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Core, periphery, and the collapse of the interwar gold standard
Peter Kugler, University of Basle
Tobias Straumann, University of Zurich1
10 February, 2010
Abstract
Building on recent research the paper reviews the collapse of the interwar gold standard. It
introduces two new elements: it focuses on the difference between core and periphery and
applies an ordered probit model instead of duration analysis. The results suggest that the coreperiphery difference was highly relevant and that papers neglecting this difference overstate
the vulnerability of the core countries. In particular, core countries were capable of
maintaining the gold standard in the face of negative GDP growth, banking crises and
government instability, while countries of the periphery showed a high vulnerability in these
instances.
1
Corresponding author: Tobias Straumann, University of Zurich, Institute for Research in Empirical Economics,
Winterthurerstrasse 30, CH–8006 Zurich, +41 44 634 35 69, [email protected]
1 1. Introduction
There is a broad consensus among economic historians that the suspension of the gold
standard was a precondition for the recovery from the world economic crisis of the 1930s
(Choudhri and Kochin 1980, Eichengreen and Sachs 1985, Bernanke and James 1991).
Recently, this insight has led a number of scholars to study the factors explaining why some
countries went off gold earlier than others (Wolf and Yousef 2007, Wandschneider 2008,
Wolf 2008). Following the seminal paper of Meissner (2005) who studied the emergence of
the classical gold standard in the late 19th century they have used duration analysis in order to
identify the crucial factors driving the collapse of the interwar gold standard.
As this kind of research is still in its infancy, there is much room for different perspectives. In
this paper we introduce two new elements. First, we study the collapse of the interwar gold
standard from a core-periphery perspective. Several narrative accounts have suggested that
many countries of the periphery had no choice but to follow the exchange-rate policy of their
main trading partners (Brown 1940, Eichengreen 1992, James 2001). Yet, nobody has tested
this proposition in a systematic way. Another reason for introducing the core-periphery
dimension is methodological. If core and periphery react differently, statistical analysis which
does not account for the core-periphery distinction is likely to generate distorted results. In
particular, the lack of differentiation may be the reason why the recent papers mentioned
above come up with a rather broad set of significant variables, making it hard to see the basic
mechanism behind the collapse of the interwar gold standard. It may well be that there was no
such thing as a basic mechanism, but it is also clear that economic historians have not
explored all statistical possibilities yet.
The second new element of this paper is that we apply a different econometric method than
the scholars cited above. This methodological change involves five issues. First, while
2 duration analysis relates a “change” variable (leaving the gold standard) to its potential
economic and political determinants, we use a specification in which the “state” variable
“being on or off the gold standard” is related to economic and political indicators. Thus, in
contrast to duration analysis we use not only the data before leaving the gold standard but take
into account the data after this decision. Second, we allow for a richer menu of exchange rate
regimes than “being on or off the gold standard” by considering the option of capital controls
with an unchanged parity as an intermediate state before leaving the gold standard and an
additional restriction after going off gold. This possibility was used for instance by Eastern
European countries like Czechoslovakia and Romania. These choices were probably not
accidental, but were linked to basic economic factors. Therefore instead of a binary probit
model we apply an ordered probit model with the four possible states “being on gold with old
parity”, “imposing capital controls with old parity”,
“being off gold with an official
suspension or a depreciation relative to gold” and “being off gold and imposing capital
controls”. Third, we introduce two new variables, namely the level of foreign debt and the
denomination of the foreign debt. Bordo and Flandreau (2003) have shown that financial
maturity mattered for the choice of the exchange rate regime during the era of the classical
gold standard and since the demise of Bretton Woods. There are strong reasons to assume that
they were equally important for the demise of the interwar gold standard. Fourth, all authors
cited above pool the data for a couple of countries over time without fully taking into account
the panel structure of such a macroeconomic dataset. In particular unobserved cultural and
political country characteristics may strongly bias the estimates if the indicators included in
the model are correlated with these unobserved characteristics. As most of the country
characteristics are more or less time invariant we can represent them by a country fixed effect
as a “catch all” variable. Fifth, we explicitly test for the significance of the core-periphery
dimension by allowing different coefficients for these two groups of countries in our model.
3 Our results seem to confirm that it is worthwhile to introduce a distinction between core and
periphery and a different methodology render. Predictably, countries of the periphery were
much more vulnerable than core countries. The former responded to a decline in GDP and
negative growth, diminishing gold reserves and banking crises, while core countries devalued
only because gold reserves diminished and major trading partners had depreciated their
currencies. In other words, peripheral countries showed little resistence to external shocks,
whereas core countries were willing and able to deal with external shocks of all sorts. The
devaluation of their currencies was mainly motivated by network externalities: as everybody
else devalued, it became more and more costly to maintain an overvalued currency.
The remainder of the paper is organized as follows. Section 2 provides a survey of the
literature. Section 3 defines the core-periphery divide. Section 4 discusses the model, the
choice of variables and the data. Section 5 presents the results. The paper ends with a short
conclusion.
