
gross domestic product (GDP) deflator are incomplete measures since they
only signal information on the prices of goods consumed today, whereas a
more complete measure would also signal future cost of living, such as
the information contained in asset prices (Alchian and Klein 1973).
Second, asset prices may be useful in forecasting inflation (Goodhart
and Hofmann 2000). Third, asset prices may directly affect real economic
activity and inflation since, for example, wealthier consumers spend
more, which may affect price developments (Carroll, Otsuka, and Slacalek
2011).
Whether central banks should react to asset prices is, however,
debated in the literature. The traditional view is that central banks
should only react to asset prices to the extent that they feed back into
the conventional monetary policy goals inflation and output (Bernanke
and Gertler 1999, 2001). On the contrary, Cecchetti et al. (2000)
conclude that it may be reasonable for central banks to react to asset
prices (in particular house prices) in order to avoid the buildup of
asset price bubbles. As the burst of asset price bubbles may lead to
financial crises (Reinhart and Rogoff 2009), leaning against the buildup
of extreme asset price changes may therefore be a reasonable choice for
central banks as a part of their mission to manage systemic risk (Allen
and Carletti 2009; Bordo and Jeanne 2002). Another rationale that
central banks should take asset prices, particularly house prices, into
account is that house prices have been shown to be suitable early
warning indicators for the outbreak of the recent financial crisis
(Kemme and Roy 2012).
Several empirical papers analyze the impact of asset prices on
interest rate setting. Rigobon and Sack (2003) and Dupor and Conley
(2004) find evidence that FOMC interest rate decisions are influenced by
equity prices. On the contrary, Fuhrer and Tootell (2008) find that FOMC
interest rate decisions are not a direct response to equity price
movements and are an indirect response only to the extent that equity
prices helped forecast the conventional monetary policy goals. For
Europe, Botzen and Marey (2010) find evidence of a monetary policy
response to equity prices in the European Central Bank, while Bohl,
Siklos, and Werner (2007) do not find such evidence for the Deutsche
Bundesbank in the pre-Euro era.
To the best of our knowledge, we are not aware of any study that
analyzes the impact of regional house prices on the voting behavior in
the FOMC. (2) The subprime crisis has shown that monitoring house prices
should be of crucial importance for central banks. Taylor (2007) argues
that the Federal Reserve has kept interest rates too low for too long
after 2001 and that the rapid interest rate swing in 2005-2006 has
contributed to the boom-bust cycle in U.S. house prices. There have
been, however, regional differences with California, Florida, Arizona,
and Nevada experiencing the most pronounced boom--bust cycles in house
prices while Texas and Michigan had a relatively remote development in
house prices (Taylor 2009). Given these regional differences in house
price dynamics, it may be difficult for a central bank to implement
interest rates that fit all regional real-estate markets at one time.
(3) Thus, it remains an empirical question as to whether monetary
policymakers do indeed react to house prices.
As interest rate decisions are committee decisions in the United
States, a FOMC member may dissent from the consensus interest rate
decision as this rate may not suit the development of house prices in
the region he/she represents. We analyze the regional dimension of the
impact of house prices on monetary policy by investigating the impact of
the (heterogeneous) regional house price developments on the interest
rate decisions of regionally affiliated FOMC members. In order to
measure the voting behavior in the FOMC, we use either a categorical
dissents indicator (presenting dissenting votes of each member against
the majority decision in the FOMC) or a categorical voting indicator
(presenting actual interest rate votes of each member). Both models use
data from 1978 to 2010 and are estimated using a random effects ordered
probit model. Our analysis seeks to answer three research questions: Is
the voting behavior of FOMC members affected by regional house price
developments in their district? Do Bank presidents and Board members
differ with respect to such a house price-related voting pattern? How
large is the impact of regional house prices on the voting behavior in
the FOMC as compared to the effect of the regional unemployment rate?
II. DESCRIPTIVE EVIDENCE
The Federal Reserve System is structured in 12 Federal Reserve
districts that are represented in the FOMC. The FOMC consists of 12
monetary policymakers, including five voting Federal Reserve Bank
presidents (4) who come directly from the district they represent, and
seven members of the Board of Governors, who are only legally (5)
affiliated with the district they represent but are located at the main
office in Washington, DC. (6) Considering regional affiliations, it is
often argued that Bank presidents should react more sensitively to
changes of regional economic conditions than Governors should. (7)
In order to analyze interest rate decisions of individual FOMC
members, we use the minutes of the FOMC meetings that have been used by
several other papers dealing with interest rate voting. FOMC meeting
minutes provide information as to whether each voting member agrees with
the interest rate decision of the committee (coded as 0), dissents in
favor of a tighter monetary policy, indicating a higher preferred
interest rate (coded as +1), or dissents in favor of easier policy,
indicating a lower preferred interest rate (coded as -1).
Table 1 summarizes the heterogeneity in the voting behavior of the
representatives of the 12 Federal Reserve districts in the period from
1978M3 through 2010M9. (8) Overall, dissents are quite rare. Members of
the FOMC have cast dissenting votes relative to the majority of just
6.86% out of 3,264 recorded votes during the sample period. Bank
presidents have cast dissenting votes more frequently than Board members
(9) (8.22% vs. 5.75%).
Nearly 70% of all dissents were cast in favor of tighter monetary
policy and just about 30% in favor of easier monetary policy (155 vs.
69, respectively). Bank presidents generally show a tendency toward
tighter dissents, while Board members more frequently cast easier
dissents. Around two-thirds of tighter dissents were cast by Bank
presidents and only one-third by Board members. Around 77% of easier
dissents were cast by Board members and only 23% by Bank presidents.
Notably, all dissents in the district of Atlanta were cast in favor of
tighter monetary policy and only by Bank presidents, as opposed to the
Chicago district where only Board members cast dissenting votes and only
for easier monetary policy stance. Dissents from Kansas City, Cleveland,
St. Louis, Dallas, Richmond, and Minneapolis districts were mainly cast
by Bank presidents and in favor of a tighter monetary policy. For
Boston, we find dissenting votes in favor of monetary tightening mostly
cast by Board members. The districts of San Francisco and New York show
no clear voting behavior. To summarize, this descriptive analysis shows
that Bank presidents in particular tend to dissent more frequently for
tighter monetary policy than Governors do.
In order to study whether voting behavior in the FOMC is driven by
regional house prices, we use house price data provided by the Federal
Housing Finance Agency and taken from the Federal Reserve Bank of St.
Louis. This house price index is a weighted repeated sales index and
tracks the development of single-family house prices in each U.S. state.
(10) In order to study the relevance of house prices for a possible
regional bias in FOMC voting, we focus on the regional house price gap,
which is defined as the deviation of regional house prices from their
time trend. Similar to the output gap used in the literature, we opt for
using a house price gap (in contrast, for example, to the case of the
national unemployment rate where the raw number is used) since in all
considered states' house prices show a long-term upward trend and
therefore monetary policymakers may be concerned about significant
deviations from that trend (similar to the case of output). We calculate
the regional house price gap in the following way. First, we compute
percentage deviations of the house price index from its time trend for
each U.S. state using the Hodrick--Prescott filter. Second, we construct
the district house price gap by weighting the state house price gaps by
population shares based on county level data. (11) Positive values of
the house price gap indicate that regional house prices are above the
time trend and should be associated with a voting behavior in favor of
monetary tightening of this district's FOMC member. Negative values
of the house price gap indicate that house prices are below the time
trend and should be associated with a voting behavior in favor of
monetary easing.
[FIGURE 1 OMITTED]
Figure 1 illustrates the development of the house price gaps of
each Federal Reserve district. From the late 1970s to the late 1980s,
many districts show a clear boom-bust period, where house price
volatilities have been considerably high, particularly in the districts
of Boston, New York, Philadelphia, and Cleveland. During the 1990s house
prices show a relatively remote behavior with levels near their time
trends. During the mid-2000s house prices rose markedly above their time
trends in several districts. The subsequent bust with house prices
falling below their trend levels starting at the beginning of 2007 was
most striking in the districts of Atlanta, Richmond, and San Francisco.
Notably, the San Francisco district, as the region with the highest
economic importance, experienced the most volatile house prices during
the last decade with a house price gap ranging from 12% at the beginning
of 2006 to -8% at the beginning of 2009.
