JOHN F. BOSCHEN
CHARLES L. WEISE
What Starts Inflation: Evidence from the
OECD Countries
We use a pooled cross-country time series framework to study the factors associated with the start of 73 inflation episodes in OECD countries
since 1960. We find that policy-makers’ pursuit of high real growth targets and
national elections were important factors in initiating inflation episodes.
U.S. inflation turns out to be a triggering event for simultaneous outbreaks of
inflation across countries. Several other explanations for inflation starts,
including increases in the natural rate of unemployment, oil price
shocks, changes in the political orientation of governments, and government
debt policy, are not supported by the data.
Repeated episodes of moderate inflation were a central
characteristic of many OECD economies during the 1960s, 1970s, and 1980s. This
inflation experience is documented in Figure 1, which plots trend inflation for 19
OECD countries since 1960.1 The figure shows that recurring bouts of inflation
were common in these countries and that these episodes follow a qualitatively similar
pattern. Inflation, initially low or in decline, changes direction and begins a sustained
rise. Eventually a disinflation occurs and inflation falls until the next inflation episode.
Research on the events and policies that triggered the inflation episodes in Figure
1 can give insight into what circumstances make inflation episodes likely to reoccur
in the future. The inflation literature has generated a wide variety of explanations
as to what starts inflation episodes. These explanations fall into several categories, including policy mistakes driven by a misunderstanding of the Phillips curve
(Taylor, 1992, 1997, De Long, 1997, Sargent, 1999); the interaction between an
exogenous rise in the natural rate of unemployment and time consistent monetary
policy (Parkin, 1993, Ireland, 1999); increases in oil and food prices (Blinder
The authors are grateful to Robert Hetzel, seminar participants at VCU, the Federal Reserve Bank
of St. Louis, the Federal Reserve Board, Gettysburg College, and two anonymous referees for valuable
comments and suggestions.
John F. Boschen is affiliated with the School of Business of The College of William and
Mary. E-mail: john.boschen얀business.wm.edu Charles L. Weise is affiliated with the
Department of Economics of Gettysburg College. E-mail: cweise얀gettysburg.edu
Journal of Money, Credit, and Banking, Vol. 35, No. 3 (June 2003)
Copyright 2003 by The Ohio State University
Fig. 1 Inflation episodes in OECD countries since 1960
JOHN F. BOSCHEN AND CHARLES L. WEISE : 325
1982); the political use of inflation near elections (Nordhaus, 1975, Lindbeck, 1976,
Rogoff and Sibert, 1988); the “softness” of left-leaning political parties with regard
to inflation (Hibbs, 1977, Alesina, 1988); excessive budget deficits and government
debt (Calvo, 1988, Friedman, 1994); and the international transmission of inflation
(Cassese and Lothian, 1982, Darby, 1983, Canzoneri and Gray, 1985, Turnovsky,
Basar, and d’Orey, 1988).
In this paper we present results from an empirical study of the events associated
with the start of the inflation episodes in 19 OECD countries between the early
1960s and the early 1990s. The empirical methodology, pooled probit analysis,
identifies predictors of turning points in inflation. The study is unique in that it
exploits a large cross-country data set to estimate formally the contribution of the
factors mentioned above in predicting inflation starts; previous research in this area
has been confined, for the most part, to case studies and empirical work focusing
on a single country.
Section 1 presents an overview of the data and the empirical model. Section 2
presents the main empirical findings. Section 3 reports on the sources of specific
inflation episodes and compares our model’s interpretation with historical studies
of inflation. Section 4 concludes.
1. DESCRIPTION OF INFLATION EPISODES AND EMPIRICAL MODELS
1.1 Defining Inflation Episodes and Inflation Starts
We begin by constructing a trend inflation series for each country to filter out
transitory quarter-to-quarter variation in inflation. Following Ball (1994), trend
inflation is defined as a nine-quarter moving average of the quarterly CPI inflation
rate.2 From the resulting trend inflation series it is possible to identify trough and
peak dates as dates at which trend inflation is lower (higher) than in the preceding
and succeeding four quarters. We define an inflation episode as a period of time
over which trend inflation rises by at least 2% from trough to peak and which is
preceded by four or more quarters of stable or declining trend inflation.3 The start
date for an inflation episode is the year following the year in which the trough
occurred. Altogether our procedure identifies 74 inflation episodes in the 19 OECD
countries in our sample over the period 1960:1–1995:4. One inflation episode,
Australia in 1994–5, was excluded from the analysis because data was lacking for
several of the explanatory variables. The inflation episodes are listed in Appendix A.
1.2 Stylized Facts about Inflation Starts
Table 1 provides summary descriptions of the 73 inflation episodes for various
subsamples. Two facts shown in Table 1 are noteworthy. First, the inflation episodes we
identify are indeed periods of sustained rising inflation. The mean length of an
episode in our sample is 15 quarters, with the vast majority of episodes lasting eight
quarters or more. Second, the inflation episodes of the 1960s, late 1970s, and mid1980s are remarkably similar in terms of length and the trough-to-peak increase in
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TABLE 1
Summary Statistics for Inflation Episodes
Episodes Ending
Number of episodes
Length (quarters)
Initial inflation rate
Ending inflation rate
Rise in inflation
Full Sample
1961–72
1972–77
1978–82
1983–93
73
15.1
3.8
9.3
5.5
19
14.1
2.0
6.3
4.2
20
21.4
3.7
13.0
9.3
18
11.4
6.8
11.2
4.5
16
13.2
2.9
6.6
3.7
Figures are mean values. Inflation measure is 400 times first difference of log of quarterly average CPI. Inflation data are from the IMF
IFS database on CD-ROM, July 1997.
inflation. In contrast, the episodes prevailing in the early and middle 1970’s (those
that ended between 1972 and 1977) were more severe, lasting longer and with
inflation rising almost twice as much from trough to peak.
One way that the inflation episodes of the late 1970s differ from those of the
1960s, the early 1970s, and the 1980s is that the level of inflation immediately prior
to the start of the inflation episode is higher. The higher initial values for the late
1970s episodes could lead one to interpret these episodes merely as the continuation
of inflation episodes that began in the early 1970s. A close look at Figure 1, however,
suggests that it is more reasonable to treat the episodes of the early and late 1970s
as separate events. As Blinder (1982) has pointed out in his analysis of the U.S.
inflation experience of the 1970s, there were two distinct periods of accelerating
inflation in this decade separated by a major recession. The graphs in Figure 1 show
that this pattern also existed in other countries. With the exception of Spain, all the
countries in the sample that experienced inflation episodes in both the early and
late 1970s also exhibited a sustained downward movement in trend inflation in the
years prior to the late 1970s’ start of inflation.
Figure 2 displays the total number of inflation starts per year for the 19 countries.
Figure 2 suggests that any overall explanation of inflation starts over the post-1960
period must square with three facts. First, there are more inflation starts during the
1960s and 1970s than later on. The second fact is that there are some years in which
many countries simultaneously experience an inflation start. The third fact, however,
is that most inflation starts are not grouped together in a single year or two but are
country-specific. Only in one year, 1979, does anything like a majority (13) of
countries experience an inflation start. The next highest number of starts per year
(seven) occurred in 1969. These facts suggest that an overall explanation for inflation
starts must involve both international events and domestic influences.