2. Survey of the literature
In contrast to the classical gold standard before World War I the gold exchange standard of
the interwar years had a rather short life. The system began to operate in the mid-1920s when
the British government decided to restore the old monetary order by bringing sterling back to
its prewar parity against the US dollar, and it ended in the first half of the 1930s when one
country after another went off gold or introduced exchange controls. Yet, despite of its short
duration, the interwar gold standard proved to have devastating effects. It contributed to the
propagation of the great depression and prevented governments and central banks to pursue
expansionary policies (Temin 1989, Eichengreen 1992, Ahamed 2009). Instead of creating a
4 stable monetary order which was supposed to foster trade and investment it acted as a major
force crippling the world economy.
For the sake of simplicity, we can divide this dramatic episode into four stages (table 1). In
the first period (1929-30), the group of countries abandoning the gold standard was confined
to countries in Oceania (Australia and New Zealand) and South America (Argentina, Brazil,
Paraguay, Uruguay). In the second period (1931-32), a series of banking, debt and currency
crises hit Central and Eastern Europe. Austria, Germany, Hungary, and a number of other
countries in the region responded with the introduction of exchange controls. The next victim
was sterling which the British government took off gold in September 1931. This shock led a
series of countries to take the same step, namely India, Portugal, the Northern European
countries, Japan and Canada. The major event of the third phase was the devaluation of the
US dollar in April 1933. In the final phase, the so-called gold bloc, formed by Belgium,
France, Italy, the Netherlands, Poland, and Switzerland at the London Economic Conference
in July 1933, broke apart. Italy introduced capital controls in May 1934, Belgium devalued its
currency in March 1935, Poland went off gold in April 1936, and the remaining three gold
bloc countries decided to finish their experiment in September 1936.
[Table 1 about here]
Ever since it happened, the causes of the dramatic collapse have been intensely debated. Only
recently, however, economic historians have started to use quantitative methods in order to
understand why some countries abandoned the gold standard earlier than others. Simmons
(1994) pioneered this kind of research. Her goal was to demonstrate that not only economic,
but also institutional and political factors influenced a country’s decision to abandon the gold
standard. As she was primarily interested in the political side of the gold standard, she used
only a limited number of economic factors, however. Wolf and Yousef (2007),
5 Wandschneider (2008) and Wolf (2008) were the first to come with a comprehensive set of
variables as well as a large country sample and to use a more dynamic model (duration
analysis) instead of the rather static regression analysis.
The factors tested by these authors can be divided along three types or “generations” of
currency crisis models (Krugman 2000, Wolf 2008). The first type emphasizes
macroeconomic imbalances, typically caused by an expansionary monetary policy. As a
result, the real exchange rate appreciates to an unsustainable level, the current account balance
turns negative, and central banks reserves begin to shrink. Investors, sensing the unsustainable
path, precipitate the crisis by selling large amounts of domestic assets. Under the gold
exchange standard of the interwar years, macroeconomic imbalances emerged because the
gold standard had been restored at too high a parity, trading partners devalued or agricultural
world prices declined at a faster rate than industrial ones during the crisis. Denmark, Norway
and the United Kingdom restored the gold standard at the old parity at all costs, while
Belgium and France deliberately fixed their exchange rate at an undervalued level.
Deteriorating terms of trade due to a devaluation of a major economic power was a problem
for any small open economy depending on a few export markets. Again, Denmark is a typical
case as it shipped roughly two thirds of its total exports to the British market. When sterling
fell in September 1931, Copenhagen had little choice (Hoffmeyer and Olsen 1968). As for the
relative decline of agricultural prices, the early exit of some Latin American countries as well
as Australia and New Zealand can be cited as examples.
The second type of models emphasizes the self-fulfilling character of speculative attacks.
Even when the exchange rate reflects the underlying economic fundamentals, investors can
6 find it attractive to attack a currency. By selling domestic assets, they force the authorities to
adopt more restrictive policies, hoping that the resulting acceleration of the economic
downturn will lead to a devaluation. Second-generation models also highlight contagion.
When investors sense that a government began to show signs of weakness, they turn to
countries that are in a similar position or have close economic relations. As for the interwar
years, Eichengreen and Jeanne (2000) have shown how the British authorities were reluctant
to increase interest rates to avert speculative attacks because of their concern about the
growing social costs of their orthodox monetary policy. In September 1931 they threw in the
towel. Contagion played a role when after the outbreak of the German crisis in the summer of
1931 investors not only began to mistrust the British pound, but also the currencies of all
small countries entertaining trade relations with Germany (Straumann 2010).
Third-generation models, developed after the Asian crisis, focus on the banking sector of
emerging markets. Large banking conglomerates, entertaining strong ties with the government
and enjoying implicit state guarantees, attract large funds of foreign short-term capital and
invest them in long-term projects to further economic development. Negative new
information can trigger a panic among foreign investors. Short-term funds flow out, central
bank reserves dwindle, and the currency comes under strong pressure. A typical example of
the 1930s is the devaluation of the Swedish krona. A commercial bank having strong ties with
the liberal government used foreign short-term capital to finance the long-term plans of Ivar
Kreuger, notably his credits to countries ceding him the match monopoly. When the German
crisis broke out, investors withdrew their funds from Sweden causing a dramatic reduction of
central bank reserves and ultimately the suspension of the gold standard in late September
1931.
