20080416             This Time is Different: A Panoramic  VIEW—OF 8 Centuries of Financial Crises* 
Carmen M. Reinhart, UNIVERSITÄT—Maryland and NBER 
Kenneth S. Rogoff, Harvard University and NBER 
Abstract 
This paper offers a “panoramic” analysis of 
13000101—13991231    THE—HISTORY—OF—financial crises dating FROM 
13000101—13991231    ENGLAND’S default to 
20080416             the CURRENT United States SUB—PRIME—FINANCIAL—CRISIS. 
Our study is based on a new dataset that spans all regions. 
It incorporates a number of important credit episodes seldom covered in the literature, including for example, 
defaults and restructurings in India and China. 
As the 1. paper employing this data, 
our aim is to illustrate some of the broad insights that can be gleaned from such a sweeping historical database. 
We find that 
serial default is a nearly universal phenomenon as countries struggle to transform themselves from emerging markets to advanced economies. 
Major default episodes are typically spaced some years (or decades) apart,
creating an illusion that “this time is different” among policymakers and investors. 
A RECENT example of the “this time is different” syndrome is 
the false belief that domestic debt is a novel feature of the modern financial landscape. 
We also confirm that 
crises frequently emanate from the financial centers with transmission through interest rate shocks and commodity price collapses. 
Thus, 
the RECENT US SUB—PRIME—FINANCIAL—CRISIS is hardly unique. 
Our data also documents other crises that often accompany default: including 
inflation, exchange rate crashes, banking crises, and currency debasements. 

JEL E6, F3, and N0 

* The authors are grateful to Vincent Reinhart, John Singleton, Arvind Subramanian, and seminar participants 
at Columbia and Harvard Universities for useful comments and suggestions and Ethan Ilzetzki, Fernando Im, 
and Vania Stavrakeva for excellent research assistance. 

I. Introduction 
The economics profession has an unfortunate tendency to view RECENT experience in the narrow window provided by standard datasets. 
With a few notable exceptions, 
19800000             -BEGIN IN crosscountry empirical studies on financial crises typically 
and 
crosscountry empirical studies on financial crises typically are limited in several other important respects.1 
Yet an event that is rare in a three decade span may not be all that rare when placed in a broader context. 
This paper introduces a comprehensive new historical database for studying international debt and banking crises, 
inflation, 
currency crashes and 
debasements. 
The data covers sixty-six countries in Africa, Asia, Europe, Latin America, North America, and Oceania. 
The range of variables encompasses, 
among many other dimensions, 
external and domestic debt, 
trade, 
GNP, 
inflation, 
exchange rates, 
interest rates, and 
commodity prices. 
The coverage spans 8 centuries, 
generally going back to the date of independence for most countries, and 
well into the colonial period for some. 
As we detail in an annotated appendix, 
the construction of our dataset has built heavily on the work of earlier scholars. 
However, 
it also includes a considerable amount of new material from diverse primary and secondary sources. 
In addition to a systematic dating of external debt and exchange rate crises, 
the appendix to this paper also catalogues dates for domestic inflation and banking crises. 
For the dating of sovereign defaults on domestic (mostly local currency) debt, see Reinhart and Rogoff (2008). 

The paper is organized as follows.
Section II summarizes highlights from a 1. VIEW—OF the extended dataset, with special reference to the current conjuncture. 
Among other things, we note that 
policymakers should not be overly cheered by the absence of major external defaults from 2003 to 2007, 
after the wave of defaults in the preceding two decades. 

 1 Among many important previous studies include work by Bordo, Eichengreen, Lindert, Morton and Taylor. 

Serial default remains the norm, with international waves of defaults typically separated by many years, if not decades. 

Many foreign investors and policymakers today seem lulled by the fact that 
many emerging market GOVERNMENTs have become less reliant on foreign currency external borrowing than in the RECENT past. 
Countries have instead been relying more on domestic currency debt issued in local markets. 
Yet, as we show in a companion paper, 
reliance on domestic debt is hardly new, and 
the view that domestic debt can be largely ignored in looking at external debt sustainability is hard to reconcile with the extensive historical experience.2 

Our dataset reveals that 
the phenomenon of serial default is a universal rite of passage through history for nearly all countries as they pass through the emerging market state of development.
This includes not only Latin America, but Asia, the MIDDLE—EAST and Europe. 
We also find that 
high inflation, currency crashes, and debasements often go handin-hand with default. 
Last, but not least, we find that 
historically, significant waves of increased CAPITAL mobility are often followed by a string of domestic banking crises. 

Section III of the paper gives a brief over VIEW—OF the sample and data. 
Section IV catalogues 
THE—HISTORY—OF—serial default on external debts, 
from England’s defaults in the MIDDLE—AGES[MITTEL–ALTER], 
to Spain’s 13 defaults from the 
1500s on, to 19000101—19991231     
defaults in Asia, 
Africa, and 
Latin America. 
Our database marks the years that default episodes are resolved as well as when they began, 
allowing us to look at the DURATION of default in addition to the FREQUENCY. 

Section V of the paper looks at the effect of global factors on sovereign default, 
including commodity prices and CAPITAL flows emanating from the center countries. 

 2 These issues are analyzed in detail in Reinhart and Rogoff (2008). 

We show how shocks emanating from the center countries can lead to financial crises worldwide. 
In this respect, 
20070000—20080000    THE—US—SUB—PRIME—FINANCIAL—CRISIS is hardly exceptional. 

Section VI shows that 
episodes of high inflation and currency debasement are just as much a universal right of passage as serial default. 
Section VII introduces 
a composite index that aggregates the “varieties of crises.” 
In the concluding section, we take up the issue of 
how countries can graduate from the perennial problem of serial default.
Will the early 21 Jh. prove different? 

Appendix A gives a brief synopsis of how the database was constructed, while 
Appendices I (macroeconomic series) and 
II (debt) list all the variables in the database and 
provide their sources on a period-by-period and country-by-country basis. 

II. 1. Insights: 
The Big Picture 
What are some basic insights one gains from this panoramic  VIEW—OF THE—HISTORY—OF—FINANCIAL crises? 
We begin by discussing 
sovereign default on external debt (i.e., 
a GOVERNMENT default on its own external debt or 
private sector debts that were publicly guaranteed.) 

The 1. observation is that 
for the world as a whole (or 
at least the more than 90 % of global GDP represented by our dataset), 
THE CURRENT PERIOD can be seen as a typical lull that follows large global financial crises. 

18000000-20060000    Figure 1 plots for the years 
18000000-20060000    (where our dataset is most complete), 
18000000-20060000    the %-AGE of all independent countries in a state of default or restructuring 
18000000-20060000    DURING ANY GIVEN YEAR. 
18000000-20060000    Aside from the current lull, 
18000000-20060000    one fact that jumps out from the figure are 
18000000-20060000    the long periods where a high %age of all countries are in a state of default or restructuring. 
18000000-20060000    Indeed, 
18000000-20060000    there are 5 pronounced peaks or default cycles in the figure. 


The 1. is 
DURING the NAPOLEONic War. 
The 2. runs 
from the 1820s through the late 1840s, when, 
at times, 
nearly half the countries in the world were in default (
including all of Latin America). 
The 3. episode begins IN THE EARLY 1870s and 
lasts for 2 decades. 

18000000—20060000   Figure 1 
18000000—20060000   Sovereign External Debt: % of Countries in Default or Restructuring 

0 
10 
20 
30 
40 
50 
60 
% of countries 
Year 

Sources: Lindert and Morton (1989), 
Macdonald (2003), 
Purcell and Kaufman (1993), 
Reinhart, Rogoff, and Savastano (2003), 
Suter (1992), and 
Standard and Poor’s (various years). 
Notes: 
Sample size includes all countries, out of a total of sixty six listed in Table 1, that were independent states in the given year. 


The 4. episode begins in the Great Depression of the 1930s and 
extends through the 
early 1950s, 
when again nearly half of all countries stood in default.3 
The most RECENT default cycle encompasses 
the emerging market debt crises of the 
1980s and 
1990s. 

Indeed, 
when one weights countries by their share of global GDP, 
as in Figure 2 below, 
the current lull stands out even more against the preceding century. 

 3 Kindleberger (1988) is among the few scholars who emphasize that the 
1950s can be viewed as a FINANCIAL—CRISIS era. 

Only the two 
19140000—19180000    WWI—DECADES—BEFORE- the halcyon days of the gold standard—
exhibited tranquility anywhere close to that of the 
20030000—20070000    period.4 
Looking forward, once cannot fail to note that 
whereas one and two decade lulls in defaults are not at all uncommon, 
each lull has invariably been followed by a new wave of default. 

Figure 2 is interesting because it shows 
             WWII the years after 
as marking the peak of by far the largest default era in modern world history, 
with countries representing almost 40 % of global GDP in a state of default or rescheduling. 
This is partly a result of new defaults produced by the war, but 
also due to the fact that many countries never emerged from the defaults surrounding the Great Depression of the 1930s.5 
By the same token, 
the NAPOLEONic War defaults become as important as any other period. 
Outside WORLD WAR II, only the peak of the 
1980s debt crisis nears the levels of the early 
1800’s. 

As we shall see when we tabulate individual country experiences in Section IV, 
serial default on external debt—that is, repeated sovereign default—
is the norm throughout every region in the world, 
even including Asia and Europe. 

4 This comparison weights defaulting countries by share of world income. 
On an unweighted basis 
(so, for example, the poorest countries in Africa and South Asia receive the same weight as Brazil or the United States), 
the late 
1960s —UNTIL 
1982 had an even lower %age of independent countries in default. 
5 Kindleberger (1989) emphasizes the prevalence of default after
             WWII , though he does not provide quantification. 


Figure 2 
45 
Sovereign External Debt: 
18000000—20060000    Countries in Default Weighted by Their Share of World Income 
40 

Year 

Sources: Lindert and Morton (1989), 
Macdonald (2003), 
Maddison (2003), 
Purcell and Kaufman (1993), 
Reinhart, Rogoff, and Savastano (2003), 
Suter (1992), and 
Standard and Poor’s (various years). 
Notes: Sample size includes all countries, out of a total of sixty six listed in Table 1, that were independent 
states in the given year. 
Three sets of GDP weights are used, 
1913 weights for the period 1800–1913, 
1990 for the period 1914–1990, and finally 
2003 weights for the period 19910000—2006. 


We have already seen from Figure 2 that global conflagration can be a huge factor in generating waves of defaults. 
Our extensive new dataset also confirms 
the prevailing view among economists that 
global economic factors, including commodity prices and center country interest rates, play a major role in precipitating sovereign debt crises.6 

We take up this issue in Section V. 
Making use of a range of real global commodity price indices, 
we show that 
1800 TO 2006, OVER THE PERIOD 
peaks and troughs in commodity price cycles appear to be leading indicators of peaks and troughs in the CAPITAL flow cycle, 
with troughs typically resulting in multiple defaults. 

6 See Bulow and Rogoff (1990), and Mauro, Sussman and Yafeh (2006). 

0 
5 
10 
15 
20 
25 
30 
35 
% of world Income 
All countries in sample Excluding China 

An even stronger regularity found in the literature on modern financial crises (e.g., 
Kaminsky and Reinhart, 1999 and Reinhart and Rogoff, 2008b) is that 
countries experiencing sudden large CAPITAL inflows are at a high risk of having a debt crisis. 
The preliminary evidence here suggests the same to be true over a much broader sweep of history,
 with surges in CAPITAL inflows often preceding external debt crises at the country, 
regional, and global level 
since 1800 if not before. 

Also consonant with the modern theory of crises is 
the striking correlation between freer CAPITAL mobility and the incidence of banking crises, 
as illustrated in Figure 3. 
Periods of high international CAPITAL mobility have repeatedly produced international banking crises, 
not only famously as they did in the 1990s, but 
historically. 
The figure plots a three-year moving average of the share of all countries experiencing banking crises on the right scale. 
On the left scale, we employ our favored index of CAPITAL mobility, 
due 
to Obstfeld and Taylor (2003), 
updated and backcast using their same design principle, 
to cover our full sample period. 
While the Obstfeld–Taylor index may have its limitations, 
we feel it nevertheless provides a concise summary of complicated forces by emphasizing DE—FACTO CAPITAL mobility based on actual flows. 