1.3 Overview of Empirical Models
We have identified seven broad categories of explanations for inflation starts:
policy mistakes due to a misunderstanding of the Phillips curve relationship;
JOHN F. BOSCHEN AND CHARLES L. WEISE : 327
Fig. 2 Number of inflation starts per year
interaction of a rising natural rate of unemployment with an optimizing central
bank; oil and commodity price shocks; political business cycle models; partisan
models; fiscal policy explanations; and international transmission of inflation.4 In the
section that follows we evaluate the relevance of each of these explanations in turn.
We use a probit model to estimate the conditional probability of an inflation start.
A complete description of the data is included in Appendix B. The regressions are
run with annual data. The time series dimension of the sample includes the years
leading up to and including the year in which an inflation episode started. The data
for the years in which an inflation episode is already ongoing are excluded from
the regressions. For each country, the dependent variable is a binary variable taking
on a value of 1 if an inflation start occurred in that country during that year and a
value of 0 otherwise. The data for each country are stacked and the probit model
estimated via maximum likelihood. In Tables 2 through 8 below, which display the
model estimates, we follow the standard procedure of reporting the marginal effect
of the independent variables evaluated at the means of all variables along with the
t-statistic associated with the coefficient estimate.
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TABLE 2
The Policy-Mistake Explanation for Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
UR(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 2.1a
⫺0.224
(12.08)
0.047
(4.55)
0.009
(0.58)
⫺183.4
0.06
353/73
Model 2.2b
⫺0.306
(6.83)
0.034
(4.27)
⫺0.010
(2.10)
⫺182.6
0.08
366/73
Pooled probit regression for periods of stable or declining inflation, 19 OECD countries, 1961–93. Dependent variable is dummy for start of
inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates in
parentheses. a. GDP and unemployment are detrended using an 11-year centered moving average. b. GDP and unemployment are not detrended.
2. COMPETING EXPLANATIONS FOR INFLATION STARTS
2.1 Policy Mistakes and the Phillips Curve Trade-Off
Several recently developed hypotheses about inflation starts are based on the idea
that inflation episodes were triggered by misguided attempts to exploit a shortrun Phillips curve trade-off. Taylor (1992) and Sargent (1999) stress the wide
dissemination, during the 1960s, of the belief in a long-run trade-off between inflation
and unemployment. This, coupled with an overly optimistic view of what constituted
full employment, encouraged policy-makers to pursue expansionary monetary policies that led to an acceleration of inflation. De Long (1997) has emphasized that in
the U.S., politicians of the 1960s and early 1970s vividly recalled the experience
of the Great Depression and placed a high value on keeping unemployment very
low. When inflation began to rise, the Federal Reserve did not have political support
for monetary tightening to eliminate the problem.
TABLE 3
The Time-consistency Explanation for Inflation Starts
Independent Variables
CONSTANT
Trend GDP(⫺1)
Trend UR(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 3.1a
⫺0.192
(2.96)
0.011
(0.87)
⫺0.019
(3.09)
⫺188.6
0.04
355/74
Model 3.2b
⫺0.194
(2.92)
0.011
(0.82)
⫺0.019
(3.15)
⫺185.8
0.05
351/73
Pooled probit regression for periods of stable or declining inflation, 19 OECD countries, 1961–93. Dependent variable is dummy for start
of inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates
in parentheses. a. Trend GDP and unemployment computed using 11-year centered moving average. b. Trend GDP computed using 11year centered moving average, trend unemployment is the NAIRU estimated using Elmeskov’s (1993) method.
JOHN F. BOSCHEN AND CHARLES L. WEISE : 329
TABLE 4
The Effect of Price Shocks on Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
OIL(⫺1)
FOOD(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 4.2a
Model 4.1
⫺0.250
(12.36)
0.047
(4.12)
⫺0.001
(1.29)
0.005
(2.19)
⫺0.294
(12.80)
0.049
(4.10)
0.002
(1.60)
0.011
(4.67)
⫺182.8
0.07
359/73
⫺167.0
0.15
359/73
Model 4.3b
⫺0.272
(13.24)
0.046
(4.12)
0.000
(0.47)
0.008
(3.94)
⫺153.2
0.11
358/60
Pooled probit regression for periods of stable or declining inflation, 19 OECD countries, 1961–93. Dependent variable is dummy for start
of inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates
in parentheses. GDP is detrended using an 11-year centered moving average. a. Uses contemporaneous values of OIL and FOOD instead
of lagged values. b. Same as Model 4.2, omitting 1979 observations.
The policy-mistake hypothesis implies that policies designed to expand real activity preceded inflation episodes. If the price level effects of expansionary policies
were unanticipated by the private sector (as well as by policy-makers), then these
policies would have effects on real activity. That is, a key artifact of a policy
mistake would be above-trend real activity and/or a declining unemployment rate
prior to the onset of inflation. To test this we introduce the once-lagged civilian
unemployment rate and once-lagged real GDP growth, both detrended using an 11year centered moving average, as variables in the probit model. We also run similar
TABLE 5
The Effect of Elections and Political Orientation on Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
FOOD(⫺1)
ELECT
ORIENT(⫺1)
∆ORIENT(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 5.1
⫺0.317
(10.75)
0.069
(4.79)
0.005
(2.22)
0.161
(3.28)
—
—
—
—
⫺161.1
0.11
320/69
Model 5.2
⫺0.334
(10.13)
0.074
(4.78)
0.005
(2.13)
0.178
(3.34)
0.027
(0.97)
⫺0.017
(0.39)
⫺148.6
0.12
293/65
Pooled probit regression for periods of stable or declining inflation, 19 OECD countries, 1961–93. Dependent variable is dummy for start
of inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates
in parentheses. GDP is detrended using an 11-year centered moving average.
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TABLE 6
The Effect of Fiscal Variables on Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
FOOD(⫺1)
ELECT
DEBT(⫺1)
BS(⫺1)
∆DEBT(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 6.1
⫺0.386
(6.49)
0.089
(4.69)
0.006
(2.47)
0.200
(3.27)
0.001
(0.80)
0.008
(0.71)
—
—
⫺121.9
0.15
259/54
Model 6.2
⫺0.383
(6.42)
0.091
(4.73)
0.007
(2.53)
0.203
(3.34)
0.000
(0.08)
—
—
0.012
(1.28)
⫺119.3
0.16
257/53
Pooled probit regression for periods of stable or declining inflation, 19 OECD countries, 1961–93. Dependent variable is dummy for start
of inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates
in parentheses. GDP is detrended using an 11-year centered moving average.
models without detrending either GDP growth or unemployment. The results from
these models are reported in Table 2.5
Estimation of Models 2.1 and 2.2 reveals a strong positive relationship between
lagged real GDP growth and inflation starts, whether or not GDP growth is detrended.
The point estimate in Model 2.1 implies that a 1% increase in GDP growth above
trend raises the probability of an inflation start by 4.7%. The coefficients on GDP
in Models 2.1 and 2.2 are clearly indicative of a Phillips curve relation with strong
GDP growth occurring ahead of accelerating inflation. Our finding of strong GDP
growth prior to inflation starts is consistent with the Taylor–De Long–Sargent hypothesis that the attempted exploitation of the Phillips curve played a significant role
in initiating many inflationary episodes. Furthermore, rapid expansion of GDP
growth prior to inflation episodes was not confined to the U.S. but appears to have
been systematic throughout the OECD countries.
As seen in the results for Model 2.1, the unemployment rate does not have
additional predictive power for inflation starts beyond that contained in real GDP
growth. Model 2.2 shows a statistically significant negative correlation between
actual unemployment and inflation starts, a finding consistent with the policymistake hypothesis. However, this correlation is driven primarily by the fact that
average unemployment rates in many OECD countries rose in the 1980s, a decade
that saw fewer inflation starts. Indeed, additional experiments (not reported in
Table 2) indicate that once variables such as Bretton Woods and European Exchange
Rate Mechanism (ERM) dummies are brought into the model, the coefficient on
the unemployment rate is always insignificant.