Testing all these possible channels and effects requires a broad range of variables. The group
of real variables includes the level of GDP per capita, the growth rate of GDP, industrial
7 production, unemployment, and trade networks. The group of financial and monetary factors
comprise price and interest rate differentials, central bank reserves, the creditor/debtor status
and the occurrence of banking crises, and the last group assembles political and institutional
factors, namely central bank independence, the inflation history, the political regime, cabinet
changes, strength of parliament, and social unrest.
Roughly speaking, all authors find that a whole set of factors determined the time when a
country abandoned the gold standard. Wolf and Yousef find that most of the variables they
tested were significant: the economic shock, the national commitment to the gold standard,
the exits of major trading partners, the global adherence to gold, the perceived costs and
benefits of adhering to the gold standard and political instability. Wandschneider reports that
high per capita income, international creditor status, and prior hyperinflation increased the
probability that the gold standard was maintained. By contrast, democratic regimes had the
tendency to leave earlier than dictatorships. Furthermore, unemployment, membership in the
sterling group, higher inflation, and the experience of banking crises reduced the duration of
the gold standard in a particular country. Wolf’s results show that the time of exit was
dependent on the extent of deflationary pressure, the existence of a banking crisis, the cover
ratio, the character of the political regime (authoritarian or democratic), the independence of
the central bank, the history of prior devaluations, and the patterns of trade integration.
Of the three papers, only Wolf and Yousef discuss the core-periphery dimension. Their
methodological approach is not convincing, however.
They only estimated their model
separately for core and periphery countries including only economic, credibility,
network/mentality and political factors, respectively. In addition, there is no formal statistical
test of the difference between core and periphery coefficients reported. Of course, this
approach will provide strongly bias results, as all these four groups of factors are correlated.
The full model including all these factors jointly was not estimated taking into account the
8 core/periphery dimension. Furthermore, their definition of the core is debatable as it is mainly
based on GDP per capita without any further qualification.3 In other words, the core-periphery
dimension still needs to be explored.
Summarizing, recent empirical research on the basis of duration analysis has greatly advanced
a more systematic understanding of the collapse of the interwar gold standard. However, the
results are somewhat inconclusive as almost any factor has been found to be relevant.
Furthermore, in some cases the findings in some cases can differ considerably. While for
example Wandschneider finds that central bank independence is irrelevant, Wolf comes to the
counter-intuitive conclusion that countries with a weak central bank were likely to remain
longer on the gold standard than countries with a highly independent central bank. For these
reasons, we have chosen a different path by applying an ordered probit model and by
focussing on the difference between core and periphery.
3. Defining core and periphery
There is a huge literature on the economic relevance of core and periphery, notably in the
tradition of world-systems analysis. But in the field of monetary history only Flandreau and
Jobst (2005) have come up with a rigorous definition. We adopt their framework developed
for the period of the classical gold standard and adjust it to the new realities of the world after
World War I. We also experiment with two other definitions of core and periphery, one based
on international financial relations and one linked to the international political landscape.
3
The 12 core/center countries include Australia, Belgium, Canada, Denmark, France, Germany, Netherlands,
New Zealand, Sweden, Switzerland, the UK and the US. The rest are coded as periphery countries.
9 The starting point of Flandreau and Jobst is the assumption that the range of circulation of a
particular currency reflects the international economic position of the currency’s country.
Accordingly, they draw a map of how frequently a currency was quoted in foreign markets.
Their results show that the international monetary geography is best described by a three-tier
system. The core consists of the three great European powers United Kingdom, Germany and
France, the second intermediary group covers most of developed Europe, Russia and the
United States, and the periphery regroups all the rest (Table 2). For our purpose, however, the
three-tier system is too difficult to handle from a methodological standpoint. We therefore
rely on a dual view which is also provided by Flandreau and Jobst. It is essentially a mix of
the first and second group of the three-tier system, with only Denmark, Norway and Portugal
being relegated to the periphery. On the eve of World War I, the new core is made up of
eleven instead of three countries: the great European powers United Kingdom, Germany and
France as well as Austria-Hungary, Belgium, Switzerland, Spain, Italy, Netherlands, Russia,
and the United States (Table 2).
[Table 2 about here]
Unfortunately, the analysis stops in 1910 so that we lack a well defined list for the interwar
years. But we think that it is possible to adapt it to the new realities by making only minor
revisions on the basis of two well-informed contemporaries, namely the Swiss banker Felix
Somary and the British financial journalist Paul Einzig. Both described the changes of the
international monetary order after World War I (Somary 1929, Einzig 1931). There is no
doubt that the two empires which collapsed towards the end of World War I, Austria-Hungary
and Russia, were not part of the core any more during the interwar years. It is equally clear
that Germany, although suffering from severe economic and political setbacks, remained part
of the core since it continued to be the major power of Central and Eastern Europe. The same
kind of judgement applies to Belgium and Italy. They payed a high price for their war
10 involvement, although they were part of the winning coalition, but they were also capable of
maintaining their prewar position as secondary international financial centers (Brussels,
Milan). Belgium also continued to be one of the few European countries being able to export
capital in the 1920s. As for the small neutral countries, their role was rather enhanced than
hampered by World War I (Straumann 2010). The Dutch guilder and the Swiss franc
improved their relative position in the international monetary geography. Spain, another
neutral, was able to maintain its status as major economic and monetary power in Southern
Europe and Latin America.