The dating of banking crises episodes is discussed in detail in the Appendix. 
What separates this study from previous efforts (that we are aware of) is that 
for so many countries, our dating of crises extends back to far before the much-studied modern post–World War II era; 

specifically we start in 1800. 
(See Table A.3 for details.) 
Our work was greatly simplified back to 1880 by the careful study of Bordo, et al. (2001)—but 
for the earlier period we had to resort to archeological work. 
The earliest advanced economy banking crisis in our sample is Denmark in 1813; 

the two earliest ones we clock in emerging markets are India, 1863 and 
Peru 10 years later. 


Figure 3 

CAPITAL Mobility and the Incidence of Banking Crisis: All Countries, 
18000000—2007 
0 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
1 
Index 
0 
5 
10 
15 
20 
25 
30 
35 
% 
1860 
CAPITAL Mobility 
(left scale) 
Share of Countries 
in Banking Crisis, 3-year 
Sum 
(right scale) 
1914 
1945 
19801825 
1918 
High 
Low 
1800 1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 

Sources: Bordo et al. (2001), Caprio et al. (2005), Kaminsky and Reinhart (1999), Obstfeld and Taylor 
(2004), and these authors. 
Notes: As with external debt crises, sample size includes all countries, out of a total of sixty six listed in 
Table 1 that were independent states in the given year. On the right scale, we updated our favorite index of 
CAPITAL mobility, admittedly arbitrary, but a concise summary of complicated forces. The smooth red line 
shows the judgmental index of the extent of CAPITAL mobility given by Obstfeld and Taylor (2003), backcast 
from 1800 to 1859 using their same design principle. 

(The aforementioned Peruvian case comes from a little-known 
1957 book published in Lima by Carlos Camprubi Alcazar entitled 
18600000—18790000    Historia de los Bancos en el Peru, 
There are many more such case studies in our references that were a vital source of information on banking crises.) 

As noted, our database includes long time series on domestic public debt.7 
Because historical data on domestic debt is so difficult to come by, 
it has been ignored in the empirical studies on debt and inflation in developing countries. 
Indeed, 
many generally knowledgeable observers have argued that 
the RECENT shift by many emerging market GOVERNMENTs from external to domestic bond issues is revolutionary and unprecedented.8 

7 For most emerging market economies, over MOST —OF—THE—TIME PERIOD considered,
domestically issued debt was in local currency and held principally by local residents. 
External debt, on the other hand, was typically in foreign currency, and held by foreign residents. 



As we shall argue, 
nothing could be further from the truth, 
with implications for today’s markets and for historical analyses of debt and inflation. 

UNTIL VERY RECENTLY, domestic debt was not on the radar screen of the multilateral institutions. 
UNTIL VERY RECENTLY, Neither THE—INTERNATIONAL Monetary Fund nor the World Bank systematically collected such data. 
In fact, 
cross-country historical time series on domestically issued debt are also absent from private data collections. 
Reinhart, Rogoff and Savastano (2003), 
with extensive help from IMF staff and country sources, put together 
an annual series going back to 1990 for a limited number of emerging market countries.9 

The topic of domestic debt is so important, and 
the implications for existing empirical studies on inflation and external default are so profound, that 
we have broken out our data analysis into an independent companion piece (Reinhart and Rogoff, 2008). 
Here, we focus on a few major points. The 1. is that 
contrary to much contemporary opinion, 
domestic debt constituted an important part of GOVERNMENT debt in most countries, 
including emerging markets, 
over most of their existence. 
Figure 4 plots 
domestic debt as a share of total public debt over 1900 to 2006. 

For our entire sample of 66 countries, 
domestically issued debt averages more than 50 % of total debt for most of the period. 
(This figure is an unweighted average of the individual country ratios.)
 Even for Latin America, the domestic debt share is typically over 30 % and has been at times over 50 %. 

Furthermore, 
contrary to the received wisdom, 
this data reveal that 
a very important share of domestic debt—even in emerging markets— was long-term maturity 

8 
20070400             See the IMF Global Financial Stability Report, ; 
m
20070400             any private investment-bank reports also trumpet the rise of domestic debt as a harbinger of stability.
9 Since then, Jeanne and Guscina (2008) have extended them both back to 1980 and up to 2005. 

Figure 4 

Domestic Public Debt as a Share of Total Debt, 
19000000—2006 

0.00 
0.10 
0.20 
0.30 
0.40 
0.50 
0.60 
0.70 
0.80 
0.90 
1.00 
Share of which North America 
All countries of which Latin America 
Sources: THE—LEAGUE—OF—Nations, the UNITED—NATIONS, and others sources listed in Appendix II. 
(Reinhart and Rogoff 2008a). In that paper, we also present a variety of evidence to 
support the view that, at the very least, domestic debt does not appear to be junior to 
external debt, even factoring in a GOVERNMENT’s ability to default via inflation. 

As payments on domestic debt must come from the same REVENUE stream as 
payments on foreign debt, the implication is that the extent of domestic debt can be quite 
important in assessing the sustainability of a country’s external debt payments. Yet, 
because it has not been possible to obtain extensive historical time series on domestic debt 
until now, most empirical RESEARCHERS have ignored the issue entirely. Reinhart and Rogoff 
find that the same issue arises in the analysis of high inflation; 

most of the empirical 
literature since Cagan’s classic (1956) paper has focused on the “seignorage” gains from 
inflation, which are entirely levered off the real money base. Yet, the GOVERNMENT’s gain 
to unexpected inflation often derives at least as much from CAPITAL losses that are 
inflicted on holders of long-term GOVERNMENT bonds. Figure 5 on inflation and external 


default (19000000—2006) illustrates the striking correlation between the share of countries in 
default on debt at one point and the number of countries experiencing high inflation (which 
we define to be inflation over 20 % PER—ANNUM). Since
             WWII , inflation and 
default have gone hand-in-hand. 

Figure 5 

 Inflation and External Default: 19000000—2006 

Correlations: 

0 
5 
10 
15 
20 
25 
30 
35 
40 
45 
% of countries 
Share of countries in 
default 
Share of countries with 
inflation above 20 % 
19000000—2006 0.39 
excluding the Great Depression 0.60 
19400000—2006 0.75 
Year 

Sources: For share of countries in default, see Figure 1; 

for high inflation episodes, see Appendix I. 
Notes: Both the inflation and default probabilities are simple unweighted averages. 

The forgotten HISTORY—OF—DOMESTIC DEBT has important lessons for the present. 
As we have already noted, 
most INVESTMENT—BANKs, not to mention official bodies such as THE—INTERNATIONAL Monetary Fund and the World Bank, 
have argued that 
even though total public debt remains quite high today (early 2008) in many emerging markets, 
the risk of default on external debt has dropped dramatically, 
especially as the share of external debt has fallen. 
This conclusion seems to be built on the faulty premise that 
countries will treat domestic debt as junior, 
bullying domestics into accepting lower repayments or 
simply defaulting via inflation. 


THE HISTORICAL RECORD, however, suggests that 
a high ratio of domestic to external debt in overall public debt is cold comfort to external debt holders.
Default probabilities probably depend much more on the overall level of debt. 
Reinhart and Rogoff (2008b) discuss the interesting example of India, who 
19580000             -IN India rescheduled its foreign debts when it stood at only 1/4 % of REVENUes. 
19580000             -IN The sums were so minor that the event did not draw great attention in the Western press. 
The explanation, as it turns out, 
is that 
19580000             -IN AT—THIS—TIME India had a significant claim on REVENUE from the service of domestic debt 
(in effect the total debt-to REVENUE ratio was 4.4. 
To summarize, 
many investors appear to be justifying still relatively low external debt credit spreads because 
“This time is different” and 
emerging market GOVERNMENTs are NOW relying more on domestic public debt. 
If so, they are deeply mistaken. 

Another noteworthy insight from the “panoramic view” is than that 
IN THE POST–WORLD WAR II PERIOD 
the median duration of default spells is one-half the length of what it was 
18000000–19450000    DURING 
(3 years versus 6 years, 
as shown in Figure 6). 

The charitable interpretation of this fact is that 
SINCE THE BYGONE DAYS OF GUN-BOAT DIPLOMACY crisis resolution mechanisms have improved . 
After all, 
19360000             -IN Newfoundland lost nothing less than her sovereignty 
19360000             -IN when Newfoundland defaulted on its external debts and 
19360000             -IN ultimately Newfoundland became a Canadian province; 
among others, 
Egypt, became British “protectorates” following their defaults. 
A more cynical explanation points to the possibility that, 
when bail-outs are facilitated by the likes of THE—INTERNATIONAL Monetary Fund[IMF,FMI], 
creditors are willing to cut more slack to their serial-defaulting clients. 
The fact remains that, 
as Bordo and Eichengreen (2001) observe, 
IN THE MORE RECENT PERIOD the number of years separating default episodes is much lower. 
Once debt is restructured, countries are quick to releverage 
(see Reinhart, Rogoff, and Savastano (2003) for empirical evidence on this pattern). 

Figure 6 
Duration of Default Episodes: 18000000—2006 
frequency of occurrence, % 
0 
5 
10 
15 
20 
18000000—1945 
127 episodes 
Median is 6 years 
19460000—2006 
169 episodes 
Median is 3 years 
1 4 7 101316192225283134374043464952555861646770 
Years in Default 

Sources: Lindert and Morton (1989), Macdonald (2003), Purcell and Kaufman (1993), Reinhart, Rogoff, and 
Savastano (2003), Suter (1992), Standard and Poor’s (various years) and authors’ calculations. 
Notes: The duration of a default spell is the number of years from the year of default to the year of 
resolution, be it through restructuring, repayment, or debt forgiveness. 
The Kolmogorov–Smirnoff test for comparing the equality of two distributions rejects the null hypothesis of equal distributions at the 1 % significance level. 

III. 
A Global Database on Financial Crises with a Long-term View 
In this section, we provide a slim outline of the character of the sample and the building blocks of this database. 
Extensive detail is provided in three appendices. 
Country coverage 

 Table 1 lists the 66 countries in our sample. 
Importantly, we include a large number or Asian and African economies, whereas 
previous studies of the same era typically included at most a couple of each. 
Overall, 
our dataset includes thirteen African countries, 


12 Asian countries, 
nineteen European countries, 
eighteen Latin American countries, plus 
North America and Oceana. 

As the final column in Table 1 illustrates,
our sample of 66 countries indeed accounts for about 90 % of world GDP. 
Of course, 
many of these countries, 
particularly those in Africa and Asia, have become independent nations only relatively 
RECENTLY (column 2). 
These RECENTLY independent countries have not been exposed to the risk of default for nearly as long as, say, 
the Latin American countries, and 
we will have to calibrate our inter-country comparisons accordingly.10 

Table 1 flags which in our sample 
countries may be considered DEFAULT VIRGINs, 
at least in the narrow sense that 
they have never failed to meet their debt repayment or rescheduled. 
ONE CONSPICUOUS GROUPING OF COUNTRIES INCLUDES THE HIGH-INCOME ANGLOPHONE NATIONS, 
THE UNITED STATES, 
CANADA, 
AUSTRALIA, AND 
NEW ZEALAND. (
IN EARLIER ERAS THE MOTHER COUNTRY, ENGLAND, DEFAULTED 
AS WE SHALL SEE.) 
Also included are 
all of the Scandinavian countries, 
Norway, 
Sweden, 
Finland and 
Denmark. 
Also 
in Europe, there is Belgium. 
In Asia, there is 
Hong Kong, 
Malaysia, 
Singapore, 
Taiwan, 
Thailand and 
Korea. 
Admittedly, 
DURING THE LAST 1990S DEBT CRISIS the latter two countries, especially, managed to avoid default only through massive International Monetary Fund loan packages 
and otherwise 
DURING THE LAST 1990S DEBT CRISIS suffered much of the same trauma as a typical defaulting country. 

Also, 
of default-free countries,
             WWII BEFORE THE END OF only Thailand existed as an independent state 
For others, the potential opportunity for default has been relatively short. 

10 Our sample excludes many of the world’s poorest countries, 
who by and large cannot borrow meaningful amounts from private sector lenders, and 
who have virtually all effectively defaulted even on heavily subsidized GOVERNMENT-to-GOVERNMENT loans. 
This is an interesting subject for another study, but
here we are mainly interested in financial flows that, at least in the 1. instance, had a substantial market element. 


Lastly, 
several of the sovereign default virgins qualify as such only because
we are excluding events such as 
the sovereign default virgin notably the United States, 
qualify as such only because 
19330000             IN we are excluding events such as lowering the gold content of the currency , or 
18000101—18991231    in the  CIVIL—WAR the suspension of convertibility . 
Finally, 
there is one country from Africa, Mauritius, which has NEVER defaulted or restructured. 
It is notable that 
THE NON-DEFAULTERS, BY AND LARGE, ARE ALL HUGELY SUCCESSFUL GROWTH STORIES. 
THIS begs the QUESTION: 
Do high growth rates help avert default, or 
does averting default beget high growth rates? 