JOHN F. BOSCHEN AND CHARLES L. WEISE : 331
TABLE 7
The Effect of External Inflation on Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
FOOD(⫺1)
ELECT
INFDUS(⫺1)
INF(⫺1)
INFDGE(⫺1)
INFDJA(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 7.1
⫺0.270
(8.91)
0.066
(4.46)
0.004
(1.79)
0.166
(3.37)
0.043
(4.66)
—
—
—
—
—
—
⫺138.9
0.19
303/65
Model 7.2
⫺0.209
(4.02)
0.064
(4.36)
0.005
(2.07)
0.162
(3.37)
0.033
(2.84)
⫺0.013
(1.23)
—
—
—
—
⫺138.2
0.19
303/65
Model 7.3
⫺0.288
(7.16)
0.075
(4.40)
0.003
(1.21)
0.136
(2.44)
0.058
(4.12)
—
—
⫺0.020
(1.42)
0.012
(1.27)
⫺122.7
0.21
272/59
Pooled probit regression for periods of stable or declining inflation, OECD countries, 1961–93. Dependent variable is dummy for start of
inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient
estimates in parentheses. U.S. excluded from all regressions. Germany and Japan excluded from Model 7.3. GDP is detrended using an
11-year centered moving average.
2.2 Time Consistent Monetary Policy and the Phillips Curve
Parkin (1993) and Ireland (1999) have proposed the (Barro and Gordon 1983)
time-consistency model as an explanation for the rise and fall of U.S. inflation. In
both the Parkin and Ireland analyses, the key event triggering the 1970s inflation is
an exogenous rise in the natural rate of unemployment. This event increased the
wedge between the target unemployment rate and the natural rate and presented
the central bank with the incentive to engage in a more expansionary monetary policy.
Thus, in contrast to the policy-mistake hypothesis, the time-consistency hypothesis
implies that inflation episodes are preceded by an exogenous increase in the unemployment rate.
Table 2 shows that decreases (rather than increases) in the actual unemployment rate
preceded inflation starts. Model 3.1 repeats the model format in Table 2 but uses
the trend real GDP growth rate and the trend unemployment rate (measured as an
11-year centered moving average) as the explanatory variables. Model 3.2 uses the
Elmeskov (1993) measure of the nonaccelerating inflation rate of unemployment
(NAIRU). The results in Models 3.1 and 3.2 are similar because the moving average
of unemployment turns out to approximate Elmeskov’s estimates of the NAIRU.
In neither model do we find the predicted positive correlation between the natural
rate of unemployment and inflation starts suggested by the time-consistency model.
In contrast to our findings, Ireland (1999) finds empirical support for the timeconsistency explanation of inflation. There are two reasons for this difference. First,
Ireland’s empirical approach tests for a long-run relation between inflation and
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TABLE 8
The Effect of the Exchange Rate Regime on Inflation Starts
Independent Variables
CONSTANT
GDP(⫺1)
FOOD(⫺1)
ELECT
INFDUS(⫺1)
BW
ERM
BW*INFDUS(⫺1)
Log likelihood
McFadden R2
Number of observations (0/1)
Model 8.1
⫺0.292
(7.01)
0.073
(4.80)
0.003
(1.49)
0.170
(3.45)
0.052
(5.19)
0.164
(3.09)
⫺0.119
(1.78)
—
—
⫺129.1
0.25
303/65
Model 8.2
⫺0.300
(7.09)
0.073
(4.72)
0.004
(1.63)
0.171
(3.40)
0.043
(3.73)
0.193
(3.39)
⫺0.112
(1.66)
0.033
(1.47)
⫺127.9
0.25
303/65
Pooled probit regression for periods of stable or declining inflation, 18 OECD countries, 1961–93. Dependent variable is dummy for start
of inflation. Table reports marginal effect of variable evaluated at the mean of all variables. Absolute value of t-statistics for coefficient estimates
in parentheses. U.S. excluded from all regressions. GDP is detrended using an 11-year centered moving average.
unemployment. This approach treats the period after 1960 as having a single, but
very long, inflation episode that declined after the early 1980s. As we argued above,
the inflation experience in the U.S. and other OECD countries actually admits of
several distinct episodes of rising and falling inflation since 1960. When the full set
of inflation episodes is considered, we find that unemployment increases are
not closely matched with inflation starts. The second and more important difference is that Ireland’s findings are based on U.S. data only. The results here support
Taylor’s (1992) observation that the time-consistency model is less likely to explain
patterns of inflation and unemployment in other countries. In many OECD countries,
average unemployment rates rose in the 1980s, a period with fewer inflation episodes.
2.3 Price Shocks
Blinder (1982) and others have argued that the sharp increases in world commodity
prices in 1972–74 and 1978–79 were primary causes of the inflation episodes of
the 1970s. Ball (1995) argues that the price shocks of the 1970s may have triggered
inflation indirectly through monetary accommodation on the part of central banks.
However, De Long (1997) argues that while oil price shocks may have exacerbated
U.S. inflation in the 1970s, they could not be the ultimate cause of these episodes
because they occurred at a time when inflation was already high and rising.
In the empirical analysis, the oil price variable, OIL, is the percentage change in
the price of West Texas crude oil in U.S. dollars and the food price variable, FOOD,
is the percentage change in the IMF’s index of food commodity prices, in dollars.
The timing of oil and food price shocks in relation to inflation starts is shown in
JOHN F. BOSCHEN AND CHARLES L. WEISE : 333
Figure 3. The figure shows the number of inflation starts per year along with the
change in food and oil prices. Panel A shows that inflation starts are associated
with sharp increases in food prices in the years 1978–79 and (to a lesser extent)
1972–73 and 1987–88. As shown in Panel B, oil price increases do not closely
match inflation starts. The large price increases in 1974 occurred at a time when most
countries had been experiencing rising inflation for several years. The 1979–80
price spike coincided with a number of inflation starts, but the 1989–90 increase
did not. Seven inflation starts occurred in the year of and the year immediately after
the large drop in oil prices in 1986.6
The regression results presented in Table 4 confirm the impression given by
Figure 3. Model 4.1 includes lagged values of the oil and food price variables. In
this regression, food price shocks have a positive and statistically significant impact
on inflation starts. The marginal effect of the food price variable indicates that a
1% increase in food prices raises the probability of an inflation start by about 0.5%.
There is no evidence that oil price shocks triggered inflation episodes. In fact, the
coefficient on the lagged oil price increase in Model 4.1 is negative though not
statistically significant. Model 4.2 reports estimates of a regression similar to Model
4.1 using contemporaneous values of the oil and food price variables. Here oil prices
do have a positive and marginally significant effect on the probability of an inflation
start. However, the positive correlation is driven entirely by the 1979 episodes,
as shown in Model 4.3, which runs the same regression as in Model 4.2 but omits the
1979 inflation starts. The coefficient on food prices is statistically significant in
Models 4.2 and 4.3. As we discuss below, however, food price shocks are only
an important contributor to inflation starts in 1979. In fact, when the complete
empirical model (shown in Table 8) is run with the 1979 episodes omitted from
the sample, the effect of food price increases disappears while the effect of other
variables is unchanged. To summarize, there is little empirical evidence, outside the
1979 inflation starts, to support the hypothesis that oil and food prices shocks caused
the inflation starts in our sample.
