1

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 core-

periphery 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, straumann@iew.uzh.ch

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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

 3

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.

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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

 5

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),

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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

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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

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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

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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.

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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

 11

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,

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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”

 13

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 x-

variables 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

 14

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:

termerrornormalotherwisecountriescorefordummycoredc

effectfixedcountry

syifsyTtNI

dcxxy

it

i

i

sisi

itii

k

jjitj

k

jjitjit

:)0,1(:

:,

3,2,1,0,,....2,1;,...2,1

31

*1

11

*

ε

αγγ

γγ

εαδβ

∞=−∞==≤<=

==

+++=

−

−

==∑∑

The second set of regressors, the interaction terms x-variables with the core dummy, allows

for core-peripheries difference: if some of the jδ 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 jβ and jδ , whereas the periphery value is, of course, jβ .

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

 15

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

 16

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

 17

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

 18

- 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 R-

squared 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

 19

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

 20

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 core-

periphery 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

 21

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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Penguin Press.

Banks, Arthur S. (1971), Cross-Polity Time-Series Data, Cambridge (Mass.) and London:

The MIT Press.

Bernanke, Ben S., and Harold James (1991), «The Gold Standard, Deflation, and Financial

Crisis in the Great Depression: An International Comparison», in R. Glenn Hubbard (ed.),

Financial Markets and Financial Crises, Chicago and London: The University of Chicago

Press, pp. 33-68.

Bordo, Michael, and Marc Flandreau (2003), “Core, Periphery, Exchange Rate Regime and

Globalization”, in Michael Bordo, Alan Taylor and Jeffrey Williamson (eds.), Globalization

in Historical Perspective, Chicago: University of Chicago Press, pp. 417-468.

Brown, William Adams (1940), The International Gold Standard Reinterpreted, 1914-1934,

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 22

Choudri, Ehsan U., and Levis A. Kochin. “The Exchange Rate and the International

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Eichengreen, Barry (1992), Golden Fetters: The Gold Standard and the Great Depression,

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in 1931,” in Paul Krugman (ed.), Currency Crises, Chicago: University of Chicago Press.

Eichengreen, Barry, and Jeffrey Sachs (1985), “Exchange Rates and Economic Recovery in

the 1930s”, Journal of Economic History 45 (4), December, pp. 925-946.

Einzig, Paul (1931), The fight for financial supremacy, London: Macmillan.

Flandreau, Marc, and Clemens Jobst (2005), “The Ties that Divide: a Network Analysis of the

International Monetary System, 1890-1910, The Journal of Economic History 65, pp. 977-

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James, Harold, The End of Globalization: Lessons from the Great Depression. Cambridge,

MA: Harvard University Press, 2001.

Krugman, Paul (2000), “Introduction,” in Paul Krugman (ed.), Currency Crises, Chicago and

London: The University of Chicago Press, pp. 1-7.

League of Nations, Statistical Yearbook, various issues.

Maddison, Angus (2001), The world economy: a millennial perspective, Paris: OECD.

Meissner, Christopher M. (2005), “A new world order: explaining the international diffusion

of the gold standard, 1870.1913”, Journal of International Economics 66, pp. 385-406.

 23

Mitchell, Brian R. (2003), International historical statistics, 1750-2000, 3 vols., New York:

Palgrave Macmillan.

Simmons, Beth (1994), Who adjusts? Domestic sources of foreign economic policy during the

interwar years, Princeton: Princeton University Press.

Somary, Felix (1929), Wandlungen der Weltwirtschaft seit dem Kriege, Tübingen:

Mohr/Siebeck.

Straumann, Tobias (2010), Fixed ideas of money: small states and exchange rate regimes in

20th century Europe, Cambridge and New York: Cambridge University Press.

Temin, Peter (1989), Lessons from the great depression, Cambridge (Mass.): The MIT Press.

United Nations (1949), International Capital Movements during the Inter-War Period, Lake

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Wandschneider, Kirsten (2008) “The Stability of the Interwar Gold Exchange Standard: Did

Politics Matter?”, Journal of Economic History 68 (1), pp. 151-181.

Wolf, Holger C., and Tarik M. Yousef (2007), “Breaking the Fetters: Why did Countries Exit

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1928-December 1936”, Explorations in Economic History 45 (4), pp. 383-401.

 24

Table 1: Year of end of interwar gold standard (selected countries) *

Continent First phase

(1929-30)

Second phase

(1931-32)

Third phase

(1933)

Fourth phase

(1934-36)

Northern Europe Denmark

Estonia

Finland

Norway

Sweden

Western Europe UK Belgium

France

Netherlands

Central Europe Austria

Czechoslovakia

Germany

Switzerland

Eastern Europe Bulgaria

Hungary

Roumania

Danzig

Lithuania

Poland

Mediterranean Greece

Portugal

Yougoslavia

Italy

North America Canada USA

Central America

South America Argentina

Brazil

Paraguay

Uruguay

Asia India

Japan

Neth. East Indies (Indonesia)

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.

 25

Table 2: Definitions of core countries

Monetarydefinition

1900

Monetarydefinition

1930

Political definition

1930

Financialdefinition

1930

Flandreau and Jobst 2005 Flandreau and Jobst

adjusted

Winners of WW I and

major neutrals

Creditor countries

Austria-Hungary

Belgium Belgium Belgium Belgium

France France France France

Germany Germany

Italy Italy Italy

Netherlands Netherlands Netherlands Netherlands

Russia

Spain Spain Spain

Sweden Sweden Sweden

Switzerland Switzerland Switzerland Switzerland

UK UK UK UK

USA USA USA USA

 26

Table 3: Ordered Probit Model

EXRN

0: GS, 1: Exchange Controls with GS, 2: Devaluation,

Exit GS , 3: Devaluation, Exit GS and Exchange

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

 27

Convergence achieved after 8 iterations

QML (Huber/White) standard errors & covariance

Coefficient Std. Error z-Statistic Prob.

GDPPCLA 0.029855 0.010111 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 0.044440 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 3.493525 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 0.591743 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 3.115899 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

 28

DPT 1.203903 0.676593 1.779360 0.0752

DSW -2.141604 0.858774 -2.493793 0.0126

DAR 0.035912 0.645163 0.055664 0.9556

DAS -3.675287 0.820958 -4.476825 0.0000

DBR 1.590854 0.718505 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

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

 29

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

Null Hypothesis Summary:

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

 

 30

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 0.018125 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 0.089180 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

 31

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 3.339626 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 0.831339 0.358573 0.7199

DAS -4.810625 1.860203 -2.586075 0.0097

DBR 0.777147 1.317425 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

 32

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

Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

GDPPCLA -0.018001 0.009848

GGDPPCLA 0.173168 0.099047

 33

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