Table 1 also flags 
which countries in our sample have not defaulted on their external debts, 
at least in the narrow sense that 
they have not outright failed to meet their debt repayment on schedule in an important way on even one occasion. 
This is an issue we will return to in Section IV. 

Dates and Frequency of Coverage 

Appendix A describes the data in detail, while 
Appendices I and II provide 
specifics on coverage and sources on a country-by-country and period-by-period basis for all the time series. 
All the data is annual
—this includes the crises dates. 
Below we provide a list of the variables used in this study. 
Crises-related variables 
Debt 

Our debt data covers central GOVERNMENT public debt—external and domestic. 
The latter is decomposed into short-term and long—term debt in many, but not all, cases. 
For a large number of countries the time series go back to the 1800s, if not earlier. 
However, 
starting in 1913, the coverage for our sample becomes much more comprehensive. 
Debt is perhaps the most novel feature of the dataset. 


Table 1. 
Countries, Regions, and World GDP 
Country (
An asterisk denotes no sovereign Year of Independence 
Share of World Real GDP default or rescheduling 1990 International Geary–Khamis US $ history) 
1913 1990 

Africa 
1962 Algeria 0.23 0.27 
1975 Angola 0.00 0.03 
1960 Central Africa Republic 0.00 0.01 
1960 Cote D’Ivoire 0.00 0.06 
1831 Egypt 0.40 0.53 
1963 Kenya 0.00 0.10 
1968 Mauritius * 0.00 0.03 
1956 Morocco 0.13 0.24 
1960 Nigeria 0.00 0.40 
1910 SOUTH—AFRICA 0.36 0.54 
1591/1957 Tunisia 0.06 0.10 
1964 Zambia 0.00 0.02 
1965 Zimbabwe 0.00 0.05 

Asia 
13680000             China 8.80 7.70 
Hong Kong * 

1947 India 7.47 4.05 
1949 Indonesia 1.65 1.66 
1590 Japan 2.62 8.57 
1945 Korea * 0.34 1.38 
1957 Malaysia * 0.10 0.33 
1948 Myanmar 0.31 0.11 
1947 Philippines 0.34 0.53 
1965 Singapore * 0.02 0.16 
1949 Taiwan * 0.09 0.74 
1769 Thailand * 0.27 0.94 

Europe 
1282 Austria 0.86 0.48 
1830 Belgium * 1.18 0.63 
0980 Denmark * 0.43 0.35 
1917 Finland * 0.23 0.31 
0943 France 5.29 3.79 
1618 Germany 8.68 4.67 
1829 Greece 0.32 0.37 
1918 Hungary 0.60 0.25 
1569 ITALY 3.49 3.42 
1581 NETHERLANDS[NIEDERLANDE]* 0.91 0.95 
1905 Norway * 0.22 0.29 
1918 Poland 1.70 0.72 
1139 Portugal 0.27 0.40 
1878 Romania 0.80 0.30 
1457 Russia 8.50 4.25 
1476 Spain 1.52 1.75 
1523 Sweden 0.64 0.56 
1453Turkey  0.67 1.13 
1066 United Kingdom * 8.22 3.49 

Sources: 
Correlates of War (2007), 
Maddison (2004). 
Notes: 
An asterisk denotes no sovereign external default or rescheduling history. 

Table 1 (concluded) 
Countries, Regions, and World GDP 

Year of Independence 
Share of World Real GDP 
1990 International 
Geary–Khamis US $ 
1913 1990 
Latin America 
1816 Argentina 1.06 0.78 
1825 Bolivia 0.00 0.05 
1822 Brazil 0.70 2.74 
1818 Chile 0.38 0.31 
1819 Colombia 0.23 0.59 
18210000             Costa Rica 0.00 0.05 
1845 Dominican Republic 0.00 0.06 
1830 Ecuador 0.00 0.15 
18210000             El Salvador 0.00 0.04 
18210000             Guatemala 0.00 0.11 
18210000             Honduras 0.00 0.03 
18210000             Mexico 0.95 1.91 
18210000             Nicaragua 0.00 0.02 
1903 Panama 0.00 0.04 
1811 Paraguay 0.00 0.05 
18210000             Peru 0.16 0.24 
1811 Uruguay 0.14 0.07 
1830 VENEZUELA 0.12 0.59 
North America 
1867 Canada * 1.28 1.94 
1783 United States * 18.93 21.41 
Oceania 
1901 Australia * 0.91 1.07 
1907 New Zealand * 0.21 0.17 

Total Sample-66 countries 

93.04 89.24 
Sources: Correlates of War (2007), Maddison (2003). 

Prices 
the most comprehensive in our set of variables, is 
The data on prices going back to 
the early MIDDLE—AGES[MITTEL–ALTER] for Europe (including Turkey) and 
Asia. 
For the New World (the United States and some of the larger Latin American countries), 
these data go back to the 
1700s. 
Where possible, we use consumer prices (or cost—of—living) indices. 
On the basis of this data, we construct the inflation series that allow us to date inflation crises. 


Exchange rates 

Exchange rates in this database come in two forms: 
For the pre—1600s period, 
exchange rate data are constructed from the silver content of the currency, 
for which we have data 
through the mid—1800s for 11 countries; 
16000000             S, BEGINNING IN THE EARLY the Course of the Exchange in Amsterdam established actual market—based exchange rates, 
16000000             S, BEGINNING IN THE EARLY marking the beginning of modern exchange rates, 
for which we have a far more comprehensive coverage. 
As in Reinhart and Rogoff (2004), 
we use market—based exchange rates, where possible. 
These data underpin our dating of currency crashes. 
Varieties of Crises: 
Banking, and 
external and 
domestic default 

These time series are dichotomous variables that 
take on the value of one if it is a crisis year and 
zero otherwise and 
are standard in the literature on crisis. 
Particulars of the criteria used to define a banking crisis or an external or domestic default crisis are given in Appendix A. 

GOVERNMENT Finances, Trade, and GDP 

Our dataset incorporates data on central GOVERNMENT expenditures and REVENUes. 
On the whole, 
these provide some of the most reliable data on country size and economic strength 
IN THE ERA PRIOR TO DEVELOPMENT OF CONVENTIONAL NATIONAL INCOME. 
Furthermore, 
these data are available for many countries, 
including African countries 
(where data is relatively scarce), 
throughout most of their colonial history. 

The trade data (exports and imports) are next in reliability to the fiscal data. 
Like their fiscal counterparts, 
these data offer longer history than THE—NATIONAL accounts 
that are of a relatively more modern vintage. 

Having reasonably accurate output data is thus of enormous help in calibrating the severity of crises. 
Unfortunately, 
20. Jh. PRIOR TO THE 20. Jh. GDP data for most countries are quite uneven. 


For many emerging markets, data are only available sporadically and 
at long intervals, 
which is especially limiting in trying to assess the impact of crises. 
Fortunately, 
we do have reliable estimates for a sufficient number of countries 
so as to be able to draw broad conclusions and, of course, 
we can use GOVERNMENT REVENUE and 
trade data to supplement these estimates, 
as discussed in Appendix A. 

The primary use of the REVENUE, 
exports, and 
GDP series 
in our analysis is to scale debt, that is 
to construct the standard debt—to—REVENUes, etc., ratios. 
CAPITAL Flows 

PRE–WORLD WAR II gross CAPITAL flows are measured by data on debentures. 
Where possible, 
we also reconstruct net flows by taking gross new issuance minus repayment, 
taking into account partial defaults and 
negotiated interest rate reductions 
that often take place DURING rescheduling episodes. 
For the post—war, 
we rely on the actual balance—of—payments data,
as reported by the multilateral institutions or the country sources. 
Financial center data and global commodity prices 

In modern times, 
emerging market financial crises have often been triggered by vents at the center, 
as Bulow and Rogoff (1990) and others have argued. 
POST—1800 To capture developments in financial centers , 
we include: 
measures of short— and long—term interest rates, 
real GDP, and 
current account balances. 
DURING most of the 19. Jh., Britain was the global financial center. 
Since             WWII , it has been the United States, but 
DURING the long transition period from British to U.S. financial hegemony both countries were influential . 
Commodity prices have long been thought to be another important global driver of the depression–prosperity cycles in modern times. 
Our historical dataset combines several different indices of commodity prices, with 
17900000             the oldest dating back to . 

IV. 
13500000—20060000    Serial Default 
When one looks carefully, 
virtually all countries have defaulted at least once and 
many several times on external debt 
DURING their emerging market economy phase, 
a period that typically takes at least one or two centuries. 
15000000—17990000    Early Default, 
Today’s emerging markets can hardly claim credit for inventing serial default. 
Table 2 lists the number of defaults, including 
13000000-17990000    default years, between for a number of now rich European countries (
Austria, 
France, 
Germany, 
Portugal, and 
Spain). 
As the table illustrates, 
today’s emerging market countries did not invent serial default. 
Rather, a number of today’s now—wealthy countries, had similar problems when they were “emerging markets.” 

Table 2. 
13000000—17990000    The Early External Defaults: Europe, 
13000000—17990000    Country Years of default Number of defaults 
17960000             Austria 1 External Default
13400000             , England External Default
14720000             , England External Default
15940000             * England External Default2* 
15580000             , France External Default
16240000             , France External Default
16480000             France External Default
16610000             , France External Default
17010000             , France External Default
17150000             France External Default 8 
17700000             France External Default, 
17880000             France External Default
16830000             Germany (Prussia) 1 External Default
15600000             Portugal 1 External Default
15570000             , Spain External Default
15750000             Spain External Default
15960000             Spain External Default, 6 
16070000             Spain External Default, 
16270000             Spain External Default, 
16470000             Spain External Default

Sources: MacDonald (2006), Reinhart, Rogoff and Savastano (2003) and sources cited therein. 
The “*” for England denotes our uncertainty AT—THIS—TIME about whether its default was on domestic or external debt. 

Spain’s defaults establish a record that remains as yet unbroken. 
Indeed,
18000101—18991231     in the alone, Spain managed to default 7 times
after having defaulted six times in the preceding three centuries. 


18000101—18991231    With its later string of defaults, Spain took the mantle for most defaults from 
1500 and 1800. between France, which had abrogated its debt obligations on 8 occasions 
Because the French monarchs had a habit of executing major domestic creditors DURING external debt default episodes 
(an early form of “debt restructuring”), 
the population came to refer to these episodes as “bloodletting.”11 
17680000—1774,The French Finance Minister Abbe Terray, who served from  
even opined that 
GOVERNMENTs should default at least once every 100 years in order to restore equilibrium 
(Winkler, PAGINA 29).12 

Remarkably, however, despite all the trauma the country experienced in the wake of THE—FRENCH—REVOLUTION and 
the NAPOLEONic Wars, 
France eventually managed to emerge from its status of serial default. 
18120000             AFTER France did not default in the nineteenth or 
19000101—19991231     nor (so far, anyway) in the 
20000101—20991231    . 
There is, however, some debate as to whether France and others defaulted on a portion of their World War I debts to the United States. 13 
1800 UP TO IN THE PERIOD Austria and 
1800 UP TO IN THE PERIOD Portugal defaulted only once 
, but then 
DURING the 19. Jh., Austria defaulted a handful of times 
DURING the 19. Jh., Portugal defaulted a handful of times 
as we shall see. 

England, however, is perhaps an even earlier graduate. 
13400000             -IN Edward III, of Britain, defaulted on debt to Italian lenders 
(see, for example, MacDonald, 2007), 
13400000             -IN AFTER a failed invasion of France that set off the Hundred Years’ War. 
13400000             A CENTURY LATER, Henry VIII, in addition to engaging in an epic debasement of the currency, 
seized all the Catholic Church’s vast lands. 
While not strictly a bond default, 
such seizures, 
often accompanied by executions, 
qualify as reneging on financial obligations. 

11 See Reinhart, Rogoff and Savastano (2003) who thank Harald James for this observation. 
12 One wonders if 
Thomas Jefferson read those words, 
in that he subsequently held that 
“the tree of liberty must be refreshed from time to time with the blood of patriots and tyrants.” 
13 See Lloyd (1934). 