Table 2 summarizes the heterogeneity of regional house price gaps
with respect to Federal Reserve districts and time periods. The first
period, lasting from 1978 to 1989, shows high volatilities in almost
every single district, particularly in Boston, New York, Philadelphia,
and San Francisco (5.1,4.7, 3.2, and 2.7, respectively). The 1990-1999
period is characterized by relatively tranquil house prices with low
volatilities ranging between 0.5 and 1.8. Finally, the 2000-2010 period
shows high volatilities in the U.S. housing markets associated with the
subprime crisis, especially in San Francisco, Atlanta, Richmond, New
York, and Philadelphia (with volatilities of 8.5, 4.7, 3.4, 3.2, and
2.2, respectively).
Table 3 describes the link between FOMC dissenting votes and
regional house price gaps. The data suggest that during periods with
high house price volatilities (periods I and III) FOMC members tend to
dissent more frequently for tighter (easier) monetary policy when the
regional house price is above (below) its long-term trend. For
district/period cases highlighted in dark gray color, the majority of
dissents show such a voting pattern--i.e., positive house price gaps (p)
correspond to tighter dissents (+1) and negative house price gaps (n)
correspond to easier dissents (-1), while light gray color is used for
cases where the majority of dissents show a contrary voting pattern.
Overall, this descriptive evidence supports our hypothesis that interest
rate voting may be influenced by regional house price developments.
During the first period (1978-1989) we find evidence for such a voting
pattern for Boston, Cleveland, and San Francisco in particular, where
house price gap volatilities are relatively high. For the period
2000-2010, we find this pattern particularly for the districts of
Philadelphia, Richmond, and San Francisco with their high house price
volatilities. For the middle period, with its relatively remote house
price developments, such a clear voting pattern cannot be detected.
There are also districts that provide contradictive evidence to our
hypothesis (underlined in light gray color) such as Chicago (12) and
Kansas. However, in these districts house prices are much less volatile
and therefore voting dissents may be driven by other factors, such as
unemployment rates. All in all, descriptive evidence suggests that FOMC
members seem to align their interest rate voting with regional house
prices.
III. REGRESSION ANALYSIS
A. Hypothesized Determinants
Our dataset comprises several additional control variables,
including regional, national, and institutional variables. Table A1
provides an overview of definitions and sources of the variables. Table
A2 provides some summary statistics. Beside the regional house price gap
our dataset contains the regional unemployment rate, which is calculated
as the difference between the district's unemployment rate and the
national unemployment rate. (13) The per capita value of failed assets
of regional banks is included to control for a possible regional bias of
FOMC members accounting for trouble in the banking sector of their
district. As a fourth regional variable, we include the regional
coincident index, which reflects current economic conditions in the
district (as measured using various regional labor market indicators).
As already outlined, we assume that a larger regional house price gap is
assumed to increase (decrease) the probability of voting for monetary
tightening (easing). Thus, a positive coefficient is predicted. Higher
levels of the regional unemployment rate and failed assets of regional
banks per capita and a lower regional coincident index reflecting
deteriorating regional economic conditions are assumed to increase
(decrease) the incentive to vote for monetary easing (tightening). These
hypotheses are based on the assumption that FOMC members take actual
economic developments in their district into account when casting
interest rate votes in the FOMC.
We include a number of national macroeconomic variables, in
particular the national house price gap, the national industrial
production gap, the national unemployment rate, the national inflation
rate, a commodity price index, and an exchange rate index. Additionally,
we include the 1-year ahead forecasts for the national industrial
production gap, unemployment rate, and inflation rate (provided by the
Survey of Professional Forecasters) in order to test whether FOMC
members rely on forward-looking variables when deciding about their
votes. For all national variables (except for the actual and expected
unemployment rate), a positive coefficient is predicted for the voting
model, since higher values indicate a higher risk of inflationary
pressure and overheating of the national economy, which constitutes the
need for monetary tightening. For the dissents model, it is not a priori
clear to formulate hypotheses for these national variables since they
should determine the consensus among FOMC members concerning the
appropriate interest rate and may therefore have no major effect on the
dissenting behavior. In addition, we include the previous funds rate in
order to test for an autoregressive voting pattern. We use all regional
and national variables 1 month lagged in order to account for the fact
that data for the voting month is only available around 1 month after
the meeting. (14)
We also use several institutional dummy variables including tape,
which is the date when all FOMC members became aware that the meetings
were being tape recorded, (15) a dummy indicating as to whether the FOMC
meeting was a face-to-face meeting or a conference call, a dummy
indicating as to whether the voting member is a Board member or Bank
president, and time dummies for the FOMC meetings under chairmen
Volcker, Greenspan, and Bernanke. These institutional characteristics
may have a systematic influence on voting behavior although the expected
direction of the influence is not clear a priori.
B. Regression Results of the Dissents Model
In order to test the impact of regional house prices on FOMC
members' voting behavior, we use two alternative dependent
variables: a dissent indicator (used in this subsection) and a vote
indicator (used in the following subsection). The dissent indicator is
an ordered categorical variable. For each FOMC meeting, the minutes
published by the Board of Governors of the Federal Reserve provide
information for each voting member as to whether the member agrees with
the interest rate decision of the committee (coded as 0), dissents in
favor of a tighter monetary policy with a higher preferred interest rate
(coded as +1), or dissents in favor of an easier monetary policy with a
lower preferred interest rate (coded as -1). This coding procedure
follows previous studies such as Gildea (1990, 1992), Chappell and
McGregor (2000), and Meade and Sheets (2005). In order to account for
the categorical nature of the dependent variable, we use an ordered
probit model to test our hypotheses. In order to account for the
unobserved heterogeneity among Federal Reserve districts, we use a
random effects estimator for the ordered probit model. (16)
We estimate our regression models for three datasets. The full
sample uses FOMC interest rate votes of Board members and Bank
presidents together. (17) The second dataset only considers votes of
Bank presidents and the third dataset only uses data on votes of Board
members. We estimate four specifications for each dataset which consider
different combinations of regional, national, and institutional control
variables to check for the robustness of the results. In order to assess
the economic significance of the regional house price gap for the FOMC
voting pattern, we compute marginal effects, which give the change in
the probability of casting easier dissents (category -1), tighter
dissents (category +1), or voting with majority decision (category 0)
for a one unit change in the explanatory variable. Tables 4 and 5 report
the estimation results and the marginal effects (18) for the dissents
model.
Overall, the regression results confirm our hypotheses. For the
full dataset comprising Bank presidents and Board members, the
coefficient of the regional house price gap is highly significant and
shows the expected positive sign in each specification. Inspecting the
marginal effects reveals the result that a one standard deviation
increase in the regional house price gap (being 2.744) raises the
probability of tighter dissents by around 0.73% (19) (category +1) and
decreases the probability of easier dissents (category -1) or the
agreement with the majority (category 0) by 0.28% and 0.45% on average,
respectively. These results suggest that FOMC members generally take
regional house price developments into account when deciding about
dissenting votes.
The results for the subsamples reveal significant differences with
respect to the impact of the regional house price gap between Bank
presidents and Board members. While for Bank presidents the coefficient
of the regional house price gap is positive and significant in each
specification, for Board members we find a weakly significant effect
only in one specification. Comparing the marginal effects of Bank
presidents and Board members also yields interesting results. The
average marginal effect of an increase in the regional house price gap
by 1 standard deviation on casting tighter dissents is about 1.33% for
Bank presidents and about 0.19% for Board members. Regarding easier
dissents, we find that the standardized average marginal effects for
both groups are about the same. All in all, our results suggest that
regional house price developments have a much greater effect on the
voting behavior of Bank presidents than of Board members.
Turning to the control variables, we find that both Bank presidents
and Board members significantly align their voting behavior with the
regional unemployment rate (confirming the results of previous studies,
e.g., Chappell, McGregor, and Vermilyea 2008; Gildea 1992; Meade and
Sheets 2005). A rise in the regional unemployment rate (relative to the
national level) reduces the likelihood for casting tighter dissents and
increases the likelihood for casting easier dissents. This effect is
more pronounced for Board members than for Bank presidents. A 1 standard
deviation increase in the regional unemployment rate increases the
probability that a Bank president casts a dissent in favor of easier
monetary policy by 0.27% on average, while this standardized marginal
effect is 1.00% for Board members. A 1 standard deviation increase in
the regional unemployment rate reduces the probability of tighter
dissents, on average, by 1.42% for Bank presidents and by 1.06% for
Board members.
A comparison of the economic significance of the regional house
price gap and the regional unemployment rate yields interesting
implications for monetary policy goals of both types of FOMC members.