World War I did not only induce a shrinking of the core, but also added a new member. In a
similar vein as the Netherlands and Switzerland, Sweden began to play a more important
regional role in the 1920s than before the war. While being a typical late industrializing
country importing great amounts of capital before the war, it belonged to the small and
privileged group of European capital exporters in the 1920s, and the Swedish krona therefore
established itself as a currency of some international importance. Sweden, however, seems to
be the only country that experienced an upward grading. Its Scandinavian neighbors Denmark
and Norway, though profiting from the war as small neutrals, did not undergo a comparable
improvement of their international position. Denmark continued to send two thirds of total
exports to the British market, mostly for the English breakfast table, and Norway remained
highly dependent on shipping and fisheries. Accordingly, their currencies were still irrelevant
in international finance. The same is true for Portugal, another small neutral which survived
the war without major damage. Clearly, World War I reduced the number of countries
belonging to the core (Table 2).
Because the definition of the core countries may be crucial for the results, we also test two
alternatives (Table 2). The first one deletes Germany from the list because it can be argued
that its defeat in World War I relegated the country, similary as Austria-Hungary and Russia,
11 to an emerging market. Perhaps the most negative consequence of the lost war was that the
change of the reparations regime due to the Young Plan dramatically weakened the resilience
of the economy and the political room of manoeuvre. After 1929, Germany was not allowed
any more to borrow foreign capital to pay parts of the reparations bill (Ritschl 2003). This
interpretation, though disputed by Temin (2008), needs to be considered by our definitions of
core and periphery. It also has the advantage that the omission of Germany fulfills a clear
criterion: the core countries are all either winners of World War I or major neutrals. It is
therefore essentially a political definition of the core.
The second alternative to the monetary definition highlights the central role played by
international capital movements. Creditor countries had more possibilities to defend the gold
standard than debtor countries. Especially those countries which imported large amounts of
US capital in the second half of the 1920s were severely suffering from the reversal of the US
funds following the steep increase in the discount rate of the Fed towards the end of the
decade. It therefore seems legitimate to use this divide as the defining criterion for core and
periphery. Another advantage of this definition is that the list of core countries becomes
considerably smaller which provides a useful contrast to the monetary or political definition.
4. Model, variables and data
So far, economic historians have applied duration analysis in order to identify the crucial
factors leading to the exit of individual countries. For two reasons, we prefer an ordered probit
model. First, while duration analysis relates a “change” variable (leaving the gold standard) to
its potential economic and political determinants, we use a specification in which the “state”
12 variable “being on or off the gold standard” is related to economic and political indicators.
Thus, in contrast to duration analysis we use not only the data before leaving the gold
standard but take into account the data after this decision. Second, an ordered probit model
allows for a richer menu of exchange rate regimes than “being on or off the gold standard” by
considering the option of capital controls before or after leaving the gold standard. This
possibility was used for instance by Eastern European countries like Czechoslovakia and
Romania. These choices were probably not accidental, but were linked to basic economic
factors. Therefore instead of a binary probit model we apply an ordered probit model with the
four possible states “being on gold with old parity”, “imposing capital controls with old
parity” , “being off gold with an official suspension or a depreciation relative to gold” and
“being off gold with imposing capital controls”.
Furthermore, we are sceptical of how all authors working with duration analysis pool the data
for a couple of countries over time without fully taking into account the panel structure of
such a macroeconomic dataset. In particular unobserved cultural and political country
characteristics may strongly bias the estimates if the indicators included in the model are
correlated with these unobserved characteristics. As most of the country characteristics are
more or less time invariant we can represent them by a country fixed effect as a “catch all”
variable. And finally, we explicitly test for the significance of the core-periphery dimension
by allowing different coefficients for these two groups of countries in our model.
In our model the ordinal observable variable to be explained is the variable y taking the value
0 for “being on gold”, 1 for “imposing capital controls”, 2 for “being off gold” and 3 for
“being off gold with capital controls”, respectively. The ordered probit model is based on a
regression model for one underlying non-observable variable y* depending on observed xvariables with a normally identically and independently distributed error term. The latent
variable y* represents the willingness of countries to remove the gold standard restrictions. If
13 this unobservable dependent variable gets larger than a first bound we observe that the ordinal
variable y takes the value 1 (imposition of capital controls). If it increases further and crosses
a second bound we observe y to take the value 2 “being off gold” and so on. These bounds are
unknown and have to be estimated jointly with the regression coefficients by maximum
likelihood. A positive (negative) value of such a regression coefficient means that an increase
in the corresponding variable makes the country more (less) willing to remove the gold
standard restrictions. Formally the model with N countries and T periods panel data set with
fixed effects can be written as follows:
k
k
j =1
j =1
yit* = ∑ β j x jit + ∑ δ j x jit dci + α i + ε it
I = 1,2,...N ; t = 1,2....T
yi = s, if
γ s−1 < yi* ≤ γ s , s = 0,1,2,3
γ −1 = −∞, γ 3 = ∞
α i : country fixed effect
dci : core dummy (1 for core countries, 0 otherwise)
ε it : normal error term
The second set of regressors, the interaction terms x-variables with the core dummy, allows
for core-peripheries difference: if some of the δ j are different from zero we have a different
reaction of the core countries to the corresponding x-variable and the respective core
coefficient is the sum of β j and δ j , whereas the periphery value is, of course, β j .