2.4 Political Business Cycle and Partisan Models of Inflation
In the political business cycle models of Nordhaus (1975) and Lindbeck (1976),
central banks conduct an expansionary monetary policy in the period leading up to
an election in order to enhance the governing party’s chances for reelection. The
empirical evidence on the political business cycle hypothesis is mixed. McCallum
(1978) and Alesina (1988) reject the hypothesis. More recent work by Alesina
and Roubini (1997) shows that while elections have no impact on output and
unemployment, they do affect inflation.
In contrast to the political business cycle model, the partisan models of Hibbs
(1977, 1994) and Alesina (1988) assume politicians have different preferences over
unemployment and inflation. A common interpretation of the partisan model is
that left-leaning political parties will attempt to move to a higher inflation–lower
unemployment point on the Phillips curve. The switch from a right- to left-leaning
government is the critical factor triggering inflation, not elections per se. Table 5
Fig. 3 Changes in food and oil prices and number of countries experiencing inflation episode
JOHN F. BOSCHEN AND CHARLES L. WEISE : 335
examines the role of elections and political orientation in triggering inflation episodes. Given the findings in Table 4, we build on a model composed of a constant,
lagged detrended GDP growth and the lagged change in food prices. In Model 5.1
we add a variable, ELECT, which equals 1 during an election year and 0 otherwise.
The use of the contemporaneous value of the election dummy reflects the timing
of events in the political business cycle models discussed above. In these models,
the policy actions designed to influence election results take place in the year(s)
prior to the election, with the inflationary consequences being felt at or subsequent
to the election itself.
The results of Model 5.1 offer support for elections as a significant factor in
inflation starts. The coefficient on ELECT is positive and statistically significant,
indicating that inflation episodes are more likely to start during election years.
The impact is quantitatively large, with the occurrence of an election raising the
probability of an inflation start by 16%. In fact, of the 70 inflation starts available to
be matched with the election variable, 31 coincided with an election.7 All countries
except Ireland, Netherlands, and Spain have at least one inflation start that coincides with an election. Two large countries, Japan and the UK, each has three
episodes associated with elections. Election-related inflation episodes do not differ
significantly from other episodes either in severity of the inflation rise or in the
beginning rate of inflation.
It is possible that a government is more likely to call an election when there is
a strong economy, raising the issue of whether elections are an endogenous response
to policies that trigger inflation episodes. However, the regressions in Table 5 already
include real GDP growth as a separate argument, so the coefficient on the election
dummy represents an effect on inflation starts that is separate from the state of the
economy prior to the election. Furthermore, a regression of ELECT on lagged
economic variables including GDP growth, unemployment, and inflation shows that
none of these variables, alone or in combination, helps predict the occurrence of
an election.8 Alesina and Roubini (1997), using a similar set of countries, also find
that the timing of elections is not systematically affected by economic variables.
In Model 5.2 we test the partisan model of inflation starts. To Model 5.1 we add
a variable, ORIENT, which takes the value ⫹1 when a right-leaning government
is in power and –1 when a left-leaning government is in power. We also include
one-half the change in this variable, ∆ORIENT, which takes on the value ⫹1 on
the arrival of a right-leaning government and –1 on the arrival of a left-leaning
government. Neither variable has predictive power for the start of an inflation episode
in this regression, and they do not enter significantly in regressions (not reported) in
which they are entered separately. Contrary to predictions of partisan models of the
political business cycle, inflation episodes are no more likely to begin under leftwing governments than under right-wing governments.
Alesina and Roubini (1997, Table 6.3) find somewhat stronger support for the
partisan model than we report. They use a similar country set and time frame but
use quarterly data on inflation rates rather than a binary choice framework. Their
political orientation variable, which is identical to ours, is negatively correlated with
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: MONEY, CREDIT, AND BANKING
inflation, but the coefficient estimates are not statistically significant at the 10%
level. A dummy variable that also accounts for center-left and center-right governments, which we do not consider, produces slightly more significant results.
In addition to the difference in empirical framework, our regressions control for
GDP growth whereas Alesina and Roubini do not. We come closest to replicating
Alesina and Roubini’s results when we use the one-year ahead value of ORIENT
(which is closer to the timing used in their model) and do not control for GDP
growth. When GDP is omitted from our model (results not displayed), the coefficient
on ORIENT is negative, consistent with the partisan theory, but not significant at the
10% level.
2.5 Fiscal Policy
Increased levels of public debt have long been considered an important factor in
triggering inflation episodes. Friedman (1994) expresses the view that expansionary
fiscal policy has generated inflation in the U.S. by encouraging overly expansionary monetary policy. In the models of Calvo (1988) and Missale and Blanchard
(1994) higher levels of privately held government debt and longer maturity of the
debt raise the incentive for a government to attempt a surprise inflation.
Table 6 reports regressions to assess the importance of fiscal variables in triggering
inflation starts. In Model 6.1 of Table 6 we add two indicators of fiscal policy to
the regression of Model 5.1. The first fiscal variable is the once-lagged budget
surplus as a fraction of GDP (BS), and the second is the once-lagged level of central
government debt as a percentage of GDP (DEBT). Including the budget surplus
distinguishes between the effect of the stock of nominal debt and the effect of
expansionary fiscal policy. In Model 6.1 the budget surplus enters with the right
sign but is far from statistical significance. The coefficient on the debt variable is
also of the correct sign but again is not statistically significant. Model 6.2 replaces the
surplus measure, BS, with the lagged change in government debt, ∆DEBT. Neither
DEBT nor ∆DEBT is statistically significant in this regression. Overall, these findings
are not supportive of a role for fiscal variables in starting moderate inflations.
In their cross-sectional study of differences in long-run average inflation, Campillo
and Miron (1997) report a positive and significant relation between a country’s
public debt ratio and its average inflation rate. It is useful in interpreting the findings in
Table 6 to reconcile why the conclusions we draw are different from theirs. The
difference is principally due to two factors. First, the study of Campillo and Miron
uses as its fiscal variable a country’s debt-to-GDP ratio measured in the year 1975.
Although the high-GDP country list used in the Campillo and Miron study differs
somewhat from our sample of countries, auxiliary regressions done using our data
also exhibit a positive and marginally significant cross-country relation between the
debt-to-GDP ratio in 1975 and average post-1975 inflation. That is, our data
are capable of generating a cross-country debt–inflation correlation similar to that
found by Campillo and Miron. However, our study uses debt and inflation data
that span the entire post-1960 period, specifically including the important inflation
starts that occurred in the 1960s and early 1970s. Over this longer period, our
JOHN F. BOSCHEN AND CHARLES L. WEISE : 337
auxiliary regressions show that the positive correlation between average debt-toGDP and average inflation is smaller and is not statistically significant.9 The second
reason for the difference in results is that our tests involve a time series dimension
in addition to a cross-sectional dimension. Our regressions show that the timing of
movements in fiscal variables does not match up well with the start of inflation
episodes.
2.6 International Transmission of Inflation
An earlier literature was concerned with how U.S. inflation was transmitted abroad
under the Bretton Woods system of fixed exchange rates. Brunner and Meltzer
(1977), Cassese and Lothian (1982), and Darby (1983) found evidence of the
international transmission of inflation from the U.S. during the Bretton Woods
period. Canzoneri and Gray (1985) and Turnovsky, Basar, and d’Orey (1988)
have developed models in which expansionary policies abroad could cause the home
country to inflate even in a flexible exchange rate regime.