For the full sample, the impact of a 1 standard deviation change in the
regional unemployment rate on the probability of casting a dissenting
vote is twice as high as for the regional house price gap. A comparison
of the inflation- and output orientation of Bank presidents and Board
members yields the following results. For Bank presidents we find that
the regional house price gap and the regional unemployment rate have
similar standardized marginal effects, while for Board members the
marginal effect of the regional unemployment rate is around four times
as high as for the regional house price gap.
Thus, while Bank presidents' dissenting votes significantly
depend on both the regional house prices and the regional unemployment
rate, Board members seem to align their dissents much more with the
regional unemployment rate than with the regional house prices. The
significant differences in the voting behavior of both types of FOMC
members may be explained by several aspects. First, Bank presidents are
typically supposed to have a more pronounced regional bias due to their
stronger affiliation to the Federal Reserve region they represent, while
Board members' regional affiliation is more constituted on a de
jure basis. Due to their closer de facto regional affiliation Bank
presidents may be more aware of regional house price developments than
Board members who serve in Washington, DC and have fewer opportunities
to monitor changes in regional house prices. Bank presidents maintain
frequent contacts in the business community of their region and regional
businessmen may provide them with information concerning regional
economic conditions including house price developments. (20) Bank
presidents therefore typically have an information advantage over Board
members concerning regional house prices and should more probably align
their voting behavior with regional house price developments. Second,
difference in the voting behavior of Bank presidents and Board members
may also be explained by different preferences with respect to monetary
policy goals. Board members are appointed by the President of the United
States, while Bank presidents are elected by the Board of Directors of
their regional Federal Reserve Bank. These differences in the
appointment process may yield the result that Board members share the
government's preference for output stabilization to a much greater
extent than Bank presidents who may want to stabilize both output and
inflation. Since house price dynamics may indicate both inflation and
output risks, it seems to be a reasonable result that the voting
behavior of Bank presidents depends much more on regional house prices
than the voting behavior of Board members who put more weight on the
unemployment rate. A third explanation for the different voting behavior
may be that Board members may less likely perceive house prices as a
relevant monetary policy goal than Bank presidents. According to the
traditional view, monetary policy should only be conducted toward
stabilizing output and inflation and asset prices should be relevant
solely to the extent that they feed back into output and inflation (see,
for example, Bernanke and Gertler 1999, 2001). Board members may more
probably share this traditional view as the Board of Governors and the
Board's staff are active in shaping such consensus views to make
monetary policy explicable. Bank presidents may have less orthodox views
about monetary policy goals and may more likely align their voting
behavior with a multitude of economic variables including regional house
prices.
Turning to other regional control variables, we find that current
regional economic conditions (measured by the coincident index) have a
significant impact on the voting behavior, while trouble in the regional
banking sector does not. For the national variables we generally find no
significant impact on dissenting votes in the FOMC. This suggests that
FOMC members agree on average with the committee's decisions to
change the interest rate based on national economic indicators. The
previous funds rate is also insignificant, pointing to no autoregressive
behavior in voting dissents. Regarding the institutional variables, we
find that Board members show significant preferences for a more
expansive monetary policy than Bank presidents, confirming the findings
of previous studies (see, e.g., Belden 1989; Flavrilesky and Gildea
1995; Meade and Sheets 2005). In contrast to previous studies (e.g.,
Meade and Stasavage 2008; Meade 2010), the tape dummy is insignificant
in all regressions, indicating that a higher transparency of FOMC
meetings does not influence the members' probability to dissent.
For the meeting dummy we generally find a positive impact on dissents,
but this effect is only significant for Bank presidents. This suggests
that Bank presidents more often choose face-to-face meetings than
conference calls when dissenting in favor of a tighter monetary stance.
C. Regression Results of the Voting Model
In order to check for the robustness of our results we estimate all
specifications using vote as the dependent variable. This ordered
categorical variable indicates as to whether a FOMC member votes in
favor of an interest rate increase (+1), an interest rate decrease (-1),
or an unchanged interest rate (0). (21) This alternative indicator also
signals the monetary preferences of FOMC members and allows us to study
the impact of regional house prices on regionally affiliated FOMC
members' voting behavior. Similar to the dissents model, we expect
that a higher regional house price gap in the member's Federal
Reserve district would lead to a higher probability of votes in favor of
a tighter monetary policy, i.e., higher interest rates (+1), while lower
regional house price gaps should be associated with a higher probability
of votes in favor of easier monetary policy, i.e., lower interest rates
(-1). In contrast to the dissents indicator (which shows solely
deviations from the consensus interest rate decision), the vote variable
should be more sensitive toward variation in national variables since
they should shape the consensus view about the appropriate monetary
policy stance. Similarly to the dissents indicator, the vote variable
also captures disagreement among FOMC members, since the majority view
of interest rate increase or decrease may not be shared by FOMC members
with a preference for easier or tighter monetary policy, respectively.
We estimate the same specifications for the three datasets using
the random effects ordered probit model as for the dissents model. The
estimation results and marginal effects for the voting model are
reported in Tables 6 and 7, respectively.
The results generally confirm the findings of the dissents model.
The coefficient of the regional house price gap is positive and
significant in most specifications indicating that an increase in the
regional house price gap is associated with a higher probability of
voting in favor of interest rate increases (or, equivalently, a lower
probability of voting in favor of interest rate decreases). This result
generally confirms the findings of the dissents model that larger
heterogeneity in house price dynamics among Federal Reserve districts
results in larger heterogeneity in the stabilization needs of the
districts, which, in turn, leads to more disagreement in FOMC voting.
Inspecting the marginal effects (see Table 7) reveals that the average
standardized marginal effect of the regional house price gap on votes
for Bank presidents is around 50% higher than the average marginal
effect for Board members. (22) This result resembles the findings of the
dissents model that Bank presidents align their voting behavior in the
FOMC with regional house prices to a much greater extent than Board
members do, due to their supposedly more intense regional affiliation
and the associated better information about regional house price
developments, more pronounced preferences for fighting (regional)
inflation, and a more pragmatic view of monetary policy goals.
The regional unemployment rate is insignificant in most
specifications, while the coefficient of the regional coincident index
is highly significant and has the predicted positive sign. In contrast
to the dissents model, the national inflation rate and the national
industrial production gap, as well as the forecasts of these variables,
play a highly significant role for voting in the FOMC. These findings
confirm our hypothesis that national variables determine the consensus
among FOMC members concerning the appropriate national interest rate and
thus affect the voting behavior in the FOMC. The insignificant results
for the regional unemployment rate may be explained by a possible
correlation with the national variables. (23) Moreover, higher commodity
prices are associated with votes for tighter monetary policy, which
suggests that FOMC members anticipate the risks of inflation pressure
exerted by higher commodity prices. In line with the dissents model, we
find that significantly tighter interest rate votes are cast during
regular meetings (as opposed to conference calls) and by Bank presidents
(as opposed to Board members).
IV. CONCLUSIONS
Using FOMC voting records over the period 1978M3-2010M9, we find
that regional house price developments significantly influence the
voting behavior in the FOMC. A 1 standard deviation increase in the
regional house price gap raises the probability of tighter dissents by
around 0.73% and decreases the probability of easier dissents by 0.28%.
We find that particularly Bank presidents take regional house prices
into account when casting (dissenting) interest rate votes while for
Board members this effect is mostly insignificant or small. Board
members, on the contrary, are more concerned about the regional
unemployment rate while this effect is much smaller, though also
significant, for Bank presidents. Overall, Bank presidents appear to be
much more focused in regional house prices when casting (dissenting)
votes in the FOMC than Board members. This result may be explained by
Bank presidents' stronger regional affiliation and the associated
better awareness of regional house price developments, their stronger
preferences for fighting inflation, and their more pragmatic view about
monetary policy goals.