As we prefer to represent some country characteristics by a country fixed effect, the number
of our x-variables is considerably reduced. We have omitted the degree of independence of
the central bank, the inflation history, the degree of democratization, and the extent of social
unrest. In addition, we exclude the variable creditor/debtor status because, as explained in the
third section, it can be used as an alternative criterion to divide the world into core and
14 periphery. And finally, for the sake of simplicity, we do not test interest rate and inflation rate
differentials as their effects can be caught by other variables, in particular by GDP growth and
change of gold reserves. Consider a country with a high inflation rate. Under the gold
standard its real exchange rate will appreciate, slowing down GDP growth and reducing gold
reserves as the external balance deteriorates. A high interest rate also leads to weaker or
negative GDP growth, but can have very different effects on the gold reserves. In the ideal
world of the price-specie-flow mechanism, the gold cover ratio is supposed to increase as
foreign capital flows in. Within the framework of the second generation currency crises in
which a high interest rate can lead to a self-fulfilling prophecy as investors interpret the high
yield as a sign of weakness the gold cover ratio is likely to decrease dramatically (Obstfeld
1994).
Apart from these omissions, our set of variables is quite similar to the ones used by the papers
which are based on duration analysis. Our list starts with GDP per capita (GDPPC). The
choice of this variable is motivated by the assumption that rich countries were more resilient
to the negative effects of the economic downturn than poor ones. It is, however, important to
note that the level of GDP per capita is not related to our selection of core countries.
Australia, Canada, Denmark and New Zealand had a higher level of GDP per capita than
France, Germany and Sweden, but are not part of the core according to our definition. In other
words, the periphery is a very heterogeneous group with respect to GDP per capita. It is
therefore likely to see a significant effect of this variable.
The second variable is the growth of GDP per capita (GGDPPC). The assumption is that
countries with a particularly severe contraction are more likely to devalue or to introduce
capital controls. The economic downturn can exert a negative effect on the stability of a
currency through several channels. A high rate of unemployment can make the central bank
hesitate to increase the discount rate in the face of capital flight as second-generation models
15 suggest. It can also lead to a banking crisis or a dramatic decrease of central bank reserves
triggering a run on the currency. Whether or not this variable plays a different role for core
and periphery countries is hard to predict.
As for the monetary side, we focus on the growth of gold reserves (GGRLA). This variable
reflects all kinds of shocks, for example a deterioration of the terms of trade, a drop in foreign
demand relative to domestic demand or a reversal of capital movements. It is also of vital
importance because it was one of the most important indicators guiding the discussions and
actions of central bankers. In particular, a steep decline of gold reserves often led the
authorities to abandon the gold standard, for example in Great Britain and Sweden in late
September 1931.
The fourth and fifth variables reflect international linkages. When major trading partners
devalue, it becomes more likely that a country abandons the gold standard. We therefore
calculated the share of exports affected by a devaluation of trading partners (EXDVL: Export
Share Devaluing countries). Likewise, the currency in which the foreign debt is denominated
matters. If the currency remains on the gold standard, the debtor country may not leave the
gold standard because it would be too costly to allow an increase of the foreign debt. The
variable DDVL (Debts Share Devaluing currencies) represents the share of foreign debt
affected by the devaluation of the creditor country. Both variables are expected to be more
important for countries of the periphery than for core countries.
The share of foreign debt (FDSLA) accounts for the amount of foreign debt in relation to total
debts. Countries with high a share are more likely to be more vulnerable to economic shocks.
It is a way to integrate the insight by Bordo and Flandreau (2003) concerning the importance
of financial maturity for the exchange rate regime during the classical gold standard and since
16 the demise of Bretton Woods. Core countries are able to issue international securities
denominated in domestic currency while countries of the periphery are not.
A banking crisis, expressed by a dummy variable (DBC), can force the government and the
central bank to lower interest rates and to expand the money supply which may contradict the
rules of the gold standard and subsequently lead to a weakening of the currency. This is one
of the central aspects of the third-generation model.
As noted, the change of cabinet (CCH) is the only political variable we are explicitly testing
as it is time-variant.5 It is an indicator for political instability and we expect a positive
influence on our latent endogenous variable. We would expect that it is more relevant to the
periphery than to the core. As explained in section 3, we test three different definitions of the
core, expressed by a dummy variable (DC). We also let the core dummy interact with all
variables listed above.
The country dummy variable (D*) takes account of the problem that unobserved cultural and
political country characteristics may strongly bias the estimates if the indicators included in
the model are correlated with these unobserved characteristics.
We use only lagged explanatory variables in order to avoid simultaneity and reverse causation
problems. Most of the variables are defined three years moving average in order to smooth
out transitory fluctuations.
Our sample includes the following countries:
- Europe: Austria, Belgium, Bulgaria, Denmark, Finland, France, Germany, Greece, Hungary,
Italy, Norway, Romania, Portugal, Poland, Spain, Sweden, Switzerland, UK
5
Recall that all time-invariant cultural and political characteristics are represented by the country fixed effect
17 - North America: Canada and the US
- South America: Argentina, Brazil, Chile, Peru, Uruguay and Venezuela
- Asia/Oceania: Japan, Australia and New Zealand
GDP data are taken from Maddison (2001), gold reserves from the League of Nations
(statistical yearbook), trade data from Mitchell (2003),currency denomination of foreign debt
from United Nations (1948), share of foreign debt from the League of Nations, banking crisis
data from Bernanke and James (1991), change of government from Banks (1971).