18000000—20060000    Sovereign Defaults, 
18000101—18991231    in the STARTING , the combination of the development of international CAPITAL markets together with 
18000101—18991231    in the the emergence of a number of new nation states, led to an EXPLOSION—IN—international defaults. 
Table 3 lists 
18000101—18991231    default and rescheduling episodes in Africa, 
Europe and 
Latin America. 
We include debt reschedulings,
 which THE—INTERNATIONAL finance theory literature rightly categorizes as negotiated partial defaults 
(Bulow and Rogoff, 1989). 
We briefly digress to explain this decision, which is fundamental to understanding many international debt crisis episodes. 

Reschedulings constitute partial default for two reasons. 
The 1. reason, of course, is that 
debt reschedulings often involve reducing interest rates, if not principle. 
Second, and perhaps more importantly, 
international debt reschedulings typically saddle investors with illiquid assets that may not pay off for decades. 
This illiquidity is a huge cost to investors, 
forcing them to hold a risky asset, often with compensation far below market. 
It is true that 
in some cases,
 investors that held defaulted sovereign debt for a sufficient number of years
—sometimes decades—
have often yielded a return similar to investing in relatively riskless financial center bonds 
(U.K. or later U.S.) 
over THE—SAME—PERIOD. 
Indeed, 
a number of papers have been written showing precisely such calculations 
(e.g., Mauro, Sussman and Yaffa, 2006). 

While interesting, 
it is important to underscore the fact that 
the right benchmark is the return on high—risk illiquid assets, not highly liquid low—risk assets. 
It is no coincidence that 
in the wake of THE—US—SUB—PRIME mortgage debt crisis of 2007, 
SUB—PRIME debt sells at steep discount relative to the expected value of future repayments. 
Investors rightly believe that 
if they could pull out their money, 
they could earn a much higher return elsewhere in the economy 
provided they are willing to take illiquid positions with substantial risk. 

And of course they are right. 
Investing in risky illiquid assets is precisely how venture CAPITAL and private equity, not to mention university endowments, have succeeded 
(until now) in earning enormous returns. 
By contrast, 
debt reschedulings at negotiated below—market interest rates give the creditor risk with none of the upside of say, a venture CAPITAL investment. 
Thus the distinction between debt reschedulings—negotiated partial defaults—and outright defaults 
(which typically end in partial repayment) is not a sharp one. 

Table 3 also lists each country’s year of independence. 
Most of Africa and Asia was colonized 
DURING this period, 
allowing Latin America and Europe a substantial head start. 
The only African countries to default 
DURING this period were Egypt (1876) and 
Algeria (1867). 
Austria defaulted a remarkable 5 times, 
albeit not quite so prolific as Spain.. 
Greece, which gained its independence only in 1829, 
made up for lost time by defaulting four times. 
Default was similarly rampant throughout the Latin American region, with 
VENEZUELA defaulting six times, and 
Costa Rica, 
Honduras, 
Colombia and 
the Dominican Republic 
each defaulting four times. 

 Looking down the columns of Table 3 also gives us a 1. glimpse at the clustering 
of defaults across regions and internationally. Note that a number of countries in Europe 
defaulted DURING or just after the NAPOLEONic wars, while many countries in both Latin 
America (plus their mother country Spain) defaulted DURING the 1820s. Most of these 
defaults are associated with Latin America’s wars of independence. Although none of the 
subsequent clusterings is quite so pronounced in terms of number of countries, there are 
notable global default episodes DURING the late 1860s up to the mid—1870s, and again 
starting in the mid—1880s through the early 1890s. 
We will later look at this clustering a bit 
more systematically. 


Table 3. 
External Default and Rescheduling: 
Africa, 
Europe, and 
Latin America, 
18000101—18991231    

Country/date of Dates 
independence 1 
18000000—1824 18250000—1849 18500000—1874 18750000—1899 

Africa 
Egypt, 1831 1876 
Tunisia 1867 
Europe 
Austria–Hungary 1802, 1805, 1811, 1868 
1816France 1812 
Germany 
Hesse 1814 
Prussia 1807, 1813 
Schleswig– 
Holstein 1850 
Westphalia 1812 
Greece, 1829 1826, 1843 1860 1893 
NETHERLANDS[NIEDERLANDE]1814 
Portugal 1828, 1837, 1841, 1852 1890 
1845 
Russia 1839 1885 
Spain 1809, 1820 1831, 1834 1851, 1867, 1872 1882 
Sweden 1812 
Turkey 1876 
Latin America 
Argentina, 1816 1827 1890 
Bolivia, 1825 1875 
Brazil, 1822 1898 
Chile, 1818 1826 1880 
Colombia, 1819 1826 1850, 1873 1880 
Costa Rica, 1825 1828 1874 1895 
Dominican 1872 1892, 1897, 1899 
Republic, 1845Ecuador, 1830 1826 1868 1894 
El Salvador, 1821 1828 1898 
Guatemala, 1821 1828 1876, 1894, 1899 
Honduras, 1821 1828 1873 
Mexico, 1821 1827, 1833, 1844 1866 1898 
Nicaragua, 1821 1828 1894 
Paraguay, 1811 1874 1892 
Peru, 1821 1826 1876 
Uruguay, 1811 1876, 1891 
VENEZUELA, 1830 1826, 1848 1860, 1865 1892, 1898 

1 The dates are shown for those countries that became independent DURING the 19. Jh.. 
Sources: Standard and Poor’s, Purcell and Kaufman (1993), Reinhart, Rogoff and Savastano (2003) and sources 
cited therein. 



Next we turn to the 
19000101—19991231    . 
Table 4 shows defaults in Africa and Asia, 
including among the many newly colonized countries. Nigeria, despite its oil riches, has 
defaulted a stunning five times since achieving independence in 1960, more than any other 
country over THE—SAME—PERIOD. Indonesia has also defaulted four times. Morocco, counting 
its 1. default in 1903 DURING an earlier era of independence, also defaulted four times in 
the 19000101—19991231    . India prides itself on escaping the 1990s Asian crisis (thanks to 
massive CAPITAL controls and financial repression). In point of fact, it was forced to 
reschedule its external debt three times since independence, albeit not since 1972. While 
China did not default DURING its communist era, it did default on external debt in both 1921 
and 1939. 

Thus, as Table 4 illustrates, the notion that countries outside Latin American and 
low—income Europe were the only ones to default DURING the 19000101—19991231     is an 
exaggeration, to say the least. 

Table 5 looks at Latin America and Europe, regions where, with only a few 
exceptions, countries were independent throughout the entire 19000101—19991231    . Again, as 
in the earlier tables, we see that country defaults tend to come in clusters, including 
especially the period of the Great Depression, when much of the world went into default, 
the 1980s debt crisis, and also the 1990s debt crisis. The latter crisis saw somewhat fewer 
technical defaults thanks to massive intervention by the official community, particularly by 
THE—INTERNATIONAL Monetary Fund and the World Bank. Whether these massive 
interventions were well advised is an entirely different issue that we will set aside here. In 
Table 5, notable are Turkey’s five defaults, Ecuador and Peru’s six defaults, and Brazil’s 
seven. 


So far we have focused on number of defaults, but there is some arbitrariness to this 
measure. Default episodes can be connected, particularly if debt restructuring terms are 

Table 4. Default and Rescheduling: Africa and Asia, 19000101—19991231    –2006 

Country/date of Dates 
independence 1 
19000000—1824 1925–1949 1950–1974 19750000—2006 
Africa 
Algeria, 1962 1991 
Angola, 1975 1985 
Central African 1981, 1983 
Republic, 1960 
Cote D’Ivoire, 1983, 2000 
1960 
Egypt 1984 
Kenya, 1963 1994, 2000 
Morocco, 1956 1903 1983, 1986 
Nigeria, 1960 1982, 1986, 1992, 
2001, 2004 
SOUTH—AFRICA, 1910 1985, 1989, 1993 
Zambia, 1964 1983 
Zimbabwe, 1965 1965 2000 
Asia 
China 1921 1939 
Japan 1942 
India, 1947 1958, 1969, 1972 
Indonesia, 1949 1966 1998, 2000, 2002 
Myanmar, 1948 2002 
Philippines, 1947 1983 
Sri Lanka, 1948 1980, 1982 

1 The dates are shown for those countries that became independent DURING the 19000101—19991231    . 
Sources: Standard and Poor’s, Purcell and Kaufman (1993), Reinhart, Rogoff and Savastano (2003) and sources cited 
therein. 


harsh and make relapse into default almost inevitable. We have tried in Table 4 to exclude 
obviously connected episodes, so that when a follow—on default occurs within two years of 
an earlier one, we count it as one episode. However to gain further perspective into 
countries default histories, we look next at the number of years each country has spent in 
default since independence. 

26 



Table 5. Default and Rescheduling: Europe, and Latin America, 19000101—19991231    –2006 

Country/date of Dates 
independence 1 
19000000—1824 1925–1949 1950–1974 19750000—2006 

Europe 
Austria 1938, 1940 
Germany 1932, 1939 
Greece 1932 
Hungary, 1918 1932, 1941 
Poland, 1918 1936, 1940 1981 
Romania 1933 1981, 1986 
Russia 1918 1991, 1998 
Turkey 1915 1931, 1940 1978, 1982 
Latin America 
Argentina 1951, 1956 1982, 1989, 2001 
Bolivia 1931 1980, 1986, 1989 
Brazil 1902, 1914 1931, 1937 1961, 1964 1983 
Chile 1931 1961, 1963, 1966, 1983 
1972, 1974 
Colombia 1900 1932, 1935 
Costa Rica 1901 1932 1962 1981, 1983, 1984 
Dominican 1931 1982, 2005 
RepublicEcuador 1906, 1909, 1914 1929 1982, 1999 
El Salvador 1921 1932, 1938 
Guatemala 1933 1986, 1989 
Honduras 1981 
Mexico 1914 1928 1982 
Nicaragua 1911, 1915 1932 1979 
Panama, 1903 1932 1983, 1987 
Paraguay 1920 1932 1986, 2003 
Peru 1931 1969 1976, 1978, 1980, 
1984 
Uruguay 1915 1933 1983, 1987, 1990, 
2003 
VENEZUELA 1983, 1990, 1995, 
2004 

1 The dates are shown for those countries that became independent DURING the 19000101—19991231    . 
Sources: Standard and Poor’s, Purcell and Kaufman (1993), Reinhart, Rogoff and Savastano (2003) and sources 
cited therein. 


We begin by tabulating the results for Asia and Africa in Table 6. Table 6 gives, 
for each country, the year of independence, the total number of reschedulings (using our 
measure) and the share of years since 1800 (or since independence, if more RECENT) spent in 
a state of default or rescheduling. It is notable that, while there are many defaults in Asia, 


Table 6. The Cumulative Tally of Default and Rescheduling: Africa and Asia, 
Year of Independence–2006 


Country Year of Independence 
Share of years in default Total number of defaults 
or rescheduling since and/or reschedulings 
independence or 1800 1 

Africa 
Algeria 1962 13.3 1 
Angola 1975 59.4 1 
Central African 1960 53.2 2 
Republic 
Cote D’Ivoire 1960 48.9 2 
Egypt 1831 3.4 2 
Kenya 1963 13.6 2 
Mauritius 1968 0.0 0 
Morocco 1956 15.7 4 
Nigeria 1960 21.3 5 
SOUTH—AFRICA 1910 5.2 3 
Tunisia 1591/1957 5.3 1 
Zambia 1964 27.9 1 
Zimbabwe 1965 40.5 2 
Asia 
China 1368 13.0 2 
Hong Kong 0.0 0 
India 1947 11.7 3 
Indonesia 1949 15.5 4 
Japan 1590 5.3 1 
Korea 1945 0.0 0 
Malaysia 1957 0.0 0 
Myanmar 1948 8.5 1 
Philippines 1947 16.4 1 
Singapore 1965 0.0 0 
Sri Lanka 1948 6.8 2 
Taiwan 1949 0.0 0 
Thailand 1769 0.0 0 

1 For countries that became independent prior to 1800 the calculations are for 18000000—2006. 
Sources: Authors’ calculations, Standard and Poor’s, Purcell and Kaufman (1993), Reinhart, Rogoff and 
Savastano (2003) and sources cited therein. 


the typical default was resolved relatively quickly. Only Indonesia, India, China and the 
Philippines spent more than 10 % of their independent lives in default (though of 
course on a population—weighted basis, that is most of the region). Africa’s record is much 
worse, with several countries spending roughly half their time in default. Certainly, one 
MAIN reason why African defaults are less celebrated than, say, Latin American defaults, is 
because the debts of African countries have typically been relatively small, and the 
systemic consequences less. 