ABBREVIATIONS
CPI: Consumer Price Index
FOMC: Federal Open Market Committee
GDP: Gross Domestic Product
doi: 10.1111/ecin.12050
APPENDIX
TABLE A1
Definitions and Sources of Variables
Variable Definition
Dissent Dependent variables FOMC member from Federal
Reserve district / dissents either in favor
of tighter (+1) or easier (-1) monetary policy
or agrees with the majority (0)
Vote FOMC member from Federal Reserve district i
votes in favor of interest rate increase (+1),
interest rate decrease (-1), or unchanged
interest rate (0)
Regional variables (a)
Regional house Percentage deviation of district z's house
price gap price index from time trend State-specific
house price gap is calculated as percentage
difference between state-specific house price
index and Hodrick-Prescott-based time trend;
smoothing parameter for the Hodrick-Prescott
filter was set to 1,600; quarterly house price
indexes are interpolated to monthly data using
the cubic spline method
District-specific house price gap is the
weighted average of state-specific house price
gaps (district boundaries are taken from
Chappell et al. 2008), population shares are
used as the weighting scheme
Regional Difference between unemployment rate in i's
unemployment rate district and national unemployment rate
District unemployment rate is the weighted
average of state-specific unemployment rates
(district boundaries are taken from Chappell
et al. 2008), population shares are used as
the weighting scheme
Failed assets Failed assets of insolvent banks per capita
of regional banks in district i
District failed assets is the weighted average
of price-deflated state-specific failed assets
(district boundaries are taken from Chappell
et al. 2008), population shares are used as
the weighting scheme
Regional Index reflects current economic conditions in
coincident index a state combining nonfarm payroll employment,
average hours worked in manufacturing, the
unemployment rate, and wage and salary
disbursements. The trend for each state's index
is set to the trend of its gross domestic
product (GDP), so long-term growth in the
state's index matches long-term growth in
its GDP.
Index is used as month-over month percentage
change. Difference between coincident index
in voter i's district and national
coincident index
District coincident index is the weighted
average of state-specific coincident indexes
(district boundaries are taken from Chappell
et al. 2008), population shares are used as
the weighting scheme National variables (b)
National house Percentage deviation of national house price
price gap index from Hodrick-Prescott-based time trend;
smoothing parameter for the Hodrick-Prescott
filter was set to 1,600; quarterly house price
indexes are interpolated to monthly data using
the cubic spline method
National National unemployment rate
unemployment rate
National Month-over-month percentage change in consumer
inflation rate price index
National industrial Percentage deviation of national industrial
production gap production index from Hodrick-Prescott-based
time trend; smoothing parameter for the
Hodrick-Prescott filter was set to 14,400
Commodity Quarter-over-quarter percentage change in S&P
price index GSCI Commodity Spot Price Index
Exchange rate index Quarter-over-quarter percentage change in
trade weighted nominal dollar exchange rate
index; higher values indicate depreciation
of the U.S. dollar
Inflation forecast Inflation forecasts are made by professional
forecasters published in the quarterly
Survey of Professional Forecasters
Unemployment Unemployment rate forecasts are made by
rate forecast professional forecasters published in the
quarterly Survey of Professional Forecasters
Industrial Industrial production forecasts are made by
production forecast professional forecasters published in the
quarterly Survey of Professional Forecasters
Previous funds rate Federal funds rate of the Wednesday prior to
the FOMC meeting Institutional dummy variables
Tape Dummy variable indicating the date since when
FOMC members were aware of the fact that the
meetings are being tape recorded; equals 1
from 1993M11 through 2010M9 and 0 otherwise
Meeting Dummy variable; equals 1 if vote cast at
face-to-face meeting, 0 if vote cast at
conference call
Board member Dummy variable; equals 1 if vote cast by Board
member, 0 if vote cast by Bank president
Volcker Dummy variable; equals 1 if FOMC chairman is
Volcker, 0 otherwise; reference category is
the chairmenship of Arthur Miller
Greenspan Dummy variable; equals 1 if FOMC chairman is
Greenspan, 0 otherwise; reference category is
the chairmenship of Arthur Miller
Bernanke Dummy variable; equals 1 if FOMC chairman is
Bernanke, 0 otherwise; reference category is
the chairmenship of Arthur Miller
Variable Data Sources
Dissent FOMC voting minutes
Vote FOMC voting minutes
Regional house House price index for U.S. states:
price gap Federal Housing Finance Agency
Resident population: Census Bureau
Regional National and state unemployment rate:
unemployment rate Bureau of Labor Statistics
Resident population: Census Bureau
Failed assets Failed assets: Federal Deposit Insurance Company
of regional banks
Resident population: Census Bureau
Consumer price index: Bureau of Labor Statistics
Regional Federal Reserve Bank of Philadelphia
coincident index
National house House price index for the United States:
price gap Federal Housing
Finance Agency
National National unemployment rate: Bureau of
unemployment rate Labor Statistics
National Consumer price index: Bureau of Labor Statist:
inflation rate
National industrial Industrial production: Board of Governors
production gap
Commodity S&P GSCI, drawn from Datastream
price index
Exchange rate index Federal Reserve, drawn from Datastream
Inflation forecast Inflation forecast: Federal Reserve Bank of
Philadelphia
Unemployment Unemployment rate forecast: Federal Reserve
rate forecast Bank of Philadelphia
Industrial Industrial production forecast: Federal Reserve
production forecast Bank of Philadelphia
Previous funds rate Federal funds rate: Board of Governors
Tape FOMC voting minutes (November 16, 1993)
Meeting
Board member
Volcker
Greenspan
Bernanke
(a,b) Regional and national variables are lagged 1 month.
TABLE A2
Descriptive Statistics of Selected Determinants
(Full Sample)
Variable M SD
Regional house price gap -0.050 2.744
Regional unemployment rate -0.157 0.973
Regional failed assets 8.080 52.769
Regional coincident index -0.002 0.182
National house price gap 0.207 1.990
National unemployment rate 0.109 2.831
National industrial production gap 0.037 1.436
National inflation 0.373 0.364
Previous funds rate 6.580 4.127
Commodity price index 0.265 5.252
Exchange rate index 0.269 1.307
Inflation forecast 3.924 2.263
Unemployment rate forecast 6.361 1.426
Industrial production forecast 3.102 2.797
Variable Min Max
Regional house price gap -9.926 12.527
Regional unemployment rate -3.182 3.140
Regional failed assets 0 1,492.325
Regional coincident index -1.265 1.039
National house price gap -4.162 5.552
National unemployment rate -8.511 10.204
National industrial production gap -7.025 4.468
National inflation -1.803 1.430
Previous funds rate 0.110 18.840
Commodity price index -13.086 21.103
Exchange rate index -3.442 3.443
Inflation forecast 1.236 9.461
Unemployment rate forecast 4 10.1
Industrial production forecast -6.502 8.983
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(1.) While Gildea (1992), Meade and Sheets (2005), and Tootell
(1991) use categorical data of dissenting votes, Chappell, McGregor, and
Vermilyea (2008) use the continuous desired federal funds rate of each
FOMC member (as expressed in transcripts in the period 1987-1999) in
order to study the relevance of regional economic factors.
(2.) There are several studies that empirically examine the
influence of central banks on house prices (see, e.g., Bjornland and
Jacobsen 2008, 2010; Del Negro and Otrok 2007; Dokko et al. 2011;
Iacoviello and Neri 2010; Jarocihski and Smets 2008). Most of these
studies find that some variation in house prices can be attributed to
monetary policy changes.
(3.) In a theoretical model, Allen and Carletti (2010) show that
central banks' interest rate setting should respond to real-estate
prices in small countries, where real-estate prices move relatively
homogeneously, whereas such a policy may not be optimal in large
countries with regional differences in house price developments.
(4.) While the Federal Reserve Bank of New York has a permanent
voting right in the FOMC, the voting rights of the remaining 11
districts rotate in an annual manner.
(5.) "Institutional practice does not closely link Governors
to the regions with which they are formally affiliated. Indeed,
Governors' formal district affiliations often seem to be determined
as a matter of convenience in meeting the legal requirement for regional
diversity" (Chappell, McGregor, and Vermilyea 2008, p. 285).
(6.) The members of the Board of Governors are appointed by the
President of the United States and confirmed by the Senate. The Board of
Directors of the regional Federal Reserve Banks selects its Bank
president.
(7.) The Bank presidents have, of course, frequent contacts to
businessmen living and working in their particular districts they
represent in the FOMC. These business people provide information
concerning economic conditions that should be considered by Bank
presidents in the FOMC meetings.
(8.) Data on dissenting votes from 1978M3 to 2000M12 have been
taken from the dataset introduced by Meade and Sheets (2005) and was
extended until 2010M9 using the minutes of the Federal Board of
Governors.
(9.) Excluding chairmen, the share of dissenting votes of Board
members equals 6.88%.
(10.) It measures the change in average prices paid in repeat sales
or refinancings on the same single-family properties, whose mortgages
have been purchased or securitized by Fannie Mae or Freddie Mac since
1975.