Table 7 presents evidence of the role of the U.S., Germany, and Japan in exporting
inflation episodes to the other countries in the sample. The key variable introduced
in the Table 7 models, INFDUS, is the spread between U.S. annual inflation and
the home country inflation. We also consider analogous variables for Germany
(INFDGE) and Japan (INFDJA). In the empirical models that include INFDUS, the
estimation excludes the U.S. episodes. In Model 7.3, which includes INFDGE and
INFDJA, German and Japanese episodes are excluded as well. Model 7.1 adds the
lagged value of INFDUS to the list of independent variables in Model 5.1.
The coefficient on the inflation differential in Model 7.1 is positive with a large
t-statistic, indicating strong support for the view that U.S. inflation plays an important
role in triggering inflation abroad. The estimated coefficient on INFDUS implies
that a 1% increase in the U.S. inflation rate relative to that in the home country
raises the probability of an inflation start by 4.3%. The positive coefficient is not
simply an artifact of a correlation between lagged home country inflation (INF) and
inflation starts. When lagged home country inflation (INF) is added separately in
Model 7.2, the coefficient estimate is insignificant, while that on INFDUS remains significant.
Model 7.3 adds the German and Japanese inflation differentials to Model 7.1 to
test whether other large countries export inflation to the same degree that the U.S.
does. The results suggest that neither country’s inflation rate has an important effect
on the probability of inflation starts in other countries. The negative coefficient on
the German inflation differential is not surprising. For most countries, inflation rose
relative to German inflation in the 1970s, during which inflation episodes were relatively frequent; and inflation fell relative to German inflation during the 1980s,
during which inflation episodes were relatively rare. The coefficient for the Japanese
inflation differential is positive but not statistically significant.
In Table 8 we investigate the effect of exchange rate regimes on the probability
of an inflation start. Model 8.1 adds to Model 7.1 dummy variables for the Bretton
Woods exchange rate system (BW) and the European Exchange Rate Mechanism
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: MONEY, CREDIT, AND BANKING
(ERM). The coefficient estimates signify that the probability of an inflation start
was 16% higher during the Bretton Woods era. The coefficient is significant at the
1% significance level. However, since the Bretton Woods dummy is essentially a
dummy variable for the period 1960–72, we cannot exclude the possibility that the
higher frequency of inflation starts is due to some other characteristic of the period
rather than the exchange rate system. One likely candidate, that policy-makers of
this era were attempting to achieve high real growth targets, would seem to be
eliminated because lagged GDP growth is already in the regression. The estimates
of Model 8.1 also offer support for the proposition that the ERM lowered the
probability of inflation starts. The coefficient on the ERM dummy is negative and
significant at the 10% level. The ERM dummy probably isolates an exchange rate
regime effect rather than a time or country effect. Of 17 inflation starts occurring
after 1979, only four occurred in countries that at the time were members of the
ERM. Three of the seven countries that were members of the ERM throughout
the period of its existence experienced no inflation episodes after 1979, while all
the non-ERM countries experienced at least one.
The influence of U.S. inflation as a trigger for inflation episodes in other countries
should have been stronger in the Bretton Woods era because foreign currencies were
pegged officially to the U.S. dollar. Model 8.2 includes the interaction of the Bretton
Woods dummy with the U.S. inflation–domestic inflation differential, BW*INFDUS(-1). The coefficient estimates suggest that a 1% increase in the U.S. inflation–
domestic inflation differential raises the probability of an inflation start by 4.3% in
the post-Bretton Woods era but by 7.6% in the Bretton Woods era. However, the
difference in the two periods is only marginally significant. The finding that U.S.
inflation policy continued to influence OECD inflation starts in the flexible exchange
rate period suggests the presence of implicit exchange rate smoothing. Such coordination is evident in some of the narrative accounts that we discuss in Section 3.
3. ANALYSIS OF SELECTED INFLATION STARTS
3.1 Methodology
In this section we use our empirical findings from Section 2 to measure the
contribution of each variable in triggering the inflation episodes in our sample.
The decompositions in Table 9 are derived from Model 8.2, which includes all
the key variables found to be correlated with inflation starts. The first column of
figures in each panel contains the estimated probabilities of the inflation start
(the fitted values from Model 8.2). That is, for a given country j and year t, the
estimated probability of an inflation start is
(
probjt ⫽ 1 ⫺ F ⫺
兺X
i
)
i,jtβi
,
where F(·) is the cumulative density function for the normal distribution, βi are the
coefficient estimates from Model 8.2, and i indexes the independent variables. These
probabilities can be compared with a naïve or baseline probability of inflation that
JOHN F. BOSCHEN AND CHARLES L. WEISE : 339
is computed from Model 8.2 when the values of ELECT, BW, and ERM are set to
0 and the other independent variables are set to their mean values. For our data and
model estimates the baseline probability of an inflation start in any period is 4%.10
We decompose the index, 兺iXi,jtβi, which is linear, rather than the nonlinear
probability function. Accounting for the deviation of the index from the baseline
serves as a proxy for a decomposition of the difference between the estimated and
baseline probabilities. Each number in the columns with variable-name headings in
Table 9 is the fraction of the difference between the index and its baseline value
that can be attributed to the deviation of each independent variable from its
baseline value. That is, the contribution of variable i for country j in year t is
computed as
conti, jt ⫽
βi(Xi, jt ⫺ X̄i)
兺iβi(Xi, jt ⫺ X̄i)
.
3.2 The 1969, 1979, and 1988 Episodes
Panels A, B, and C of Table 9 show decompositions of the inflation starts that
occurred in 1969, 1979, and 1988, respectively. These were the years with the
largest numbers of inflation starts. The columns labeled GDP, FOOD, ELECT, and
INFDUS summarize the contributions of these variables to inflation starts. The
column labeled OTHER summarizes the effects of the remaining variables. Panel
A reveals that, in addition to the inflationary bias that existed during the Bretton
Woods period (included in the “other” category), the main factor that triggered the
seven 1969 inflation starts was U.S. inflation. U.S. inflation rose from 2.8% in 1967
to 5.7% in 1968. Other things equal, the estimates in Model 8.2 suggest that,
occurring as it did during the Bretton Woods period, this inflation surge would raise
the probability of an inflation start in any given country by approximately 22%.
In the countries that did experience an inflation start, the model suggests that the
increase in U.S. inflation accounted for 49% of the excess probability of inflation
starts on average, ranging from 29% in Norway to 65% in Sweden. Elections
occurred in three of the countries, accounting for 25% of the probability of an
inflation start in Australia, 37% in New Zealand, and 49% in Norway. Food price
shocks and expansionary real activity (as measured by GDP growth) had essentially
no effect on the probability of an inflation start.
Panel B shows analogous breakdowns for the 13 inflation starts that are clustered
in 1979. In these episodes, U.S. inflation is again an important factor. U.S. inflation
rose from 6.4% to 8.5% from 1977 to 1978, which, other things equal, would raise
the probability of an inflation start by 9.0%. In the countries that did experience an
inflation start in 1979, the increase in U.S. inflation accounts for 45% of the excess
probability of an inflation start on average. The 13.4% increase in food commodity
prices that occurred in 1978 played a moderate role in the 1979 inflation starts.
Other things equal, the increase in prices raised the probability of an inflation start
by 5.4%. The model suggests that, in the countries that experienced an inflation
start in 1979, the increase in food prices accounted for 17% of the excess probability
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of an inflation start. The model also attributes an important role to elections in Austria,
Denmark, Finland, Italy, Japan, Sweden, and the UK in the 1979 inflation starts.