5. Results
Table 3 reports our results generated by an ordered probit model for the indicator variable
with four values as described above. We use annual data from 1926-1938 if available. As for
some countries not all x-variables are available for the full sample we have an unbalanced
panel consisting of 288 observations consisting of time series with variable length for 29
countries. We report the results for the adjusted Flandreau/Jobst core periphery classification.
However, the results for the two alternative classifications discussed in section 3 provide
results which only differ marginally from those given in Table 3. These results are available
on request from the authors.
Before turning to the result for the non-core countries let us mention that the pseudo Rsquared of 0.469 indicates a good model fit. Six x-variables are statistically significant at the
5% level (one-sided) for the periphery, namely GDP level and GDP growth, growth rate of
gold reserves, the share of exports going to devaluing countries, the banking crisis dummy as
well as the number of cabinet changes. Most of them have the expected sign: higher growth
decreases the propensity to relax the gold standard restrictions, whereas all other indicators
18 representing banking crises and export difficulties as well as political turmoil have a positive
influence on the disposition to go off gold. The growth rate of gold reserves has the expected
negative sign. The only surprising result is the positive sign of the level of per capita GDP
indicating that rich countries in the periphery were more willing to go off gold than poorer
ones. Thus countries in the periphery show a common pattern of the disposition to leave the
gold standard in response to a decline in GDP and negative growth, diminishing gold
reserves, banking crises and political instability. The coefficient estimate for the foreign debt
share is clearly statistically insignificant.
For the core countries things are different: we see that four of the interaction terms are
individually different from zero. In addition, the F-value for hypothesis that all core dummy
interaction coefficients are jointly zero is 5.962 and the null hypothesis is rejected at any
reasonable significance level (third panel in Table 3). In the fourth panel the coefficient
estimates for the core countries (the sum of the periphery and interaction term estimates) and
their standard errors are displayed. The coefficients of per capita GDP, the growth of gold
reserves and the export share with devaluing countries is highly statistical significant at the
0.1% level. Note that all coefficients have the “right” sign. All other x-variables are jointly
statistically insignificant and individually relatively small compared to their standard errors.
Thus there are only three common factors determining the disposition to go off gold among
core countries, namely a low level of per capita GDP, shrinking gold reserves and, statistically
most significant, the fact that others went off gold previously. Thus the early devaluation of
the pound in 1931 appears as a country specific phenomenon captured in our modelling
framework by the country fixed effect. All other core countries were initially willing to
withstand all the difficulties and absorb the shocks within the system of the gold standard. For
instance core countries were more able and willing to absorb negative GDP growth, a banking
crisis and political instability than countries of the periphery. Notably the US suffered from
19 four banking crises until a new administration under President F. D. Roosevelt changed
course in spring 1933. Similarly, Belgium, France and Switzerland managed to contain their
banking crises in 1931.
These results ultimately stem from the fact that the core countries on average remained longer
on the gold standard than the peripheral countries. The gold bloc consisted almost entirely of
core countries, with the exception of the Polish speaking countries (Danzig, Lithuania,
Poland). Taken together, our results suggest that the core-periphery divide was central to how
the interwar gold standard was collapsing from 1929 to 1936.
Finally we should mention that the results are robust with the respect to our choice of a four
values indicator probit model instead of a simple binary probit model with only two different
states “being on gold” (0) and “being off gold” (1): Table for reports qualitatively strongly
similar results for this model. However, it can be seen the ordered probit model provides a
higher degree of statistical significance of the estimates.
6. Conclusion
Ever since it occurred, many scholars have highlighted the core-periphery dimension of the
collapse of the interwar gold standard from 1929 to 1936. However, hardly any economic
historian has tried to catch this story by the means of econometrics. In this paper, we present
some evidence on the basis of an ordered probit model. The results suggest that the coreperiphery divide was essential for the chronology of the collapse. In fact, the story is not only
gradually, but completely different, depending on the position of a country in the international
economy. The crucial difference was that only countries of the periphery abandoned the gold
standard in reaction to negative GDP growth, banking crises and government instability. Put
differently, core countries were resilient enough to absorb the domestic consequences of the
20 depression, be they economic, financial or political. Only with respect to their external
relations, namely trade and capital, did they share the same vulnerabilities as the countries of
the periphery. In addition, the level of GDP per capita mattered in both country groups: rich
countries were more resilient than poor ones.
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23 Table 1: Year of end of interwar gold standard (selected countries) *
Continent
First phase
Second phase
Third phase
Fourth phase
(1929-30)
(1931-32)
(1933)
(1934-36)
Northern Europe
Denmark
Estonia
Finland
Norway
Sweden
Western Europe
UK
Belgium
France
Netherlands
Central Europe
Austria
Switzerland
Czechoslovakia
Germany
Eastern Europe
Mediterranean
Bulgaria
Danzig
Hungary
Lithuania
Roumania
Poland
Greece
Italy
Portugal
Yougoslavia
North America
Canada
USA
Central America
South America
Argentina
Brazil
Paraguay
Uruguay
Asia
India
Neth. East Indies
(Indonesia)
Japan
Oceania
Australia
New Zealand
Sources: League of Nations (1939), Aldcroft and Oliver (1998), Officer (2001), Obstfeld and Taylor (2004).