(11.) District boundaries are taken from Chappell, McGregor, and
Vermilyea (2008).
(12.) The finding for Chicago may be explained by the fact that
100% of dissenting votes cast by these representatives were in favor of
monetary easing. Therefore, one may argue that this tradition of
preferring monetary easing may be explained by factors other than house
prices.
(13.) The regional inflation rate is not included as a control
variable since no appropriate data exist for this variable on the
district level. Data on inflation rates for several metropolitan areas
are available. However, these metro areas are not always representative
for the Federal Reserve district used.
(14.) One exception is the federal funds rate where we use the
value of Wednesdays prior to the meeting.
(15.) Meade and Stasavage (2008) and Meade (2010) show that voting
behavior changed after publishing voting records in 1993.
(16.) This estimator was proposed by Frechette (2001). We used
fixed effects models as a sensitivity check, but the results remained
relatively robust.
(17.) The full dataset includes 3,264 interest rate votes. Due to
absence or illness of participants, this number of observations is
somewhat smaller than the number implied by the voting scheme of the
FOMC, which implies 3,540 observations, i.e., 12 votes for each of the
295 considered meetings.
(18.) The marginal effects for the institutional variables are not
reported in order to save space (but are available from the authors upon
request).
(19.) This average standardized impact is calculated by multiplying
the average marginal effect in the tightening category (being 0.267) by
the standard deviation of the regional house price gap (being 2.744).
The other average standardized marginal impacts discussed in the
following, are calculated in the same way.
(20.) Bank presidents also benefit from regional information
provided by members of their regional Federal Reserve Banks' Board
of Directors, which--by construction--consists of different branches,
particularly banking, agriculture, industry, trade, and public interest.
(21.) We thank an anonymous referee for this helpful suggestion.
(22.) For Bank presidents, a 1 standard deviation increase in the
regional house price gap (being 2.744) raises the probability of votes
in favor of higher interest rates, on average, by around 3.3% and lowers
the probability of votes in favor of lower and unchanged interest rates
by around 2.5% and 0.7%, on average. For Board members, a 1 standard
deviation increase in the regional house price gap increases the
probability of votes in favor of higher interest rates, on average, by
around 2.2% and lowers the probability of votes in favor of lower and
unchanged interest rates by around 1.7% and 0.4%, on average. However, a
r-test revealed that in one out of four specifications, the difference
between the regional house price gap coefficients of the Bank presidents
sample and the Board members sample was statistically significant.
(23.) This multicollinearity problem may not be an issue in the
dissents model where national variables played no significant role for
dissents. The multicollinearity issue may therefore explain the
different results for the regional unemployment rate in the dissents
model (where a highly significant effect is found) and the voting model
(where no significant effect is found).
STEFAN EICHLER and TOM LAHNER *
* We would like to thank participants at the 2013 International
Conference on Macroeconomic Analysis and International Finance and the
Brown Bag seminar at Technische Universitaet Dresden, two anonymous
referees, and the editor for valuable comments.
Eichler: Faculty of Business and Economics, Technische Universitaet
Dresden, Dresden 01062, Germany. Phone 49-351-463-35902, E-mail
[email protected]
Lahner: Faculty of Business and Economics, Technische Universitaet
Dresden, Dresden 01062, Germany. Phone 49-351-463-35904, E-mail
[email protected]
TABLE 1
Regional Dispersion of Interest Rate Votes in the FOMC
District Total Boston New York
Total votes 3,264 345 564
Average vote per meeting 11.07 1.17 1.91
Board 6.07 0.83 0.92
Bank 5.00 0.34 1.00
Dissents per casted votes 6.86 9.86 1.95
Board (%) 5.75 12.60 1.85
Bank (%) 8.22 3.03 2.04
Favored direction
of total dissents
Tightening (%) 69.20 94.12 45.45
Easing (%) 30.80 5.88 54.55
Dissents in favor
of tightening
Board (%) 32.26 91.18 0.00
Bank (%) 67.74 2.94 45.45
Dissents in favor of easing
Board (%) 76.81 0.00 45.45
Bank (%) 23.19 5.88 9.09
Ranking
Area size 10 11
Population 10 6
Population density 4 1
Assets 5 1
Real GDP 8 2
Votes per meeting 4 1
District Philadelphia Cleveland Richmond
Total votes 244 152 354
Average vote per meeting 0.83 0.52 1.20
Board 0.49 0.00 0.87
Bank 0.34 0.52 0.33
Dissents per casted votes 1.23 13.82 9.04
Board (%) 0.00 0.00 4.28
Bank (%) 3.03 13.82 21.65
Favored direction
of total dissents
Tightening (%) 66.67 85.71 71.88
Easing (%) 33.33 14.29 28.13
Dissents in favor
of tightening
Board (%) 0.00 0.00 6.25
Bank (%) 66.67 85.71 65.63
Dissents in favor of easing
Board (%) 0.00 0.00 28.13
Bank (%) 33.33 14.29 0.00
Ranking
Area size 12 9 8
Population 11 8 4
Population density 2 3 5
Assets 10 6 4
Real GDP 10 7 5
Votes per meeting 7 11 3
District Atlanta Chicago St. Louis
Total votes 201 357 179
Average vote per meeting 0.68 1.21 0.61
Board 0.36 0.73 0.27
Bank 0.33 0.48 0.34
Dissents per casted votes 3.48 8.68 8.94
Board (%) 0.00 14.35 0.00
Bank (%) 7.29 0.00 16.00
Favored direction
of total dissents
Tightening (%) 100.00 0.00 81.25
Easing (%) 0.00 100.00 18.75
Dissents in favor
of tightening
Board (%) 0.00 0.00 0.00
Bank (%) 100.00 0.00 81.25
Dissents in favor of easing
Board (%) 0.00 100.00 0.00
Bank (%) 0.00 0.00 18.75
Ranking
Area size 5 6 7
Population 2 3 9
Population density 6 7 8
Assets 7 2 11
Real GDP 4 3 11
Votes per meeting 8 2 10
Kansas
District Minneapolis City
Total votes 137 282
Average vote per meeting 0.46 0.96
Board 0.13 0.62
Bank 0.34 0.34
Dissents per casted votes 8.03 9.22
Board (%) 2.70 6.04
Bank (%) 10.00 15.00
Favored direction
of total dissents
Tightening (%) 90.91 92.31
Easing (%) 9.09 7.69
Dissents in favor
of tightening
Board (%) 0.00 38.46
Bank (%) 90.91 53.85
Dissents in favor of easing
Board (%) 9.09 3.85
Bank (%) 0.00 3.85
Ranking
Area size 3 2
Population 12 7
Population density 12 11
Assets 12 9
Real GDP 12 9
Votes per meeting 12 5
San
District Dallas Francisco
Total votes 251 198
Average vote per meeting 0.85 0.67
Board 0.52 0.35
Bank 0.34 0.32
Dissents per casted votes 8.76 5.05
Board (%) 5.26 4.85
Bank (%) 14.14 5.26
Favored direction
of total dissents
Tightening (%) 81.82 30.00
Easing (%) 18.18 70.00
Dissents in favor
of tightening
Board (%) 31.82 0.00
Bank (%) 50.00 30.00
Dissents in favor of easing
Board (%) 4.55 50.00
Bank (%) 13.64 20.00
Ranking
Area size 4 1
Population 5 1
Population density 9 10
Assets 8 3
Real GDP 6 1
Votes per meeting 6 9
Source: Own calculations. Rankings based on assets and real
GDP are taken from Meade and Sheets (2002).