Real GDP growth is a major contributor to inflation starts in about half of the
countries, accounting for about 25% of the excess probability of an inflation start.11
Panel C of Table 9 shows the breakdown for the six 1988 inflation starts. The
rise in U.S. inflation from 1.3% in 1986 to 4.4% in 1987 accounts for 84% of
the probability of an inflation start in the countries listed. Food prices, which rose
4.7% in 1987, account for a small fraction. As with the 1979 episodes, real GDP
growth plays an important role in most of the countries experiencing an inflation
start in 1988; excluding Italy, GDP growth accounts for 28% of the excess probability
of an inflation start.
TABLE 9
Relative Importance of Major Variables in Selected Inflation Starts
Percent Due tob
Country
a
INFDUS
OTHERc
0.25
0.00
0.00
0.37
0.49
0.00
0.00
0.16
0.40
0.59
0.41
0.48
0.29
0.65
0.62
0.49
0.28
0.47
0.56
0.41
0.55
0.36
0.39
0.43
0.20
0.13
0.17
0.19
0.14
0.15
0.33
0.08
0.17
⫺27.18
0.29
0.13
0.10
0.17
0.00
0.46
0.00
0.64
0.48
0.00
1.11
0.28
0.00
0.00
0.00
0.43
0.34
0.31
0.47
0.78
0.93
0.52
0.45
0.33
⫺0.19
0.46
0.93
⫺55.95
0.20
0.35
0.21
0.45
0.00
0.00
⫺0.37
⫺0.42
0.00
⫺0.34
⫺0.72
0.00
⫺0.38
0.00
0.00
0.00
0.00
⫺0.19
⫺0.08
0.20
2.10
0.18
0.36
0.73
0.58
0.11
0.16
0.40
0.07
0.07
0.07
0.15
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.97
1.51
0.65
0.75
0.95
0.21
0.84
0.00
⫺0.88
⫺2.16
0.00
⫺0.39
0.00
⫺0.57
0.24
0.26
0.11
0.00
0.19
0.29
0.56
0.11
⫺0.11
0.34
Probability
GDP
FOOD
A. 1969 EPISODES
Australia
Austria
Denmark
New Zealand
Norway
Sweden
Switzerland
Average
0.81
0.42
0.32
0.50
0.33
0.59
0.55
0.50
0.10
⫺0.02
0.07
⫺0.23
⫺0.29
0.01
0.02
⫺0.05
⫺0.02
⫺0.03
⫺0.04
⫺0.03
⫺0.04
⫺0.03
⫺0.03
⫺0.03
B. 1979 EPISODES
Australia
Austria
Belgium
Denmark
Finland
Ireland
Italy
Japan
Netherlands
New Zealand
Norway
Sweden
UK
Averaged
0.27
0.44
0.33
0.29
0.42
0.38
0.17
0.78
0.33
0.06
0.19
0.48
0.62
0.39
0.33
⫺0.37
0.28
0.07
⫺0.07
0.85
0.48
0.18
0.28
84.13
0.51
0.10
0.34
0.25
C. 1988 EPISODES
Austria
Belgium
Italy
Japan
Netherlands
Spain
Average
0.20
0.14
0.09
0.34
0.32
0.35
0.24
D. AVERAGES
1969, 1979, and 1988 episodes
Others
0.39
0.38
ELECT
JOHN F. BOSCHEN AND CHARLES L. WEISE : 341
TABLE 9
Relative Importance of Major Variables in Selected Inflation Starts
E. U.S., GERMANY, AND JAPAN
Percent Due tob
Country
a
Probability
GDP
FOOD
ELECT
INFDUS
OTHERc
1965
1972
1977
1986
0.51
0.63
0.27
0.13
0.40
0.13
1.11
2.69
U.S.
0.04
0.04
⫺0.11
⫺1.69
0.00
0.39
0.00
0.00
0.00
0.00
0.00
0.00
0.56
0.44
0.00
0.00
1978
1987
0.31
0.22
0.32
0.13
Germany
⫺0.03
⫺0.21
0.00
0.82
0.71
0.80
0.00
⫺0.54
1967
1972
1979
1988
0.63
0.14
0.78
0.34
0.16
⫺1.03
0.18
0.18
Japan
0.05
0.09
0.08
0.07
0.31
0.99
0.28
0.00
0.14
⫺0.15
0.46
0.75
0.35
1.11
0.00
0.00
a
Fitted values from probit regression Model 8.2. bPercentage difference between average probability and baseline probability due to each
variable. Computed as follows: Baseline probability is probability of an inflation start when continuous variables (GDP, FOOD, INFDUS)
are set to mean values and dichotomous variables (ELECT, BW, ERM) are set to 0. Baseline probability ⫽ 1 ⫺ F(⫺X̄β) ⫽ 0.04, where F
is the normal distribution, X̄ is the vector of independent variables set to baseline values, β is the coefficient vector. Contributions of each variable are computed relative to the index, Xβ. The percentage due to variable i is βi(Xi ⫺ X̄)/兺i β(Xi ⫺ X̄). cBW, ERM.
d
Average excludes New Zealand. eAverage excludes U.S. (all years), Norway 1972, New Zealand 1979, and Sweden 1987.
Panel D compares averages of the contributions of each variable over the 1969,
1979, and 1988 episodes with the 37 non-U.S. episodes that did not occur in
1969, 1979, or 1988. Not surprisingly, in the sample that excludes the 1969, 1979,
and 1988 episodes, purely domestic variables play a more important role in producing
inflation while international variables have a diminished role. Of particular note is
the fact that U.S. inflation accounts for only 11% of the probability of an inflation
episode in the episodes that did not occur in 1969, 1979, or 1988 (versus 56% in the
episodes that did occur in those years); elections accounted for 29% (versus 19%);
food price shocks accounted for 0% (versus 11%).
3.3 Inflation Episodes in the U.S., Germany, and Japan
Panel E shows the decomposition of inflation starts that occurred in the three
largest economies, the U.S., Germany, and Japan. Model 8.2, which is used for
the German and Japanese simulations, includes INFDUS and, as discussed above,
was estimated without the U.S. episodes. The U.S. decompositions are computed
in the same way as the others, except for having the U.S. inflation differential set
to 0.
For the U.S., expansionary domestic real activity (high GDP growth) played the
major role in three of four inflation starts. However, the results for the 1986
episode are not informative because the model does not explain this episode very
well—the model predicts only a 13% probability of an inflation start that year. The
1965 U.S. episode is largely due to excess GDP growth, a finding consistent with
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the Taylor–De Long–Sargent argument that the tendency to expansionary economic
policy in the 1960s led to the first major U.S. inflation episode. The 1972 inflation
start was associated with an election, which our model predicts had an important
effect on the probability of an inflation start. The political influences on U.S. macroeconomic policy around 1972 have been widely discussed, most recently by De Long.
De Long suggests that a major reason for the increase in inflation at the time was
President Nixon’s opposition to policies that would produce high unemployment at
the time of the election. Consistent with this, our model interprets the election
itself as the underpinning for policies that ultimately caused an inflation episode to
begin.
The results in Panel E also imply that the Bretton Woods monetary system had a
separate impact, raising the probability of inflation starts in the U.S. in 1965 and
1972. A possible interpretation is that under the Bretton Woods system the U.S. was
insulated from the adverse exchange rate consequences of monetary expansion. On
the other hand, as we noted above, the Bretton Woods variable has a somewhat
ambiguous interpretation because its construction closely matches the time frame over
which the long-run Phillips curve trade-off view held sway in U.S. policy circles.
The 1977 U.S. inflation, like that of 1965, was largely driven by rapid GDP growth.