Table 7 gives the same set of statistics for Europe and Latin America. Greece, as 
noted, spent more than half the years since 1800 in default. A number of Latin American 
countries spent roughly 40 % of their years in default, including Mexico, Peru, 
VENEZUELA, Nicaragua, Dominican Republic, and Cost Rica. 

One way of summarizing the data in Tables 6 and 7 is by looking at a time line 
giving the number of countries in default or restructuring at any given time. We have 
already done this in Figure 1 in section II. These figures, in which spikes represent a surge 
in new borrowers, illustrate the clustering of defaults in an even more pronounced fashion 
than our debt tables that MARK 1. defaults. 

The same is true across countries, although there is a great deal of variance, 
depending especially on how long countries tend to stay in default (compare serial—debtor 
Austria, which has tended to emerge form default relatively quickly, with Greece, which 
has lived in a perpetual state of default). Overall, one can see that default episodes, while 
recurrent, are far from continuous. This wide spacing no doubt reflects adjustments debtors 
and creditors make in the wake of each default cycle. For example, today, many emerging 
markets are following quite conservative macroeconomic policies. Over time, though, this 
caution usually gives way to optimism and profligacy, but only after a long lull. 


Table 7. The Cumulative Tally of Default and Rescheduling: Europe, Latin America, North America, and 

Oceania, Year of Independence–2006 

Country Year of Independence Share of years in default Total number of defaults 

or rescheduling since and/or reschedulings 

independence or 1800 1 

Europe 

Austria 1282 17.4 7 
Belgium 1830 0.0 0 
Denmark 980 0.0 0 
Finland 1917 0.0 0 
France 943 0.0 8 
Germany 1618 13.0 8 
Greece 1829 50.6 5 
Hungary 1918 37.1 7 
ITALY 1569 3.4 1 
NETHERLANDS[NIEDERLANDE]1581 6.3 1 
Norway 1905 0.0 0 
Poland 1918 32.6 3 
Portugal 1139 10.6 6 
Romania 1878 23.3 3 
Russia 1457 39.1 5 
Spain 1476 23.7 13 
Sweden 1523 0.0 0 
Turkey 1453 15.5 6 
United Kingdom 1066 0.0 0 

Latin America 

Argentina 1816 32.5 7 
Bolivia 1825 22.0 5 
Brazil 1822 25.4 9 
Chile 1818 27.5 9 
Colombia 1819 36.2 7 
Costa Rica 1821 38.2 9 
Dominican Republic 1845 29.0 7 
Ecuador 1830 58.2 9 
El Salvador 1821 26.3 5 
Guatemala 1821 34.4 7 
Honduras 1821 64.0 3 
Mexico 1821 44.6 8 
Nicaragua 1821 45.2 6 
Panama 1903 27.9 3 
Paraguay 1811 23.0 6 
Peru 1821 40.3 8 
Uruguay 1811 12.8 8 
VENEZUELA 1830 38.4 10 

North America 

Canada 1867 0.0 0 
United States 1783 0.0 0 

Oceania 

Australia 1901 0.0 0 
New Zealand 1903 0.0 0 


1 For countries that became independent prior to 1800 the calculations are for 18000000—2006. 
Sources: Authors’ calculations, Standard and Poor’s, Purcell and Kaufman (1993), Reinhart, Rogoff and 
Savastano (2003) and sources cited therein. 


30 



V. Global Cycles and External Defaults 
As Kaminsky, Reinhart and Vegh (2004) have demonstrated for the post—war 
period, and Aguirre and Gopinath (2007) have RECENTLY modeled, emerging market 
borrowing tends to be extremely pro—cyclical. Favorable trends in countries’ terms of trade 
(meaning typically, high prices for primary commodities) typically lead to a ramp—up of 
borrowing that collapses into defaults when prices drop. The upper panel of Figure 7 is an 
illustration of the commodity price cycle, which we split into two periods, the pre– and 
post–World War II periods. As the figure broadly suggests for the period 1800 through 
1940, (and as econometric testing corroborates), spikes in commodity prices are almost 
invariably followed by waves of new sovereign defaults. The lower panel of Figure 7 
calibrates the same phenomenon for the 1990s and 2000s. We note that while the 
association does show through in the pre–World War II period, it is less compelling 
subsequently. 

As observed earlier, defaults are also quite sensitive to the global CAPITAL flow cycle. 
When flows drop precipitously, more countries slip into default. Figure 8 documents this 
association by plotting the current account balance of the financial center (the United 
Kingdom and the United States) against the number of new defaults prior to the breakdown 
of Bretton Woods. There is a marked visual correlation between peaks in the CAPITAL flow 
cycle and new defaults on sovereign debt. The financial center current accounts capture 
“global savings glut” pressures, as they give a net measure of excess center—country 
savings, rather than the gross measure given by the CAPITAL flow series in our dataset. 


World commodity prices, 
deviation from trend 
3-year average 
(right axis) 
Number of new defaults 
3-year sum (left axis) 
World commodity prices, 
deviation from trend 
3-year average 
(right axis) 
Number of new defaults 
3-year sum (left axis) 
Figure 7. Commodity Prices and New External Defaults 
1800–1939 

World commodity prices, 

0.6 


18 


deviation from trend, 
3-year average 
(i h i ) 
Number of new defaults 
3-year sum 
18001810182018301840185018601870188018901900191019201930


14 


12 


10 


N umber of countries 

Index leve l 

0 


8 


6 


-0.2 


4 


-0.4 


2 


0 


-0.6 


19400000—2006 

0.5 


0.4 


0.3 


10 


0.2 


Index level 

Number of countries

8 


1940 
1950 
1960 
1970 
1980 
1990 
2000 


2 


-0.2 


0 


-0.3 


Sources: Boughton (1991), The Economist, Gayer, Rostow, and Schwartz (#1953), World Economic Outlook, 
IMF and the authors’ calculations based on the sources listed in Table AI.9. For external default, see 
Appendix I. 
Notes: New external defaults refer to the 1. year of default. Because of the marked negative downward drift 
in commodity prices DURING the sample period, prices are regressed against a linear trend, so as to isolate the 
cycle. 



Figure 8 

Net CAPITAL Flows from the Financial Center and Default 
18180000—1968 

18 


30 


Pe r c e n t 
UK and US Current account balance, 
3-year sum as a % of GDP 
(right axis) 
Number of new defaults 
3-year sum 
1818182818381848185818681878188818981908191819281938


Number of countries 

15

10 


10 


6 
5 


4 


0 
2 


0 


-5 


Sources: Historical Statistics of the United States (2007), Imlah (1958), Mitchell (1993), BANK—OF—ENGLAND. 
Notes: The current account for the UK and THE—US is defined ACCORDING—TO—the relative importance (albeit in a 
simplistic arbitrary way) of these countries as the financial centers and primary suppliers of CAPITAL to the rest 
of the world: 1800–1913 UK receives a weight of 1 (US, 0); 

1914–1939 both countries’ current accounts are 
equally weighted; 

post—1940, US receives a weight equal to 1. 

We recognize that the correlations captured by these figures are merely illustrative, 
and different default episodes involve many different factors. But aside from illustrating 
the kind of insights one can get from such a long and broad dataset, the figures do bring 
into sharp relief the vulnerabilities of emerging markets to global business cycles. The 
problem is that crisis—prone countries, particularly serial defaulters, tend to over—borrow in 
good times, leaving them vulnerable DURING the inevitable downturns. The pervasive view 
that “this time is different” is precisely why it usually isn’t different, and catastrophe 
eventually strikes again. 

The CAPITAL flow cycle illustrated in Figure 8 comes out even more strikingly in 
many individual country graphs, but we do not have space here to include these. An early 


example, though, is illustrated in Figure 9, based on 
16000101—16991231    Spain. The figure 
illustrates how defaults often follow in the wake of large spikes in CAPITAL inflows. 
Figure 9 

Spain: Defaults and Loans to the Crown, 16011679 
(3-year moving sum) 

35 

Defaults of 1607, 1627, and 
1647

30 

25 

20 

15 

10 

5 

0 

Sources: Gelabert (1999a and b), European State Finance Database. 

Crises Emanating from the Center 

We have already seen that major global spikes in defaults 
began in the 1820s, 
the 1870s, 
the 1930s and 
the 1980s. 
The 1930s spike was caused by the worldwide depression that, by most accounts, began in the United States. 
So, too, did 
the 1980s spike, which was caused by U.S. disinflation. 
What of earlier eras? 
Tables 8 and 9 give a thumbnail summary of events, showing how 
the 1825 crisis began with a FINANCIAL—CRISIS in LONDON that spread to Europe, 
causing global trade and CAPITAL flows to plummet. 
This summary of events, of course, is silent as to the magnitude of THE—INTERNATIONAL transmission channel, but 
the tables are nevertheless illustrative of some of the common shocks that might have sparked the commodity and CAPITAL flow cycles seen in the figures in the preceding 

Millions DUCATS 

1600 1606 1612 1618 1624 1630 1636 1642 1648 1654 1660 1666 1672 1678 



Table 8. Crises at the Financial Center and Their International Repercussions: 
1800’s 

Origin of the shock: Nature of common Contagion Countries affected 
country and date external shock mechanisms 
LONDON, 18250000—1826 Major commercial and Upon Peru’s 1826 Chile and Gran Colombia 
financial crises in LONDON default, LONDON bond (which comprised today’s 
DURING 1825–26, which holders IMMEDIATELY Colombia, Ecuador, and 
spread to continental become concerned VENEZUELA) default later 
Europe. Trade and CAPITAL about other Latin in the year. By 1828, all 
flows with Latin America American countries’ of Latin America, with 
plummet. ability to service their the exception of Brazil, 
debts; 

bond prices had defaulted. 
collapse. 

German and Austrian 
STOCK—MARKETs collapse, 
May 1873 

Baring Crisis, 1890 

French war indemnity paid to Prussia in 1871 leads to speculation in Germany and Austria. 
As far as the periphery is concerned, the world recession (
1873–1879) results in a dramatic fall in trade and CAPITAL flows originating in the core. 

Argentina stops dividend payments in April 1890, 
leading to a domestic bank run. 
The HOUSE—OF—BARING, a major lender to Argentina, declares itself insolvent in 
November 1890. 

CAPITAL flows to THE—US fall in the wake of German crisis 
(Kindleberger 1989). 
Ensuing world recession (18730000—1879) 
leads to debt servicing problems in the periphery through reduced exports and tax REVENUes. 
Initial defaults in small Central American nations in ;;01;; 1873 leads to a fall in bond prices. 
Strong economic links between Britain and Argentina through trade and financial integration. 

Crisis spreads quickly to ITALY, HOLLAND, and 
Belgium, leaps the 
Atlantic in September and crosses back again to 
involve England, France, and Russia 
(Kindleberger, 2000). 
By 1876, the Ottoman Empire, 
Egypt, 
Greece, and 
8 Latin American countries had defaulted. 

Crisis mostly confined to Argentina and 
Uruguay (which defaulted in 1891). 

sections. Other examples where crises in the center lead to global financial crises include 
THE—GERMAN—and Austrian STOCK—MARKET collapse of 1873 (which has been studied by 
Eichengreen in several contributions) and, of course, the WALL—STREET STOCK—MARKET crash of 
1929. It is also notable that crises in the center do not always lead to full—blown global 
financial crises, as illustrated by the Barings crisis of 1890 (where the repercussions were 


mainly felt by Argentina and Uruguay), as well as by THE—US—STOCK—MARKET crash and bank 
runs of 1907, which transmitted mainly to Germany, France and ITALY. 

Domestic Debt 

So far, we have focused on external debt crises, but NOT—YET looked at domestic debt 
buildups. 

Some have argued that external defaults are less likely in the present period because 
GOVERNMENTs are now relying more on domestic debt. For example, in 2001 to 2005, 
domestic GOVERNMENT debt in Mexico and Colombia accounted for more than 50 % of 
total debt, as opposed to less than 20 % IN THE EARLY 1980s. But this is not new. In 
1837, in the midst of one of Mexico’s longer default spells, domestic debt amounted to 64 
% of total public debt. The earliest year where our dataset has domestic debt statistics 
for Colombia is 1923, when domestic debt accounted for 54 % of total debt. DURING 
THE SAME YEAR, domestic debt accounted for 52 % of Brazil’s debt and 63 % of 
Peru’s debt. The 1920s, of course, was a period prior to the massive wave of external 
defaults in the 1930s, a fact that ought to be looked at more closely by those who believe 
that the RECENT shift by emerging markets towards domestic debt, and away from external 
debt, somehow provides strong protection to creditors. 