TABLE 2 Descriptive Analysis of the Regional House Price Gap
Boston New York Philadelphia Cleveland
1978-1989
M 0.352 0.408 -0.404 0.699
SD 5.048 4.679 3.158 2.169
1990-1999
M -1.362 -0.519 0.057 0.165
SD 1.362 1.310 0.959 0.492
2000-2010
M -0.207 -0.236 -0.352 -0.146
SD 2.487 3.205 2.233 1.161
Richmond Atlanta Chicago St. Louis
1978-1989
M 0.455 0.434 0.981 1.241
SD 2.034 1.447 2.320 1.566
1990-1999
M 0.136 -0.117 -0.114 -0.144
SD 0.642 0.602 0.527 0.611
2000-2010
M -0.243 -0.594 0.175 0.100
SD 3.369 4.743 1.144 1.211
San
Minneapolis Kansas City Dallas Francisco
1978-1989
M 0.315 0.421 -1.717 -0.929
SD 1.476 1.321 2.236 2.725
1990-1999
M -0.336 -0.551 -0.135 -0.154
SD 0.929 0.846 1.123 1.751
2000-2010
M 0.167 0.221 0.823 -1.442
SD 1.828 1.314 1.162 8.531
TABLE 3 Descriptive Analysis of FOMC Dissents and Regional
House Price Gaps
Boston New York Philadelphia
Dissenting
Period vote p n p n p n
1978-1989 +1 19 7 3 2 0 0
-1 0 1 3 3 0 0
1990-1999 +1 0 6 0 0 0 0
-1 0 0 0 0 1 0
2000-2010 +1 0 0 0 0 2 0
-1 1 0 0 0 0 0
Cleveland Richmond Atlanta
Dissenting
Period vote p n p n p n
1978-1989 +1 8 0 3 8 7 0
-1 0 1 4 2 0 0
1990-1999 +1 10 0 4 4 0 0
-1 2 0 2 0 0 0
2000-2010 +1 0 0 4 0 0 0
-1 0 0 0 1 0 0
Chicago St. Louis Minneapolis
Dissenting
Period vote p n p n p n
1978-1989 +1 0 0 6 0 5 2
-1 19 10 3 0 0 0
1990-1999 +1 0 0 1 4 3 0
-1 2 0 0 0 0 0
2000-2010 +1 0 0 2 0 0 0
-1 0 0 0 0 1 0
Kansas San
City Dallas Francisco
Dissenting
Period vote p n p n p n
1978-1989 +1 3 5 4 0 2 1
-1 1 1 5 1 1 5
1990-1999 +1 1 6 1 3 0 0
-1 0 0 0 2 0 0
2000-2010 +1 3 3 5 0 0 0
-1 0 0 1 0 0 1
Note: "p" indicates a positive value of the regional house price
gap when casting tighter (+1) or easier (-1) dissents; "n" indicates
a negative value of the regional house price gap when casting
tighter (+1) or easier (-1) dissents.
TABLE 4 Random Effects Ordered Probit Estimates of the
Dissents Model
Full Sample
I II
Regional house price gap 0.033 ** 0.039 ***
(1.99) (2.91)
Regional unemployment rate -0.224 *** -0.205 ***
(-5.87) (-4.84)
Failed assets of 0.000
regional banks (0.26)
Regional coincident index 0.460 **
(2.28)
National inflation rate 0.024 0.019
(0.24) (0.18)
National house price gap 0.019
(0.79)
National unemployment rate 0.028
(0.61)
National industrial 0.031 -0.022
production gap (1.33) (-0.45)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production
forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bernanke
Threshold 1 -2.064 *** -2.269 ***
(-12.66) (-22.13)
Threshold 2 1.982 *** 1.829 ***
(12.49) (20.92)
[rho] 0.108 *** 0.095 ***
(3.78) (2.95)
[chi square] 47.18 *** 39.57 ***
Number of obs. 3,264 2,978
Full Sample
III IV
Regional house price gap 0.038 *** 0.038 ***
(2.63) (2.78)
Regional unemployment rate -0.230 *** -0.223 ***
(-5.87) (-5.42)
Failed assets of 0.000 0.000
regional banks (0.35) (0.44)
Regional coincident index
National inflation rate 0.028
(0.21)
National house price gap
National unemployment rate
National industrial 0.035
production gap (0.74)
Commodity price index -0.003
(-0.52)
Exchange rate index -0.020
(-0.75)
Inflation forecast 0.038
(0.77)
Unemployment rate forecast 0.018
(0.45)
Industrial production 0.003
forecast (0.24)
Tape 0.01 -0.052
(0.09) (-0.50)
Meeting 0.355 *** 0.340 ***
(2.67) (2.59)
Board member -0.482 *** -0.485 ***
(-6.63) (6.72)
Previous funds rate -0.017 -0.007
(-0.72) (-0.45)
Volcker -0.287 * -0.266 *
(-1.66) (-1.83)
Greenspan -0.316 * -0.366 **
(-1.78) (-2.41)
Bernanke -0.275 -0.292
(-1.20) (-1.49)
Threshold 1 -2.359 *** -2.555 ***
(-6.95) (-11.37)
Threshold 2 1.791 *** 1.598 ***
(5.35) (7.34)
[rho] 0.208 *** 0.076 ***
(4.81) (3.63)
[chi square] 102.75 *** 104.51 ***
Number of obs. 3,264 3,264
Bank Presidents Sample
V VI
Regional house price gap 0.044 * 0.070 ***
(1.82) (3.26)
Regional unemployment rate -0.158 *** -0.109 *
(-2.92) (-1.88)
Failed assets of 0.001
regional banks (0.42)
Regional coincident index 1.306 ***
(4.00)
National inflation rate 0.038 0.041
(0.27) (0.27)
National house price gap 0.025
(0.71)
National unemployment rate 0.034
(1.06)
National industrial 0.034 -0.070
production gap (0.52) (-0.97)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production
forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bernanke
Threshold 1 -2.187 *** -2.507 ***
(-9.52) (-19.04)
Threshold 2 1.856 *** 1.630 ***
(8.50) (18.22)
[rho] 0.220 *** 0.241 ***
(3.24) (3.71)
[chi square] 20.25 *** 34.09 ***
Number of obs. 1,472 1,337
Bank Presidents Sample
VII VIII
Regional house price gap 0.047 ** 0.046 **
(2.27) (2.36)
Regional unemployment rate -0.165 *** -0.167 ***
(-2.76) (-2.89)
Failed assets of 0.000 0.000
regional banks (0.19) (0.09)
Regional coincident index
National inflation rate -0.105
(-0.57)
National house price gap
National unemployment rate
National industrial 0.043
production gap (0.64)
Commodity price index 0.001
(0.07)
Exchange rate index -0.071*
(-1.85)
Inflation forecast 0.039
(0.54)
Unemployment rate forecast -0.038
(-0.67)
Industrial production 0.032 *
forecast (1.69)
Tape -0.142 -0.136
(-0.87) (-0.91)
Meeting 0.411 ** 0.421 **
(2.15) (2.22)
Board member
Previous funds rate -0.008 0.018
(-0.22) (0.75)
Volcker -0.235 -0.301
T o VO (-1.46)
Greenspan -0.307 -0.338
(-1.22) (-1.58)
Bernanke 0.057 -0.033
(0.18) (-0.12)
Threshold 1 -2.400 *** -2.366 ***
(-4.92) (-7.59)
Threshold 2 1.685 *** 1.759 ***
(3.50) (5.82)
[rho] 0.211 *** 0.212 ***
(2.84) (2.72)
[chi square] 33.58 *** 34.46 ***
Number of obs. 1,472 1,472
Board Members Sample
IX X
Regional house price gap 0.036 0.017
(1.43) (0.83)
Regional unemployment rate -0.405 *** -0.419 ***
(-7.85) (-7.45)
Failed assets of 0.000
regional banks (0.07)
Regional coincident index -0.259
(-0.84)
National inflation rate -0.037 0.023
(-0.24) (0.14)
National house price gap 0.012
(0.31)
National unemployment rate 0.040
(1.09)
National industrial 0.059 0.055
production gap (0.81) (0.69)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production
forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bernanke
Threshold 1 -1.683 *** -2.729 ***
(-6.87) (-16.27)
Threshold 2 3.074 *** 2.065 ***
(10.90) (17.56)
[rho] 0.455 *** 0.425 ***
(7.86) (6.41)
[chi square] 34.97 *** 28.52 ***
Number of obs. 1.792 1,641
Board Members Sample
XI XII
Regional house price gap 0.030 0.037 *
(1.35) (1.78)
Regional unemployment rate -0.401 *** -0.402 ***
(-7.72) (-7.76)
Failed assets of 0.000 0.000
regional banks (0.23) (0.28)
Regional coincident index
National inflation rate 0.217
(1.09)
National house price gap
National unemployment rate
National industrial 0.052
production gap (0.73)
Commodity price index -0.011
(-1.11)
Exchange rate index 0.037
(0.95)
Inflation forecast 0.067
(0.89)
Unemployment rate forecast 0.061
(1.00)
Industrial production -0.015
forecast (-0.79)
Tape 0.244 0.118
(1.43) (0.75)
Meeting 0.232 0.202
(1.17) (1.04)
Board member
Previous funds rate -0.034 -0.036
(-0.98) (-1.47)
Volcker -0.286 -0.229
(-1.11) (-1.06)
Greenspan -0.191 -0.329
(-0.72) (-1.44)
Bernanke -0.689 * -0.770 **
(-1.88) (-2.49)
Threshold 1 -1.867 *** -2.553 ***
(-3.72) (-7.77)
Threshold 2 2.934 *** 2.256 ***
(5.70) (6.99)
[rho] 0.650 *** 0.657 ***
(9.07) (9.43)
[chi square] 22.42 ** 22.22 *
Number of obs. 1,792 1,792
Notes: The dataset includes 295 meetings from 1978M3 through 2010M9.