Increases in food prices did not play an important role in any of the episodes. This
is in contrast to Blinder’s (1982) conclusion that food prices were the triggering
event in the U.S. inflations of the early and late 1970s and is due to our ability to
identify the start of these episodes as occurring well before the initial rise in food
prices.
Our model attributes the bulk of Germany’s 1978 inflation start to U.S. inflation
and excessive GDP growth. This is consistent with the narrative analyses of this
period (e.g., Laubach and Posen, 1997, and Clarida and Gertler, 1997). Laubach
and Posen (pp. 19–20) write that in 1977, “signs of weakness in economic activity
combined with a strong appreciation of the deutsche mark” prompted the Bundesbank
to allow money growth to exceed targets. The Bundesbank’s inflationary response to
the appreciation of the deutsche mark is an example of the mechanism we proposed
above by which U.S. inflation is transmitted to other countries. This was reinforced when, at the Bonn economic summit in July 1978, Germany (along with
Japan) agreed to accelerate growth even further to match the expansionary policies
of the U.S. in order to stimulate growth of the world economy.
The German inflation problem of the early 1990s is typically associated with the
reunification of East Germany and West Germany (see, for example, Blanchard
2000). Our analysis dates the start of the most recent German inflation episode in
1987, three years prior to reunification. The model attributes this episode to U.S.
inflation and the 1987 election. While we were unable to find reference to the 1987
election in the narratives, the narratives do offer a description of events that is
consistent with a large role for U.S. inflation. Both papers cited above note a switch
to a more expansionary monetary policy in 1987–88, brought about by concern
with the deutsche mark’s appreciation relative to the dollar and its effect on economic growth.12
JOHN F. BOSCHEN AND CHARLES L. WEISE : 343
Cargill, Hutchison, and Ito (1997) and Hetzel (1999) discuss the 1972, 1979, and
1988 Japanese inflation episodes at length. Japan’s 1972–74 inflation, in which
trend inflation rose from 5.4% to 16.8%, is a watershed event in Japanese monetary
history. High inflation during this period is usually attributed to two factors: the Bank
of Japan’s purchases of dollars in order to stabilize the yen–dollar exchange rate
during the collapse of Bretton Woods, and the first OPEC oil shock. Our model,
by contrast, attributes the inflation start to the 1972 election. Cargill, Hutchison,
and Ito’s analysis also suggests a political business cycle explanation. They point
out that the Bank of Japan cut the discount rate six times between October 1970
and June 1972 to stimulate growth and note that “political pressure to maintain a
rapidly expanding economy, leading to excessive cuts in the discount rate, was
exerted by Prime Minister Tanaka to support his “Reconstruct the Japanese Archipelago Plan” (Cargill, Hutchison, and Ito 1997, p. 35). Our model assigns a large role
to U.S. inflation and a positive, though more modest, role for expansionary demand
policy in the 1979 and 1988 Japanese inflation episodes. Again, the historical
record is roughly consistent with this finding. As mentioned above, at the Bonn
summit in 1978 the Japanese government committed itself to a high growth policy.
Hetzel (1999) describes the period leading up to the 1988 inflation episode. U.S.
monetary policy turned expansionary in 1985–86, increasing U.S. demand for imports and increasing Japan’s current account surplus, despite a concurrent appreciation of the yen. The Louvre Accord of 1987 committed Japan to reducing its
current account surplus through expansionary demand policy. Consistent with this
commitment, the Bank of Japan reduced interest rates from 7% in 1985 to 3.75%
in the third quarter of 1987. Inflation began to increase the following year.
4. CONCLUSIONS
What emerges from our study is a view of the origins of inflation episodes that
combines features of the Taylor–De Long–Sargent policy-mistake hypothesis, the
international transmission of inflation, and the political business cycle. High real GDP
growth prior to many inflation starts is consistent with the idea that policy-makers
focused on the short-term benefits of stimulating real growth while avoiding the
costs of ending incipient inflation. Exchange rate stability concerns seem to have led
other countries to follow U.S. inflation policy even after the demise of Bretton Woods.
Thus policy mistakes in the U.S., coupled with the U.S. role as a leader in setting
inflation policy, contributed to a large number of inflation starts in OECD countries in
the 1960s and 1970s. Through 1979, U.S. inflation episodes are linked to two major
inflation outbreaks in other countries totaling 20 inflation episodes. After 1979,
U.S. inflation is linked to only one outbreak of six inflation episodes. The decline in
the total number of inflation starts after 1979 is consistent with a more conservative
U.S. monetary policy and with more flexible international monetary arrangements
after Bretton Woods. Elections also played an important role in triggering inflation
episodes. The correlation between inflation starts and elections is particularly surprising
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given that our experiments controlled separately for the inflation-triggering effects of
stimulating GDP growth. More research is needed on this finding.
Our empirical findings do not support a number of other possible explanations
for inflation episodes. Oil and food price shocks, while probably aggravating inflation, were not the triggering events for inflation episodes except possibly in 1979.
Fiscal policy, the political orientation of ruling parties, and increases in the natural
rate of unemployment were not correlated with inflation starts. Our results do not
prove that these factors were not important in individual episodes or cannot be a
factor in future episodes, only that they did not have systematic effects in our sample
of inflation episodes.
APPENDIX A
TABLE A1
INFLATION EPISODES
Trough Date
Peak Date
Trough Inflation
Peak Inflation
0.05
2.52
8.01
5.45
1.14
3.62
14.38
10.31
8.59
3.50
2.69
3.48
1.26
8.61
6.65
4.00
0.58
2.36
4.42
1.00
4.54
12.18
8.27
3.56
0.58
3.02
7.24
3.81
4.47
10.47
11.32
5.66
3.11
4.45
7.97
8.73
8.45
12.39
11.25
12.00
4.19
2.23
7.01
3.25
7.19
16.24
11.33
6.42
2.65
2.37
5.20
8.82
5.29
5.76
11.72
12.78
Australia
1962:3
1968:4
1978:1
1984:1
1993:1
1965:2
1974:2
1982:1
1986:2
1995:1
1968:2
1978:2
1987:2
1974:3
1981:1
1991:3
1961:1
1967:3
1978:4
1987:1
1965:2
1975:1
1982:2
1990:1
1961:1
1970:4
1976:4
1985:2
1968:2
1974:3
1981:2
1990:1
1964:1
1968:4
1976:1
1978:1
1966:3
1973:4
1977:1
1980:2
1965:3
1969:4
1978:4
1986:4
1967:2
1975:1
1981:1
1989:1
1960:2
1966:3
1971:2
1977:1
1962:3
1969:3
1974:3
1981:1
Austria
Belgium
Canada
Denmark
Finland
France
TABLE A1
INFLATION EPISODES
Trough Date
Peak Date
Trough Inflation
Peak Inflation
Germany
1967:1
1977:3
1986:3
1973:1
1981:1
1992:2
1962:3
1966:3
1978:2
1964:3
1975:2
1981:2
1967:4
1978:3
1987:3
1975:2
1980:4
1989:4
1966:3
1971:3
1978:3
1987:1
1970:2
1974:1
1980:2
1990:2
1961:1
1967:3
1978:3
1987:1
1965:1
1975:2
1980:4
1991:2
1960:2
1968:4
1972:1
1978:1
1983:4
1968:1
1971:1
1976:2
1980:2
1985:4
1968:4
1971:2
1978:4
1984:4
1971:1
1975:1
1981:1
1987:1
1960:3
1969:2
1987:2
1965:2
1977:2
1989:3
1963:3
1968:2
1972:2
1978:4
1986:3
1966:2
1971:1
1977:1
1980:3
1990:1
1960:1
1968:4
1977:4
1986:3
1962:4
1973:4
1981:3
1990:4
1963:1
1966:3
1978:2
1986:3
1965:2
1975:1
1980:2
1989:2
1964:1
1971:4
1976:4
1985:4
1969:4
1974:2
1980:1
1989:4
1.28
2.83
⫺0.01
6.84
5.87
4.95
2.51
2.69
9.49
5.70
18.81
18.62
1.35
12.31
4.54
16.96
18.53
6.40
3.61
5.35
3.64
0.00
7.11
16.77
6.65
3.63
0.82
3.07
3.80
⫺0.58
6.73
9.72
6.69
3.22
1.04
3.94
7.29
11.69
6.75
5.64
8.76
14.79
15.45
15.13
2.97
6.50
5.82
5.61
8.50
10.20
12.59
8.04
1.11
2.98
4.70
10.06
20.30
6.85
2.96
2.32
5.61
8.09
3.96
5.64
7.34
10.88
11.97
9.44
0.36
2.20
1.03
0.97
3.94
9.33
6.03
5.92
2.29
2.59
9.70
3.14
4.74
19.01
15.20
8.66
1.22
3.61
5.73
2.45
5.49
9.54
11.77
5.14
Ireland
Italy
Japan
Netherlands
New Zealand
Norway
Spain
Sweden
Switzerland
UK
U.S.