Note: * We consider either a depreciation or the introduction of foreign exchange controls as the break with the
gold standard, even in these cases in which the government suspends the gold standard later.
24 Table 2: Definitions of core countries
Monetarydefinition
Monetarydefinition
Political definition
Financialdefinition
1900
1930
1930
1930
Flandreau and Jobst 2005
Flandreau and Jobst
Winners of WW I and
Creditor countries
adjusted
major neutrals
Belgium
Belgium
Belgium
Belgium
France
France
France
France
Germany
Germany
Italy
Italy
Italy
Netherlands
Netherlands
Netherlands
Spain
Spain
Sweden
Sweden
Sweden
Switzerland
Switzerland
Switzerland
Switzerland
UK
UK
UK
UK
USA
USA
USA
USA
Austria-Hungary
Netherlands
Russia
Spain
25 Table 3: Ordered Probit Model
0: GS, 1: Exchange Controls with GS, 2: Devaluation,
Exit GS , 3: Devaluation, Exit GS and Exchange
EXRN
Controls
GDPPCLA
Per Capita GDP (ma lagged 3 years)
GGDPPCLA
Growth Per Capita GDP (ma lagged 3 years)
GGRLA
Growth Gold Reserves(ma lagged 3 years)
EXDVL
Export Share Devaluing countries( lagged 1 year)
DDVL
Debt Share Devaluing currencies( lagged 1 year)
FDSLA
Share Foreign Debt (ma lagged 3 years)
DBC
Banking Crisis Dummy
CCH
Change of Government
DC
Core Dummy (BE,FR,CH,NL,UK,US)
D*
Country Dummy
Dependent Variable: EXRN
Method: ML - Ordered Probit (Quadratic hill climbing)
Included observations: 288 after adjustments
Number of ordered indicator values: 4
26 Convergence achieved after 8 iterations
QML (Huber/White) standard errors & covariance
Coefficient Std. Error
z-Statistic
Prob.
GDPPCLA
0.029855
2.952626
0.0032
GGDPPCLA
-0.103190 0.020474
-5.039981
0.0000
GGRLA
-2.61E-05 7.75E-06
-3.360061
0.0008
EXDVL
0.023087
0.008413
2.744130
0.0061
DCDL
0.004836
0.005289
0.914363
0.3605
DBC
0.802895
0.445121
1.803767
0.0713
CCH
0.300163
0.142651
2.104177
0.0354
FDSLA
0.233093
0.706891
0.329744
0.7416
DC
5.960094
1.581709
3.768135
0.0002
GDPPCLA*DC
-0.068971 0.013403
-5.145790
0.0000
GGDPPCLA*DC
0.122899
2.765484
0.0057
GGRLA*DC
-0.026697 0.005525
-4.832279
0.0000
EXDVL*DC
0.037971
0.014398
2.637212
0.0084
DCDL*DC
0.008299
0.010752
0.771872
0.4402
DBC*DC
-0.835821 0.621115
-1.345680
0.1784
CCH*DC
-0.285386 0.224102
-1.273464
0.2029
FDSLA*DC
0.172228
0.049299
0.9607
DAU
-1.785704 0.683557
-2.612370
0.0090
DBE
-0.699084 1.473210
-0.474531
0.6351
DCH
0.207638
0.350892
0.7257
DDK
-2.546091 0.889558
-2.862197
0.0042
DFI
-2.069742 0.485654
-4.261760
0.0000
DGE
0.172926
0.055498
0.9557
DIT
-2.009256 0.810581
-2.478784
0.0132
DNL
-1.883756 0.684381
-2.752496
0.0059
DNO
-1.819362 0.464798
-3.914304
0.0001
0.010111
0.044440
3.493525
0.591743
3.115899
27 DPT
1.203903
DSW
0.676593
1.779360
0.0752
-2.141604 0.858774
-2.493793
0.0126
DAR
0.035912
0.055664
0.9556
DAS
-3.675287 0.820958
-4.476825
0.0000
DBR
1.590854
2.214117
0.0268
DBU
-2.238768 0.782403
-2.861401
0.0042
DCA
-3.322924 0.546540
-6.079926
0.0000
DCL
-0.906749 0.464685
-1.951320
0.0510
DHU
-1.301073 0.357921
-3.635090
0.0003
DNZ
-3.428446 0.855857
-4.005861
0.0001
DPE
-0.373169 0.519288
-0.718617
0.4724
DRO
-1.244013 0.842749
-1.476138
0.1399
DUS
1.094894
0.724485
1.511272
0.1307
0.645163
0.718505
Limit Points
LIMIT_1:C(40)
1.441085
1.034910
1.392473
0.1638
LIMIT_2:C(41)
1.502153
1.033381
1.453629
0.1460
LIMIT_3:C(42)
3.790398
1.043814
3.631296
0.0003
Akaike info criterion
1.481142
Schwarz criterion
2.015324
Log likelihood
-171.2844
Hannan-Quinn criter.