Figure 10 makes this point more systematically by examining the behavior of 
domestic and external sovereign debt in the run—up to default. The bars give the average 
experience of both types of debt, normalized by their levels four years prior to the credit 
event. As can be seen, both components rise rapidly, at about the same rates, just before 
default. 


But domestic debt buildups often happen in the aftermath of external default, 
precisely because countries have difficulty borrowing abroad. Figure 11 illustrates the case 
of China, which had a massive run—up in domestic debt following its default of 1921. 


Figure 10 

The Runup in Domestic and External Debt 
ABEND—DES—AM Default, Average Default Episode: 18000000—2006 

150 


140 


130 


External 
Domestic 
t-4=100 
t-4t-3t-2t-1T


Index . 

120 


110 


100 


90 


Sources: See Appendix I and Reinhart and Rogoff (2008a). 
Notes: T refers to the year of the external debt crisis. 

Figure 11 

 China: Domestic Public Debt Outstanding, 18950000—19460000 0000  
The default of 

0 
2000 
4000 
6000 
8000 
10000 
12000 
14000 
Millions Chinese $ (yuan) 
The default of 
19210000—1936 
19390000—1949 
1895 1899 1903 1907 1911 1915 1919 1923 1927 1931 1935 1939 1943 

Sources: Cheng (2003), Huang (1919), UN and authors’ calculations. 
Notes: For 1895–1915 the debt stock is calculated from domestic debentures data. ACCORDING—TO—Huang, 
China did not have domestic debt prior to its 1895 domestic issue. 



We have already acknowledged that domestic debt is not equivalent to foreign debt, 
nor should it be treated as such. But we have also established that domestic debt has long 
been fully as significant as external debt in meeting emerging market financing needs. 
There is nothing “original” about it. And as we show in Reinhart and Rogoff (2008a), 
defaults on domestic debt appear to be associated with similar magnitudes of output loss as 
defaults on external debt. 

VI. Default through inflation 
If serial default is the norm for a country passing through the emerging market state 
of development, then the tendency to lapse into periods of high and extremely high 
inflation is an even more striking common denominator. No emerging market country in 
history, including the United States (whose inflation rate exceeded 20 % DURING the 
country’s 1860s CIVIL—WAR) has managed to escape bouts of high inflation. 

Of course, the problems of external default, domestic default and inflation are all 
integrally related. A GOVERNMENT that chooses to default on its debts can hardly be relied on 
to preserve the value of its country’s currency. Money creation and interest costs on debt 
all enter the GOVERNMENT’s budget constraint and, in a funding crisis, a sovereign will 
typically grab from any and all sources. 

12000101—12991231     Europe going back to In this section, we give an over VIEW—OF results from our annual cross—country database on inflation 
We are only able here to give a 
helicopter tour (so to speak) of our entire cross—country inflation dataset which, to our 
knowledge, spans considerably more episodes of high inflation and across a broader range 
of countries than any existing. 

Although some writers seem to believe that inflation only really became a problem 
with the advent of paper currency in the 1800s, students of THE—HISTORY—OF—metal currency 


will know that GOVERNMENTs found ways to engineer inflation long before that. The MAIN 
device was through debasing the content of the coinage, either by mixing in cheaper 
metals, or by shaving down coins and reissuing smaller coins in the same denomination. 
Modern currency presses are just a more technologically advanced and more efficient 
approach to achieving the same end. 

 Tables 9 and 10 give data on currency debasement across a broad range of 
European countries DURING the pre–paper currency era, 12280000—1799. The table illustrates 
how strikingly successful monarchs were at implementing inflationary monetary policy. 
Sweden achieved a debasement of 41 % in a single year (1572), while the UK 
achieved a 50 % debasement in 1551; 

Turkey’s debasement was 44 % in 1586. 
The 2. column of the table looks at cumulative currency debasement over long 
periods, often adding up to 50 % or more. Table 10 looks at the same statistics for 
European countries DURING the 19. Jh., where outliers include Austria’s 55 
% debasement in 1812, and Russia’s 57 % in 1810, both in the aftermath of the 
NAPOLEONic War. Turkey, in 1829, managed to reduce the silver content of its coins by 50 
%. 

The pattern of sustained debasement emerges strikingly in Figure 12, which plots 
the silver content of an equally weighted average of the European currencies in our early 
sample (plus Russia and Turkey). “The March Toward Fiat Money” shows that modern 
inflation is not as different as some might believe. 


Figure 12. 

1 
2 
3 
4 
5 
6 
7 
8 
Grams 
NAPOLEONic Wars, 17990000—18150000 
in 1812 Austria debases 
currency by 55 % 
The March Toward Fiat Money: Europe 14000000—1850 

10 

Average Silver Content (in grams) of 10 Currencies 

9 

0 
1400 1450 1500 1550 1600 1650 1700 1750 1800 1850 

Sources: Primarily Allen and Unger and other sources listed in Table AI.4. 
Notes: In the cases where there is more than one currency circulating in a particular country (in Spain, for 
example, we have the New CasTILLE maravedi and the Valencia dinar) we calculate the simple average. 



Table 9. Expropriation through Currency Debasement: Europe, 12580000—17990000 

Cumulative Share of years in which there was 
Country and Period covered decline in Largest a debasement of the currency (i.e. 
currency silver content debasement a reduction in the silver content) 
of currency (%)
( %) and year All 15 % or 
greater 

Austria Vienna 13710000—140000 –69.7 –11.1 1463 25.8 0.0 
kreuzer 15000000—17990000 –59.7 –12.5 1694 11.7 0.0 

Belgium 13490000—140000 –83.8 –34.7 1498 7.3 3.3 
hoet 15000000—17990000 –56.3 –15.0 1561 4.3 0.0 

France livre 12580000—140000 –74.1 –56.8 1303 6.2 0.4 
tournois 15000000—1789 –78.4 –36.2 1718 14.8 1.4 
Germany 
Bavaria– 14170000—140000 –32.2 –21.5 1424 3.7 1.2 

AUGSBURG 15000000—17990000 –70.9 –26.0 1685 3.7 1.0 
pfenning 
Frankfurt 13500000—140000 –14.4 –10.5 1404 2.0 0.0 
pfenning 15000000—1798 –12.8 –16.4 1500 2.0 0.3 

ITALY lira 12800000—140000 –72.4 –21.0 1320 5.0 0.0 
fiorentina 15000000—17990000 –35.6 –10.0 1550 2.7 0.0 
NETHERLANDS[NIEDERLANDE]

Flemish grote 13660000—140000 –44.4 –26.0 1488 13.4 5.2 
1500–1575 –12.3 –7.7 1526 5.3 0.0 
Guilder 14500000—140000 –42.0 –34.7 1496 14.3 6.1 
15000000—17990000 –48.9 -15.0 1560 4.0 0.0 

 Portugal REIS 17500000—17990000 -25.6 –3.7 1766 34.7 0.0 

Russia ruble 17610000—17990000 –42.3 –14.3 1798 44.7 0.0 
Spain 
New CasTILLE 15010000—17990000 –62.5 –25.3 1642 19.8 1.3 
MARAVEDI 
Valencia dinar 13510000—140000 –7.7 –2.9 1408 2.0 0.0 
1500–1650 –20.4 –17.0 1501 13.2 0.7 

Sweden mar 1523–1573 –91.0 –41.4 1572 20.0 12.0 

ortug 
Turkey 
Akche 15270000—17990000 –59.3 –43.9 1586 10.5 


3.1 
United 12600000—140000 –46.8 –20.0 1464 0.8 0.8 
Kingdom 15000000—17990000 –35.5 –50.0 1551 2.3 1.3 
pence 
Sources: Primarily Allen and Unger and other sources listed in Table AI.4. 


Table 10. Expropriation through Currency Debasement: Europe, the 
18000101—18991231    

Country Period 
covered 
Cumulative 
decline in 
silver content 
of currency 
(%) 
Largest 
debasement 
(%) and 
year 
Share of years in which there 
was a debasement of the 
currency (i.e. a reduction in 
the silver content) 
All 15 % or 
greater 
Austria 18000000—18600000 –58.3 –55.0 1812 37.7 11.5 
Germany 18000000—18300000 –2.2 –2.2 1816 3.2 0.0 
ITALY 18000000—1859 0.0 0.0 0.0 0.0 
Portugal 18000000—1855 –12.8 –18.4 1800 57.1 1.8 
Russia 18000000—1899 –56.6 –41.3 1810 50.0 7.0 
Turkey 18000000—1899 –83.1 –51.2 1829 7.0 7.0 
United 
Kingdom 
18000000—1899 –6.1 –6.1 1816 1.0 0.0 

Sources: Primarily Allen and Unger and other sources listed in Table AI.4. 

Inflation 

However spectacular some of the coinage debasements reported in Tables 9 and 10, 
there is no question that the advent of the printing press brought inflation up to a whole 
new level. Figure 12 illustrates the median inflation rate for all the countries in our sample, 
from 
15000000—20060000    (taking a five—year moving average to smooth out cycle and measurement error). 
The figure shows a clear inflationary bias throughout history 
(although of course there are always periods of deflation due to business cycles, poor crops, etc.). 
Starting in the 19000101—19991231    , however, inflation spikes radically. 


Figure 13 

Median Inflation Rate All Countries 

5—Year Moving Average: 15000000—2006 


-4 
-2 
0 
2 
4 
6 
8 
10 
12 
14 
1500 1550 1600 1650 1700 1750 1800 1850 1900 1950 2000 
% 
Sources: There are innumerable sources given the length of the period covered and the large number of 
countries included. These are listed in Table AI. 

We look at country inflation data across the centuries in the next three tables. Table 
11 gives data for the sixteenth through 
18000101—18991231    over a broad range of currencies. 
What is stunning is that every country in both Asia and Europe experienced a significant 
number of years with inflation over 20 % DURING this era, and most experienced a 
significant number of years with inflation over 40 %. Take Korea, for example, 
where our dataset begins in 1743. Korea experienced inflation of over 20 % almost 
half the time —UNTIL 1800, and inflation over 40 % almost one—third —OF—THE—TIME. 
Poland, where the data go back to 1704, has extremely similar ratios. Even the United 
States experienced an episode of very high inflation, as inflation peaked at nearly 200 
% five % in 1779. The New World colonies of Latin America experienced 
frequent bouts of very high inflation long before the wars of independence from Spain. 


Table 11. “Default” through Inflation: Asia, Europe, and the “New World” 15000000—17990000 

Country 
Period 
covered 
Share of years in which 
inflation exceeded 
Number of 
hyperinflations1 
20 % 40 % 
Maximum 
annual 
inflation 
Year of 
peak 
inflation 
Asia 
16390000—17990000    China 
Japan 16010000—1650 
Korea 17430000—17990000 
Europe 
Austria 15010000—17990000 
Belgium 15010000—17990000 
Denmark 17490000—17990000 
France 15010000—17990000 
Germany 15010000—17990000 
ITALY 15010000—17990000 
NETHERLANDS[NIEDERLANDE]15010000—17990000 
Norway 16660000—17990000 
Poland 17040000—17990000 
Portugal 17290000—17990000 
Spain 15010000—17990000 
15400000—17990000    Sweden 
Turkey 15860000—17990000 
United 
Kingdom 
15010000—17990000 
The “New 
World” 
Argentina 17770000—17990000 
Brazil 17640000—17990000 
Chile 17510000—17990000 
Mexico 17420000—17990000 
Peru 17510000—17990000 
United 
States 
17210000—17990000 
14.3 6.2 0 
34.0 14.0 0 
43.9 29.8 0 
8.4 6.0 0 
25.1 11.0 0 
18.8 10.4 0 
12.4 2.0 0 
10.4 3.4 0 
19.1 7.0 0 
4.0 0.3 0 
6.0 0.8 0 
43.8 31.9 0 
19.7 2.8 0 
4.7 0.7 0 
15.5 4.1 0 
19.2 11.2 0 
5.0 1.7 0 
4.2 0.0 0 
25.0 4.0 0 
4.1 0.0 0 
22.4 7.0 0 
10.2 0.0 0 
7.6 4.0 0 
116.7 
98.9 
143.9 
99.1 
185.1 
77.4 
121.3 
140.6 
173.1 
40 
44.2 
92.1 
83.1 
40.5 
65.8 
53.4 
39.5 
30.8 
33.0 
36.6 
80.0 
31.6 
192.5 
1651 
1602 
1787 
1623 
1708 
1772 
1622 
1622 
1527 
1709 
1709 
1762 
1757 
1521 
1572 
1621 
1587 
1780 
1792 
1763 
1770 
1765 
1779 

1 Hyperinflation is defined here as an annual inflation rate of 500 % or higher (this is not the traditional 
Cagan definition). 