Dependent variable: dissent, r-values in parentheses.
*, **, *** indicate significance at the 10%, 5%, and 1% levels,
respectively.
TABLE 5
Marginal Effects for the Random Effects Ordered Probit
Estimates of the Dissents Model
Variable Category Full Sample
I II III IV
Regional house -1 -0.09 -0.11 -0.1 -0.11
price gap 0 -0.17 -0.18 -0.18 -0.13
+1 0.26 0.29 0.28 0.24
Regional -1 0.61 0.55 0.57 0.65
unemployment 0 1.17 0.96 1.09 0.75
rate +1 -1.78 -1.51 -1.66 -1.40
Failed assets of -1 0 0 0
regional hanks 0 0 0 0
+1 0 0 0
Regional -1 -1.25
coincident 0 -2.14
index +1 3.39
National -1 -0.07 -0.05 -0.08
inflation rate 0 -0.12 -0.09 -0.09
+1 0.19 0.14 0.17
National house -1 -0.05
price gap 0 -0.10
+1 0.15
National -1 -0.08
unemployment 0 -0.16
rate
+1 0.24
National -1 -0.07 0.06 -0.1
industrial 0 -0.15 0.10 -0.12
Production gap +1 0.22 -0.16 0.22
Commodity price -1 0.01
index 0 0.01
+1 -0.02
Exchange rate -1 0.06
index 0 0.07
+1 -0.13
Inflation -1 -0.09
forecast 0 -0.18
+1 0.27
Unemployment -1 -0.05
rate forecast 0 -0.08
+1 0.13
Industrial -1 -0.01
production 0 -0.01
forecast +1 0.02
Variable Category Bank Presidents Sample
V VI VII VIII
Regional house -1 -0.09 -0.1 -0.09 -0.08
price gap 0 -0.34 -0.51 -0.37 -0.36
+1 0.43 0.61 0.46 0.44
Regional -1 0.32 0.18 0.30 0.30
unemployment 0 1.24 0.89 1.30 1.30
rate +1 -1.56 -1.07 -1.60 -1.6
Failed assets of -1 0 0 0
regional hanks 0 -0.01 0 0
+1 0.01 0 0
Regional -1 -2.09
coincident 0 -10.64
index +1 12.73
National -1 -0.07 -0.07 0.19
inflation rate 0 -0.3 -0.33 0.82
+1 0.37 0.40 -1.01
National house -1 -0.05
price gap 0 -0.2
+1 0.25
National -1 -0.07
unemployment 0 -0.27
rate
+1 0.34
National -1 -0.07 0.11 -0.08
industrial 0 -0.26 0.57 -0.34
Production gap +1 0.33 -0.68 0.42
Commodity price -1 0
index 0 -0.01
+1 0.01
Exchange rate -1 0.13
index 0 0.55
+1 -0.68
Inflation -1 -0.07
forecast 0 -0.31
+1 0.38
Unemployment -1 0.07
rate forecast 0 0.30
+1 -0.37
Industrial -1 -0.06
production 0 -0.25
forecast +1 0.31
Variable Category Board Members Sample
IX X XI XII
Regional house -1 -0.18 -0.01 -0.07 -0.08
price gap 0 0.15 -0.08 0 0
+1 0.03 0.09 0.07 0.08
Regional -1 2.07 0.31 0.88 0.87
unemployment 0 -1.71 1.96 -0.01 0
rate +1 -0.36 -2.27 -0.87 -0.87
Failed assets of -1 0 0 0
regional hanks 0 0 0 0
+1 0 0 0
Regional -1 0.19
coincident 0 1.21
index +1 -1.4
National -1 0.19 -0.02 -0.47
inflation rate 0 -0.16 -0.11 0
+1 -0.03 0.13 0.47
National house -1 -0.06
price gap 0 0.05
+1 0.01
National -1 -0.20
unemployment 0 0.17
rate
+1 0.03
National -1 -0.3 -0.04 -0.11
industrial 0 0.25 -0.26 0
Production gap +1 0.05 0.30 0.11
Commodity price -1 0.02
index 0 0
+1 -0.02
Exchange rate -1 -0.08
index 0 0
+1 0.08
Inflation -1 -0.15
forecast 0 0
+1 0.15
Unemployment -1 -0.13
rate forecast 0 0
+1 0.13
Industrial -1 0.03
production 0 0
forecast +1 -0.03
TABLE 6
Random Effects Ordered Probit Estimates of the Voting Model
Full Sample
I II
Regional house price gap 0.020 * 0.049 ***
(1.93) (5.98)
Regional unemployment rate -0.039 -0.006
(-1.60) (-0.23)
Failed assets of regional banks -0.000
(-1.17)
Regional coincident index 0.477 ***
(3.69)
National inflation rate 0.465 *** 0.200 ***
(7.30) (3.01)
National house price gap 0.021
(1.38)
National unemployment rate -0.020
(-1.40)
National industrial production gap 0.430 *** 0.425 ***
(13.92) (12.85)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bemanke
Threshold 1 -1.019 *** -0.949 ***
(-10.20) (-21.48)
Threshold 2 0.839 *** 0.951 ***
(8.42) (21.69)
[rho] 0.015 0.007
(1.56) (1.15)
LR 363.19 *** 295.90 ***
Number of obs. 3,264 2,978
Full Sample
III IV
Regional house price gap 0.061 *** 0.064 ***
(6.79) (7.49)
Regional unemployment rate -0.025 -0.036 *
(-1.16) (-1.67)
Failed assets of regional banks -0.001 -0.000
(-1.28) (-0.48)
Regional coincident index
National inflation rate 0.484 ***
(5.78)
National house price gap
National unemployment rate
National industrial production gap 0.421 ***
(12.93)
Commodity price index 0.020 ***
(4.80)
Exchange rate index -0.002
(-0.10)
Inflation forecast 0.335 ***
(10.29)
Unemployment rate forecast -0.014
(-0.57)
Industrial production forecast 0.059 ***
(6.93)
Tape 0.416 *** 0.177 ***
(5.98) (2.79)
Meeting 0.427 *** 0.383 ***
(5.17) (4.59)
Board member -0.083 ** -0.083 **
(-2.03) (-2.01)
Previous funds rate -0.126 *** -0.048 ***
(-8.41) (-4.72)
Volcker -0.632 *** -0.713 ***
(-5.45) (-7.27)
Greenspan -0.561 *** -1.025 ***
(-4.73) (-9.98)
Bemanke -1.133 *** -1.625 ***
(-7.59) (-12.61)
Threshold 1 -0.109 -2.232
(-0.00) (-0.02)
Threshold 2 1.769 -0.300
(0.01) (-0.00)
[rho] 0.521 0.514
(0.00) (0.00)
LR 413.14 *** 554.96 ***
Number of obs. 3,264 3,264
Bank Presidents Sample
V VI
Regional house price gap 0.025 * 0.057 ***
(1.65) (4.69)
Regional unemployment rate -0.023 0.007
(-0.58) (0.15)
Failed assets of regional banks -0.001
(-1.10)
Regional coincident index 0.641 ***
(3.29)
National inflation rate 0.451 *** 0.233 **
(4.82) (2.37)
National house price gap 0.024
(1.06)
National unemployment rate -0.007
(-0.31)
National industrial production gap 0.434 *** 0.412 ***
(9.47) (8.41)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bemanke
Threshold 1 -1.014 *** -1.017 ***
(-6.81) (-14.13)
Threshold 2 0.849 *** 0.892 ***
(5.73) (12.60)
[rho] 0.059 ** 0.070
(2.46) (1.58)
LR 169.13 *** 147.39 ***
Number of obs. 1,472 1,337
Bank Presidents Sample
VII VIII
Regional house price gap 0.075 *** 0.069 ***
(5.59) (5.47)
Regional unemployment rate 0.186 -0.009
(0.49) (-0.24)
Failed assets of regional banks -0.001 -0.001
(-0.84) (-0.66)
Regional coincident index
National inflation rate 0.427 ***
(3.31)
National house price gap