1.695210
Restr. log likelihood
-323.1666
Avg. log likelihood
-0.594738
LR statistic (39 df)
303.7644
LR index (Pseudo-R2)
0.469981
Probability(LR stat)
0.000000
28 Joint and Single Test of Core Periphery Difference
Wald Test:
Test Statistic
Value
df
Probability
F-statistic
5.962324
(9, 246)
0.0000
Chi-square
53.66092
9
0.0000
Core Coefficient Estimates and Joint Test
Wald Test:
Test Statistic
Value
df
Probability
F-statistic
9.106582
(8, 246)
0.0000
Chi-square
72.85265
8
0.0000
Normalized Restriction (= 0)
Value
Std. Err.
GDPPCLA
-0.039116
0.008555
GDPPCLA
0.019709
0.039136
GGRLA
-0.026723
0.005525
EXDVL
0.061058
0.012092
DCDL
0.013135
0.009367
DBC
-0.032927
0.430946
CCH
0.014777
0.171803
FDSLA
0.405320
3.420966
Null Hypothesis Summary:
29 Table 4 Binary Probit Model
Dependent Variable: EXRB (0: “on gold”, 1: “off
gold”
Method: ML - Binary Probit (BHHH)
Included observations: 288 after adjustments
Convergence achieved after 2589 iterations
QML (Huber/White) standard errors & covariance
Variable
Coefficient Std. Error
z-Statistic
Prob.
C
-3.109188 1.821980
-1.706488
0.0879
GDPPCLA
0.043982
2.426645
0.0152
GGDPPCLA
-0.098729 0.041287
-2.391292
0.0168
GGRLA
-1.55E-05 5.71E-06
-2.709213
0.0067
EXDVL
0.025341
0.012349
2.052079
0.0402
DCDL
0.013061
0.007243
1.803181
0.0714
DBC
0.979974
0.505867
1.937218
0.0527
CCH
0.538394
0.295217
1.823726
0.0682
FDSLA
2.785173
1.838917
1.514573
0.1299
DC
3.371443
2.284026
1.476097
0.1399
GDPPCLA*DC
-0.061983 0.020628
-3.004847
0.0027
GGDPPCLA*DC
0.271896
3.048865
0.0023
GGRLA*DC
-0.026967 0.011570
-2.330734
0.0198
EXDVL*DC
0.082821
0.031539
2.626007
0.0086
DCDL*DC
0.072600
0.074555
0.973779
0.3302
DBC*DC
-0.669621 0.750756
-0.891929
0.3724
CCH*DC
-0.359253 0.369136
-0.973227
0.3304
FDSLA*DC
-5.465537 4.123237
-1.325545
0.1850
DAU
-5.203589 1.645541
-3.162236
0.0016
0.018125
0.089180
30 DBE
-7.416042 6.553801
-1.131563
0.2578
DCH
-1.471311 1.019147
-1.443669
0.1488
DDK
-5.289646 1.467751
-3.603911
0.0003
DFI
-3.634721 1.389436
-2.615968
0.0089
DGE
2.883643
0.863463
0.3879
DIT
-2.770277 0.928498
-2.983612
0.0028
DNL
-3.378303 1.629056
-2.073780
0.0381
DNO
-2.178102 1.020011
-2.135371
0.0327
DSW
0.298096
0.358573
0.7199
DAS
-4.810625 1.860203
-2.586075
0.0097
DBR
0.777147
0.589899
0.5553
DBU
-3.639534 1.509473
-2.411129
0.0159
DCA
-4.119451 1.303329
-3.160714
0.0016
DCL
-3.047501 1.500149
-2.031466
0.0422
DHU
-1.791626 0.899699
-1.991361
0.0464
DNZ
-4.741750 1.875148
-2.528733
0.0114
DPE
-1.283821 1.644889
-0.780491
0.4351
DRO
-2.483250 1.478493
-1.679582
0.0930
DUS
-0.689533 1.676500
-0.411293
0.6809
Mean dependent var
0.649306
S.D. dependent var
0.478018
S.E. of regression
0.294104
Akaike info criterion
0.754651
Sum squared resid
21.62429
Schwarz criterion
1.237959
Log likelihood
-70.66981
Hannan-Quinn criter.
0.948332
Restr. log likelihood
-186.5881
Avg. log likelihood
-0.245381
LR statistic (37 df)
231.8367
McFadden R-squared
0.621252
Probability(LR stat)
0.000000
Obs with Dep=0
101
Total obs
288
Obs with Dep=1
187
3.339626
0.831339
1.317425
31 Joint and Single Test of Core Periphery Difference Wald Test:
Test Statistic
Value
df
Probability
F-statistic
5.122697
(9, 250)
0.0000
Chi-square
46.10427
9
0.0000
Core Coefficient Estimates and Joint Test
Wald Test:
Test Statistic
Value
df
Probability
F-statistic
6.737437
(8, 250)
0.0000
Chi-square
53.89950
8
0.0000
Normalized Restriction (= 0)
Value
Std. Err.
GDPPCLA
-0.018001
0.009848
GGDPPCLA
0.173168
0.099047
Null Hypothesis Summary:
32 GGRLA
-0.026983
0.011570
EXDVL
0.108162
0.029021
DCDE
0.085661
0.074202
DBC
0.310353
0.554738
CCH
0.179141
0.221604
FDSLA
-2.680364
3.690456
33