Table 12 looks at THE SAME YEARs 18000000—2006 as Table 11, but for thirteen African 
countries and 12 Asian countries. SOUTH—AFRICA, Hong Kong and Malaysia have notably 
the best track records at resisting high inflation, albeit SOUTH—AFRICA’s record extends back 


to 1896, whereas Malaysia’s and Hong Kong’s only go back to 1949 and 1948 
respectively.14 

Table 12. “Default” through Inflation: Asia and Africa 18000000—2006 

Beginning Share of years in which Number of Maximum Year of 
Country of period inflation exceeded hyperinflation annual peak 
covered years1 inflation inflation 
20 % 40 % 

Africa 

Algeria 1879 24.1 12.0 0 69.2 1947 
Angola 1915 53.3 44.6 4 4,416.0 1996 
Central 1957 4.0 0.0 0 27.7 1971 
African 
Republic 
Cote 1952 7.3 0.0 0 26.0 1994 
D’Ivoire 
Egypt 1860 7.5 0.7 0 40.8 1941 
Kenya 1949 8.3 3.3 0 46.0 1993 
Mauritius 1947 10 0.0 0 33.0 1980 
Morocco 1940 14.9 4.5 0 57.5 1947 
Nigeria 1940 22.6 9.4 0 72.9 1995 
South 1896 0.9 0.0 0 35.2 1919 
Africa 
Tunisia 1940 11.9 6.0 0 72.1 1943 
Zambia 1943 29.7 15.6 0 183.3 1993 
Zimbabwe 1920 23.3 14.0 1,216.0 2006 

Asia 

China 1800 19.3 14.0 3 1,579.3 1947 
Hong 1948 1.7 0.0 0 21.7 1949 
Kong 
India 1801 7.3 1.5 0 53.8 1943 
Indonesia 1819 18.6 9.6 1 939.8 1966 
Japan 1819 12.2 4.8 1 568.0 1945 
Korea 1800 35.3 24.6 0 210.4 1951 
Malaysia 1949 1.7 0.0 0 22.0 1950 
Myanmar 1872 22.2 6.7 0 58.1 2002 
Philippines 1938 11.6 7.2 0 141.7 1943 
Singapore 1949 3.4 0.0 0 23.5 1973 
Taiwan 1898 14.7 11.0 0 29.6 1973 
Thailand 1821 14.0 7.5 0 78.5 

14 The dates in table 13 extend back prior to independence for many countries, including for example 
Malaysia. 


Most of the countries in Asia and Africa however, have experienced waves of high and very high inflation. 
The notion that Asian countries have been immune from Latin 
American–style high inflation is just as naïve as the notion that Asian countries were 
immune from default crises up —UNTIL the late 1990s Asian FINANCIAL—CRISIS. China 
experienced over 1.500 % inflation in 1947 
15, 
Indonesia over 900 % in 1966. 
Even the Asian tigers Singapore and Taiwan experienced inflation well over 20 % in 
the early 1970s. 

Africa, perhaps not surprisingly, has a still worse record. 
Angola had inflation of over 4.000 % in 1996, and 
Zimbabwe of over 1.000 % in 2006. 
Had we extended the table through 2007, 
we would have picked up Zimbabwe’s 66.000 % inflation for 2007, 
putting that country on track to surpass the REPUBLIC—OF—the Congo (not included in our sample), 
which has experienced three hyperinflations since 1970 
(Reinhart and Rogoff, 2002). 

Finally, Table 13 lists inflation for 1800 through 2006 for Europe, Latin America, 
North America and Oceana. The European experiences include the great post—war 
hyperinflations studied by Cagan (1956). But even setting aside the hyperinflations, we see 
that countries such as Poland, Russia and Turkey experienced high inflation an 
extraordinarily large % —OF—THE—TIME. Norway had 152 % inflation in 1812, 
Denmark 48 % inflation in 1800, and Sweden 36 % inflation in 1918. Latin 
America’s post–World War II inflation history is famously spectacular, as the table 
illustrates, with many episodes of peacetime hyperinflations in the 1980s and 1990s. 

15 China, which invented the printing press well ahead of Europe, famously experienced paper—currencycreated 
high inflation episodes in the twelfth and thirteen centuries. (See for example, Fischer, Sahay and 
Vegh, 2003) These episodes are in our database as well. 


Table13. “Default” through Inflation: 
Europe, Latin America, North America and Oceania, 18000000—20060000  


Beginning Share of years in which Number of Maximum Year of 
Country of period 
covered 
inflation exceeded hyperinflation 
years1 
annual 
inflation 
peak 
inflation 
20 % 40 % 
Europe 
Austria 1800 20.8 12.1 2 1,733.0 1922 
Belgium 1800 10.1 6.8 0 50.6 1812 
Denmark 1800 2.1 0.5 0 48.3 1800 
Finland 1861 5.5 2.7 0 242.0 1918 
France 1800 5.8 1.9 0 74.0 1946 
Germany 1800 9.7 4.3 2 2.22E+10 1923 
Greece 1834 13.3 5.2 4 3.02E+10 1944 
Hungary 1924 15.7 3.6 2 9.63+26 1946 
ITALY 1800 11.1 5.8 0 491.4 1944 
NETHERLANDS[NIEDERLANDE]1800 1.0 0.0 0 21.0 1918 
Norway 1800 5.3 1.9 0 152.0 1812 
Poland 1800 28.0 17.4 2 51,699.4 1923 
Portugal 1800 9.7 4.3 0 84.2 1808 
Russia 1854 35.7 26.4 8 13,534.7 1923 
Spain 1800 3.9 1.0 0 102.1 1808 
Sweden 1800 1.9 0.0 0 35.8 1918 
Turkey 1800 20.5 11.7 0 115.9 1942 
United 1800 2.4 0.0 0 34.4 1800 
Kingdom 
Latin America 
Argentina 1800 24.6 15.5 4 3,079.5 1989 
Bolivia 1937 38.6 20.0 2 11.749.6 1985 
Brazil 1800 28.0 17.9 6 2,947.7 1990 
Chile 1800 19.8 5.8 0 469.9 1973 
Colombia 1864 23.8 1.4 0 53.6 1882 
Costa Rica 1937 12.9 1.4 0 90.1 1982 
Dominican 1943 17.2 9.4 0 51.5 2004 
Republic 
Ecuador 1939 36.8 14.7 0 96.1 2000 
El Salvador 1938 8.7 0.0 0 31.9 1986 
Guatemala 1938 8.7 1.4 0 41.0 1990 
Honduras 1937 8.6 0.0 0 34.0 1991 
Mexico 1800 42.5 35.7 0 131.8 1987 
Nicaragua 1938 30.4 17.4 6 13,109.5 1987 
Panama 1949 0.0 0.0 0 16.3 1974 
Paraguay 1949 32.8 4.5 0 139.1 1952 
Peru 1800 15.5 10.7 3 7,481.7 1990 
Uruguay 1871 26.5 19.1 0 112.5 1990 
VENEZUELA 1832 10.3 3.4 0 99.9 1996 
North America 
Canada 1868 0.7 0.0 0 23.8 1917 
United States 1800 1.0 0.0 0 24.0 1864 
Oceania 
Australia 1819 4.8 1.1 0 57.4 1854 
New Zealand 1858 0.0 0.0 0 17.2 1980 


 In all of Table 13, only New Zealand and Panama have no periods of inflation over 
20 %, although New Zealand’s inflation rate reached 17 % as RECENTLY as 1980, 
and Panama had 16 % inflation in 1974. 

As with debt defaults, the last few years have been a relatively quiescent period in 
terms of very high inflation, although many countries (including Argentina, VENEZUELA and 
of course Zimbabwe) still have very high inflation.16 Many observers, following the same 
logic as in commenting on external default, have concluded that “this time is different.” 
Perhaps, but, as with debt defaults, experience suggests that quiet periods do not extend 
indefinitely. 
Exchange rate crashes 

Having discussed currency debasement and inflation crises, including at this late 
stage a long expose on exchange rate crashes seems somewhat redundant. The database 
on exchange rates is almost as rich as that on prices, especially if one takes into account 
silver—based exchange rates, and is described in detail in the Appendices. In this lengthy 
sample inflation crises and exchange rate crises travel hand— in— hand in the 
overwhelming majority of episodes across time and countries (with a markedly tighter 
link in chronic— inflation countries). 

Instead, as regards exchange rate behavior, probably the most surprising evidence 
comes from the NAPOLEONic Wars, DURING which exchange rate instability escalated to a 
level that had not been seen before and was not to be seen again for nearly one hundred 
years. This is starkly illustrated in Figures 14 and 15, with the former depicting the 
incidence of high inflation and the latter showing median inflation. The significantly higher 

16 —AT—THE—TIME—OFthis writing the “official” inflation rate in Argentina is 8 %—informed estimates place 
it at 26 %. 


incidence of crashes and larger median changes in the more modern period are hardly a 
surprise. 
Figure 14 

Currency Crashes: Share of Countries with an Annual 
Depreciation Greater than 15 %: 18000000—2006 

0 
10 
20 
30 
40 
50 
% 
NAPOLEONic 
Wars 
1800 1815 1830 1845 1860 1875 1890 1905 1920 1935 1950 1965 1980 1995 
Figure 15 

Median Annual Depreciation All Countries 

5—Year Moving Average: 18000000—2006 

-2 
-1 
0 
1 
2 
3 
4 
5 
6 
7 
8 
1800 1815 1830 1845 1860 1875 1890 1905 1920 1935 1950 1965 1980 1995 
%NAPOLEONic Wars 
Sources: The primary sources are Global Financial Data, and Reinhart and Rogoff (2003), but there are numerous 
others that are listed in Appendix I. 


VII. Varieties of Crises 
We deal with five “varieties” of economic crises: external default, domestic 
default, banking crises, currency crashes, and inflation outbursts.17 All these variables take 
on the value of one when there is a crisis and zero otherwise. Thus, in a tranquil year the 
tally across crises for that particular country would total zero, while in the worst—case 
scenario (Argentina 2002, for instance) it would sum to five. HENCE, each country has an 
entry each year in the 0 to 5 range. We next take simple averages across all countries, or 
across countries in a particular region, and thus construct a composite index of financial 
instability that is multidimensional. 

These are the time series shown for the full sample in Figure 16 for the whole 
sample and for Asia. We have comparable figures for all the other regions. We selected 
Asia to highlight a point we have made earlier on more than one occasion. Namely, that 
the 1997–1998 crisis was not the 1. and that the region had several protracted bouts of 
economic instability by international standards of the day. 

VIII. Conclusions 
This paper offers a detailed quantitative over VIEW—OF THE—HISTORY—OF—financial crises 
dating from the mid-
13000101—13991231    default of Edward III of England to the present subprime 
crisis in the United States. Our study is based on a comprehensive new statistical 
dataset compiled by the authors that covers every region of the world and spans several 
centuries. 

Inevitably, a database of this scale and scope, involving so many primary and 
secondary historical sources (that do not always agree), will contain some errors and 
omissions, despite our best efforts. We welcome suggestions for corrections, additions, 

17 The total would come to 6 if we include currency debasement—but for this there is far less data across 
countries, as discussed. 


and improvements of this database, which we will attempt to incorporate into the online 
version, with appropriate attribution and cross—referencing. 

Bad 

1.8 

1.6 

1.4 

1.2 
1 
0.8 

0.6 

0.4 

0.2 
Good 0 
Number (from 0 to 5) 

Figure 16 

Variety of Crises, Asia: 18000000—2006 
Average Number of Crises per Country, 5—year Average 


Asia 
All 
Countries 
Sources: The authors’ calculations on the basis of the data reported in Appendices I and II and the crises 
definitions described earlier. 
Notes: We deal with five “varieties” of economic crises (external default, domestic default, banking crises, 
currency crashes, and inflation outbursts), and all these variables take on the value of one when there is a 
crisis and zero otherwise. Thus, in a tranquil year the tally across crises for that particular country would total 
zero, while in the worst—case scenario (Argentina 2002, for instance) it would sum to five. HENCE, each 
country has an entry each year in the 0–5 range. We next take simple averages across all countries or across 
countries in a particular region and these are the time series shown above. 

Our principle aim here has been to illustrate some core features of this sweeping 

database, trying to bring out a few fundamental regularities. We are fully aware that, in 

such a broad synthesis, we are inevitably obscuring important nuances surrounding 

historically diverse episodes. 