National unemployment rate
National industrial production gap 0.427 ***
(8.87)
Commodity price index 0.020 ***
(3.25)
Exchange rate index -0.020
(-0.81)
Inflation forecast 0.283 ***
(5.72)
Unemployment rate forecast 0.004
(0.10)
Industrial production forecast 0.068 ***
(5.23)
Tape 0.406 *** 0.171 *
(3.84) (1.77)
Meeting 0.396 *** 0.362 ***
(3.25) (2.94)
Board member
Previous funds rate -0.102 *** -0.041 ***
(-4.51) (-2.65)
Volcker -0.665 *** -0.619 ***
(-3.86) (-4.23)
Greenspan -0.613 *** -0.943 ***
(-3.50) (-6.15)
Bemanke -1.153 *** -1.481 ***
(-5.25) (-7.73)
Threshold 1 -0.585 * -1.735 ***
(-1.85) (-8.52)
Threshold 2 1.284 *** 0.197
(4.05) (0.99)
[rho] 0.045 ** 0.052 **
(2.08) (2.19)
LR 185.21 *** 257.11 ***
Number of obs. 1,472 1,472
Board Members Sample
IX X
Regional house price gap 0.012 0.043 ***
(0.84) (3.79)
Regional unemployment rate -0.077 ** -0.045
(-2.43) (-1.48)
Failed assets of regional banks -0.000
(-0.68)
Regional coincident index 0.365 **
(2.11)
National inflation rate 0.488 *** 0.218 **
(5.58) (2.39)
National house price gap 0.026
(1.27)
National unemployment rate -0.036 *
(-1.81)
National industrial production gap 0.441 *** 0.445 ***
(10.43) (9.84)
Commodity price index
Exchange rate index
Inflation forecast
Unemployment rate forecast
Industrial production forecast
Tape
Meeting
Board member
Previous funds rate
Volcker
Greenspan
Bemanke
Threshold 1 -1.071 *** -1.178 ***
(-8.10) (-7.66)
Threshold 2 0.820 *** 0.762 ***
(6.24) (5.03)
[rho] 0.006 0.294
(0.87) (1.30)
LR 206.42 *** 146.64 ***
Number of obs. 1,792 1,641
Board Members Sample
XI XII
Regional house price gap 0.032 *** 0.067 ***
(2.65) (5.57)
Regional unemployment rate -0.057 * -0.057 *
(-1.79) (-1.68)
Failed assets of regional banks -0.001 ** -0.000
(-1.99) (-0.47)
Regional coincident index
National inflation rate 0.531 ***
(4.61)
National house price gap
National unemployment rate
National industrial production gap 0.434 ***
(9.71)
Commodity price index 0.019 ***
(3.44)
Exchange rate index 0.013
(0.59)
Inflation forecast 0.508 ***
(13.27)
Unemployment rate forecast -0.106 ***
(-3.37)
Industrial production forecast 0.072 ***
(6.42)
Tape 0.280 *** 0.187 **
(3.10) (2.16)
Meeting 0.348 *** 0.352 ***
(3.08) (3.06)
Board member
Previous funds rate -0.177 *** -0.056 ***
(-9.16) (-3.97)
Volcker -0.015 -0.737 ***
(-0.12 (-5.43)
Greenspan 0.262 *** -1.045 ***
(3.05) (-7.36)
Bemanke -1.769 ***
(-9.78)
Threshold 1 -0.107 -1.808 ***
(-.39) (-9.54)
Threshold 2 1.808 *** 0.179
(6.54) (0.97)
[rho] 0.006 0.010
(0.93) (1.13)
LR 238.38 *** 341.84 ***
Number of obs. 1,792 1,792
Notes: The dataset includes 295 meetings from 1978M3 through
2010M9. Dependent variable: vote, r-values in parentheses.
*, **, *** indicate significance at the 10%, 5%, and 1%
levels, respectively.
TABLE 7 Marginal Effects for the Random Effects Ordered
Probit Estimates of the Voting Model
Variable Category Full Sample
I II III IV
Regional house -1 -0.35 -0.89 -1.35 -0.66
price gap 0 -0.07 -0.07 0.44 -0.84
+1 0.42 0.96 0.91 1.50
Regional -1 0.67 0.11 0.54 0.36
unemployment rate 0 0.15 0.01 -0.17 0.47
+1 -0.82 -0.12 -0.37 -0.83
Failed assets of -1 0.01 0.01 0.00
regional banks 0 0.00 -0.00 0.00
+1 -0.01 -0.01 -0.00
Regional coincident -1 -8.66
index 0 -0.73
+1 9.39
National inflation -1 -7.98 -3.63 -4.95
rate 0 -1.72 -0.31 -6.38
+1 9.70 3.94 11.33
National house -1 -0.36
price gap 0 -0.08
+1 0.44
National -1 0.35
unemployment rate 0 0.07
+1 -0.42
National industrial -1 -7.39 -7.71 -4.30
production gap 0 -1.60 -0.65 -5.55
+1 8.99 8.36 9.85
Commodity price -1 -0.20
index 0 -0.26
+1 0.46
Exchange rate index -1 0.02
0 0.02
+1 -0.04
Inflation forecast -1 -7.39
0 2.39
+1 5.00
Unemployment rate -1 0.31
forecast 0 -0.10
+1 -0.21
Industrial -1 -1.29
production forecast 0 0.42
+1 0.87
Variable Category Bank Presidents Sample
V VI VII VIII
Regional house -1 -0.41 -0.97 -1.21 -1.08
price gap 0 -0.14 -0.18 -0.38 -0.38
+1 0.55 1.15 1.59 1.46
Regional -1 0.37 -0.11 -0.30 0.14
unemployment rate 0 0.12 -0.02 -0.10 0.05
+1 -0.49 0.13 0.40 -0.19
Failed assets of -1 0.02 0.02 0.01
regional banks 0 0.01 0.00 0.01
+1 -0.03 -0.00 -0.02
Regional coincident -1 -11.03
index 0 -2.02
+1 13.05
National inflation -1 -7.29 -4.01 -6.39
rate 0 -2.40 -0.73 -2.29
+1 9.69 4.74 8.68
National house -1 -0.39
price gap 0 -0.13
+1 9.52
National -1 0.11
unemployment rate 0 0.03
+1 -0.14
National industrial -1 -7.00 -7.10 -6.67
production gap 0 -2.31 -1.30 -2.39
+1 9.31 8.40 9.06
Commodity price -1 -0.31
index 0 -0.11
+1 0.42
Exchange rate index -1 0.32
0 0.11
+1 -0.43
Inflation forecast -1 -4.59
0 -1.46
+1 6.05
Unemployment rate -1 -0.06
forecast 0 -0.02
+1 0.08
Industrial -1 -1.10
production forecast 0 -0.35
+1 1.45
Variable Category Board Members Sample
IX X XI XII
Regional house -1 -0.21 -0.63 -0.55 -1.14
price gap 0 -0.03 -0.29 -0.09 -0.20
+1 0.24 0.93 0.64 1.34
Regional -1 1.34 0.67 0.99 0.96
unemployment rate 0 0.21 0.31 0.16 0.17
+1 -1.55 -0.98 -1.15 -1.13
Failed assets of -1 0.01 0.02 0.00
regional banks 0 0.00 0.00 0.00
+1 -0.01 -0.02 -0.00
Regional coincident -1 -5.39
index 0 -2.49
+1 7.88
National inflation -1 -8.55 -3.22 -8.96
rate 0 -1.34 -1.49 -1.58
+1 9.89 4.71 10.54
National house -1 -0.46
price gap 0 -0.07
+1 0.53
National -1 0.63
unemployment rate 0 0.10
+1 -0.73
National industrial -1 -7.74 -6.58 -7.31
production gap 0 -1.22 -3.01 -1.29
+1 8.96 9.63 8.60
Commodity price -1 -0.32
index 0 -0.06
+1 0.38
Exchange rate index -1 -0.23
0 -0.04
+1 0.27
Inflation forecast -1 -8.83
0 -1.42
+1 10.25
Unemployment rate -1 1.84
forecast 0 0.30
+1 -2.14
Industrial -1 -1.25
production forecast 0 -0.20
+1 1.45