346
: MONEY, CREDIT, AND BANKING
APPENDIX B: DATA SOURCES
GDP: Real GDP growth. Source, IMF.
UR: Civilian unemployment rate. For Belgium, Denmark, Germany, Greece,
Spain, France, Ireland, Italy, Luxembourg, Netherlands, Austria, Portugal, Finland,
Sweden, UK, U.S., and Japan: European Commission, European Economy 1997.
For Australia, Canada, Norway, New Zealand, Switzerland: R. Layard et al. (1994).
FOOD: Percentage change in index of food commodity prices, in current dollars.
Source: IMF CD-ROM, line 00176exd. Includes bananas, cereals (maize, rice, and
wheat), meat (beef and lamb), vegetable oils and protein meals (coconut oil, fish
meal, groundnut oil, palm oil, soybeans, soybean meal, and soybean oil), and sugar.
OIL: Percentage change in dollar price of West Texas crude oil. Source, Federal
Reserve Economic Data (FRED).
ELECT: Dummy: 1 if an election was held in that year, 0 otherwise. Source,
Alesina and Roubini (1997).
ORIENT: Dummy: ⫹1 if right-wing government in power, -1 if left-wing government in power. Source, Alesina and Roubini (1997).
BS: Central government budget surplus, percent of nominal GDP. Source, IMF.
DEBT: Total nominal domestic and foreign government debt, percent of nominal
GDP. Source, IMF.
INFDUS, INFDGE, INFDJA: U.S., German, Japanese, CPI inflation rate minus
home country CPI inflation rate. Source, IMF.
ERM: Dummy variable for membership in the European Exchange Rate Mechanism. For the bulk of the countries involved, the participation began in 1979. For
Sweden, the date was 1989, and for the UK, 1990.
BW: Dummy: 1 if Bretton Woods exchange rate system in place, 0 otherwise.
Takes value of 1 for all countries from 1960–72.
All IMF data are from the International Financial Statistics CD-ROM, issue date
July 1997.
NOTES
1. Trend inflation is a nine-quarter centered moving average of CPI inflation.
2. The nine-quarter moving average produces trend turning points that are very close to a HodrickPrescott filter trend using a smoothness parameter of 400. The findings do not change substantively if
a seven-quarter moving average is used.
3. In practice, most inflation episodes in our data set are clearly preceded by periods of declining
inflation and therefore pose no ambiguities in identification. The exceptions to this pattern are cases
where trend inflation was close to zero for a substantial period of time prior to the rise in inflation, a
circumstance that prevailed in several countries in the early 1960s. In these cases we identified the start
date as the year in which inflation began a rise of 2% or more. The episodes are Belgium, 1961:1–
1965:2, in which trend inflation was 0.41% at the trough prior to the upturn; Canada, 1962:1–1968:2,
in which trend inflation was 0.66% at the trough; and Ireland, 1962:3–1964:3, in which inflation was
2.51% at the trough.
4. A number of other theories have been proposed to explain the genesis of inflation in developing
countries, among them political instability, macroeconomic populism, and optimal collection of seigniorage. These explanations do not seem relevant for the countries in our sample.
JOHN F. BOSCHEN AND CHARLES L. WEISE : 347
5. As a check on the robustness of our empirical findings, we reran our regressions with dummy
variables designed to capture country-level fixed effects. These did not enter significantly and had
essentially no effect on the other estimates.
6. There is an interesting identification issue here. It is possible that in some cases we identify an
inflation start only because subsequent increases in oil prices pushed inflation above the 2% threshold.
We looked for this effect in the inflation episodes of the early 1970s. In all the countries experiencing
an inflation episode in 1973, annual inflation had already risen by 2% or more by the first quarter of
1973. The first spike in oil prices did not come until August 1973.
7. There are 121 elections in total in 409 country-years for which data are available, so the unconditional
probability of an election occurring in a given year for a given country is 29.6%, implying that the
expected number of elections during years associated with inflation starts is 20.7. The probability of
getting x matches in n independent draws where the probability of a match is p is given by
f(x) ⫽
()
n x
p (1 ⫺ p)n⫺x .
x
In this case, n ⫽ 70 and p ⫽ 0.296. The probability of getting 31 or more matches is found by integrating
f(x) from x ⫽ 31 to x ⫽ 70. Under the null hypothesis that elections and inflation starts are independent,
the probability of x ≥ 31 is 0.006.
8. The estimated equation from a probit regression including only the 15 countries with endogenous
timing of elections (excluding Norway, Sweden, Switzerland, and the U.S.) is
ELECT ⫽ ⫺ 0.34 ⫺ 0.0006GDP(⫺1) ⫺ 0.014UR(⫺1) ⫹ 0.05D (UR(⫺1)) ⫺ 0.02 INF(⫺1)
(⫺0.02)
(⫺0.77)
(⫺0.53)
(⫺1.04)
t-statistics are in parentheses. The p-value for the test that all coefficients except the constant are 0 is 0.80.
9. In a cross-sectional regression of average inflation on the debt-to-GDP ratio (1975) for 1975–93,
the coefficient on debt has a t-statistic of 1.6. In the full sample the t-statistic on the debt coefficient
falls to 0.7.
10. The baseline probability differs from the unconditional probability of an inflation start in two
ways. First, due to the nonlinearity of the function F(·), the probability of an inflation start evaluated at
the means of the explanatory variables will not equal the proportion of inflation starts in the sample.
Second, the baseline probability assumes all dummy variables are 0, whereas their mean values in
the sample are greater than 0.
11. This average excludes New Zealand. As is apparent from Panel B, Model 8.2 has essentially no
explanatory power for the 1979 New Zealand inflation start-up. Likewise, the model cannot explain
the episodes in Norway (1972) and Sweden (1987); these episodes are excluded from the averages
taken below.
12. Interestingly, Laubach and Posen note that in 1988 the Bundesbank announced a change in its
monetary target from the central bank money stock (CBM), which had been growing faster than target,
to M3, which had been growing at a rate within its target range. This maneuver allowed the Bundesbank to
continue its expansionary policies without appearing to be in violation of its self-imposed money
growth targets.
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