With these caveats in mind, this “panoramic” quantitative overview has revealed a 

number of important facts. 1. and foremost, we illustrate the near universality of 


episodes of serial default and high inflation in emerging markets, extending to Asia, Africa, 
and —UNTIL not so long ago, Europe. We show that global debt crises have often radiated from 
the center through commodity prices, CAPITAL flows, interest rates, and shocks to investor 
confidence. We also show that the popular notion that today’s emerging markets are 
breaking new ground in their extensive reliance on domestic debt markets, is hardly new. 

This brings us to our central theme—the “this time is different syndrome.” There is 
a view today that both countries and creditors have learned from their mistakes. Thanks to 
better—informed macroeconomic policies and more discriminating lending practices, it is 
argued, the world is not likely to again see a major wave of defaults. Indeed, an often—cited 
reason THESE—DAYS why “this time it’s different” for the emerging markets is that 
GOVERNMENTs there are relying more on domestic debt financing. 

Such celebration may be premature. CAPITAL flow/default cycles have been around 
—SINCE—AT—LEAST 1800—if not before. Technology has changed, the height of humans has 
changed, and fashions have changed. Yet the ability of GOVERNMENTs and investors to 
delude themselves, giving rise to periodic bouts of euphoria that usually end in tears, seems 
to have remained a constant. As Kindelberger wisely titled the 1. chapter of his classic 
book “FINANCIAL—CRISIS: A Hardy Perennial.” 

On a more positive note, our paper at least raises the question of how a country 
might “graduate” from a HISTORY—OF—serial default. Although the case of 
16000101—16991231    
England has been much studied, it appears to be exceptional. It is not clear how well the 
institutional innovations noted by North and Weingast (1996) would have fared had Britain 
been a bit less fortunate in the many wars it fought in subsequent years. For example, had 
NAPOLEON not invaded Russia and France prevailed in the NAPOLEONic War, would Britain 
really have honored its debts. 


-RECENT Interesting cases include Greece and Spain, 
countries Greece and Spain appear to have escaped a severe HISTORY—OF—serial default not only by reforming institutions, but
 by benefiting from the anchor of the European Union. 
Austria, too, managed to emerge from an extraordinarily checkered bankruptcy history by closer integration with post—war Germany, 
that 
1980s and 1990s.IN THE BEGAN EVEN BEFORE European integration a process began to accelerate 
In Latin America, Chile has seemingly emerged from serial default 
despite extraordinary debt pressures 
through the simple expedient of running large and sustained CURRENT—ACCOUNT—SURPLUSes. 
These CURRENT—ACCOUNT—SURPLUSes allowed the country to pay down its external debt to an extremely low level. 
True graduation, of course, would mean that 
Chile could start raising its debt levels if needed 
(say, to benefit from countercyclical fiscal policy) 
without quickly slipping back into problems. 

Mexico is an interesting case where, despite profound failure to engage in deep 
institutional reform, the country stands on the verge of graduation thanks to a combination 
of better monetary and fiscal policy, as well as the North American Free Trade Agreement. 
It is an interesting question whether, through deeper economic integration, the United 
States can offer the same pull to Latin countries as the European Union has done in its early 
days. Of course, if history tells us anything, it is that we cannot jump to “this time is 
different” conclusions. In particular, concluding that countries like Hungary and Greece 
will never default again because “this time is different due to the European Union” may 
prove a very short—lived truism. 

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Appendix: A Global Database with a Long-term View: Sources and Methodology 

This appendix presents a broad-brush description of the comprehensive database used in 
this study and evaluates its MAIN sources, strengths, and limitations. Since the theme of this work is 
that the devil lurks in the details, further documentation on the coverage and numerous sources for 
individual time series by country and by period is provided 
—IN—DATA Appendices I and II. Those are 
devoted to the macroeconomic time series used and the public debt data (which is the centerpiece of 
our analysis), respectively. 

THE—REMAINDER of this appendix is organized as follows: The 1. section describes the 
compilation of the family of time series that are brought together from different major and usually 
well-known sources. These series include prices, modern exchange rates (and earlier metal-based 
ones), real GDP, and exports. For the RECENT period, the data are primarily found in standard largescale 
databases. For earlier history, we relied on individual scholars or groups of scholars—more 
details to follow. Next, we describe the data that is more heterogeneous in both its sources and 
methodologies. These are series on GOVERNMENT finances, and individual efforts to construct 
national accounts—notably nominal and real GDP, particularly pre-1900. The remaining two 
sections are devoted to describing the particulars of building a cross-country, multi-century 
database on public debt and its characteristics and the various manifestations and measurements of 
economic crises. Those include domestic and external debt defaults, inflation and banking crises, 
and currency crashes and debasements. The construction of the public domestic and external debt 
database can be best described as more akin to archeology than economics. The compilation of 


crises episodes encompasses both mechanical rules of thumb to date a crisis as well as arbitrary 
judgment calls on the interpretation of historical events as described by the financial press and 
scholars over the centuries. 

I. Prices, Exchange Rates, Currency Debasement, and Real GDP 
1. Prices 
Since an overarching theme of our analysis is to document the incidence and magnitude of 
various forms of expropriation or default through the ages, no such study would be complete 
without taking stock of expropriation through inflation. Following the rise of fiat currency, 
inflation became the modern-day version of currency debasement. To that end, our preferred 
measures are consumer price indices or their close relative, cost-of-living indices (as those 
constructed by WILLIAMSON et al. in several “regional” papers).18 Our data sources for the modern 
period are primarily the standard databases of THE—INTERNATIONAL Monetary Fund—International 
Financial Statistics (IFS) and World Economic Outlook (WEO). For pre–World War II coverage 
(usually from the early 1900s or late 1800s), Global Financial Data (GFD) and WILLIAMSON et al., 
and the Oxford Latin American History Database (OXLAD, http://oxlad.qeh.ox.ac.uk/) are key 
sources. 

Since our analysis spans several earlier centuries, we rely on the meticulous work of a number 
of economic historians who have constructed such price indices item by item, most often by city 
rather than by country, from primary sources. In this regard, the scholars participating in the Global 
Price and Income History Group project at the UNIVERSITÄT—California, Davis 
(http://gpih.ucdavis.edu/) and their counterparts at the Dutch International Institute of Social 
History (http://www.iisg.nl/hpw/ ) have been an invaluable source for prices is Europe and Asia. 19 
The complete references by author to this body of scholarly work are given in Appendix I and in 
the references. For colonial America, the Historical Statistics of the United States (HSUS , 

18 These regional papers provided time series for numerous developing countries for the mid-1800s to pre– 
WWII. 
19 While our analysis of inflation crises begins in 1500, many of the price series begin much earlier. 



http://hsus.cambridge.org) RECENTLY updated THE—US data, while Richard Gardner (Economic 
History Data Desk: Economic HISTORY—OF—Latin America, the United States and the New World, 
1500–1900, at http://home.comcast.net/~richardgarner04/) covers key cities in Latin America. 

On methodology 

 When more than one index is available for a country, we work with the simple average. This is 
most useful when there are price series for more than one city for the same country, such as in the 
pre-1800s data. When no such consumer price indices are available, we turn to wholesale or 
producer prices indices (as, for example, China in the 1800s and THE—US in the 1720s). Absent any 
composite index, we fill in the holes in coverage with individual commodity prices. This almost 
always takes the form of wheat prices for Europe and rice prices for Asia. We realize that a single 
commodity (even if it is the most important one) is a relative price, rather than the aggregate we 
seek, so if on any given year we have at least one consumer (or cost-of-living) price series and the 
price of wheat (or rice)—we do not average the two but give full weight to the composite price 
index. Finally, from 1980 to the present the WEO data dominates all other sources, as it enforces 
uniformity—although for the RECENT period discrepancies across these price indices is, at most, 
trivial. 

2. Exchange rates, modern and early, and currency debasement 
The handmaiden to inflation is, of course, currency depreciation. For post–World War II 
data, our primary sources for exchange rates are IFS for official rates and Pick’s Currency 
Yearbooks for market-based rates, as quantified and documented in detail in Reinhart and Rogoff 
(2004). For modern pre-war rates GFD, OXLAD, HSUS, and THE—LEAGUE—OF—Nations Annual 
Reports are the primary sources. These are sometimes supplemented with scholarly sources for 
individual countries, as described in Appendix I. Less modern are the exchange rates for the 
late1600s–early1800s for a handful of European currencies, which are taken from John Castaing's 
Course of Exchange, which appeared twice a week (on Tuesdays and Fridays) from 1698 


throughout the following century or so (see European State Finance Data Base, ESFDB, 
http://www.le.ac.uk/hi/bon/ESFDB/frameset.html.) 

The earlier “silver-based” exchange rates were calculated by these authors (trivially) from 
the time series provided primarily by Robert Allen (for other sources see Appendix I, Table AI.4, 
which lists individual authors), who constructed continuous annual series on the silver content of 
several European currencies).20 The earliest series begin in the mid-13. Jh. for ITALY and 
England. As we describe in this appendix, these series are the foundation for dating and 
quantifying the “debasement crises”—the precursors of modern devaluations. 

3. Real GDP 
To maintain homogeneity, inasmuch as possible for our large sample of countries over the 
course of approximately 200 years, we employ as a primary source Angus Maddison’s data, 
spanning 18200000—2003 (depending on the country), and its updated version through 2006 by the Total 
Economy Database (TED http://www.ggdc.net/). GDP is calculated on the basis of PPP 1990 
International Geary–Khamis DOLLAR. TED contains, among other things, series on levels of real 
GDP, population, and GDP per capita, for up to 125 countries from 1950 to the present. These 
countries represent about 96 % of the world population. As the smaller and poorer countries 
are not in the database, the sample represents an even larger share of world GDP (99 %). We 
do not attempt to include in our study aggregate measures of real economic activity prior to 1800.21 

To calculate a country’s share of world GDP continuously over the years, we sometimes 
found it necessary to interpolate the Maddison data. For most countries, GDP is reported only for 
selected benchmark years (1820, 1850, 1870, etc.). Interpolation took three forms, ranging from the 
best or preferred practice to the most rudimentary. When we had actual data for real GDP (from 
either official sources or other scholars) for periods for which the Maddison data is missing and 
periods for which both series are available, we ran auxiliary regressions of the Maddison GDP 

20 Sevket Pamuk constructs comparable series for Turkey through World War I. 
21 There are exceptions. For instance, Rodney Edvinsson’s careful estimates for Sweden 17200000—2000 or 
HSUS for THE—US beginning in 1790 offer a basis on which to examine earlier economic cycles and 
their relation to crises. 



series on the available GDP series for that particular country. This allowed us to fill in the gaps for 
the Maddison data, thus maintaining cross-country comparability and enabling us to aggregate GDP 
by region or worldwide. When no other measures of GDP were available to fill in the gaps, the 
auxiliary regressions linked the Maddison measure of GDP to other indicators of economic activity, 
such as an output index or, most often, central GOVERNMENT REVENUes—for which we have long 
continuous time series. 22 As a last resort, if no potential regressors were available, interpolation 
simply connected the dots of the missing Maddison data assuming a constant annual growth rate in 
between the reported benchmark years. While this method of interpolation is, of course, useless 
from the vantage point of discerning any cyclical pattern, it still provides a reasonable measure of a 
particular country’s share of world GDP, as this share usually does not change drastically from year 
to year. 

4. Exports 
Though subject to chronic misinvoicing problems,23 the external accounts are most often 
available for longer periods. Misinvoicing not withstanding, those accounts can be considered 
more reliable than many other series of economic activity. The series used in this study are taken 
from the IMF, while the earlier data come primarily from GFD and OXLAD. Official historical 
statistics and assorted academic studies listed 
—IN—DATA Appendix I complement the MAIN databases. 
Trade balances provide a rough measure of the country-specific CAPITAL flow cycle—particularly for 
the earlier periods when data on CAPITAL account balances are nonexistent. Exports are also used to 
scale debt—particularly external debt. 

II. GOVERNMENT Finances and National Accounts 
1. Public finances 
GOVERNMENT finances are primarily taken from Mitchell for the 
1963 pre-period and from 
Kaminsky, Reinhart, and Végh (2004) and sources cited therein for the more RECENT period. The 

22 It is well known that REVENUes are intimately linked to the economic cycle. 
23 See, for example, calculations in the background material to Reinhart and Rogoff (2004), 
http://www.publicpolicy.umd.edu/faculty/reinhart/PartII_Dual.pdf. 



web pages of the central banks and finance ministries of the many countries in our sample provide 
the most up-to-date data. For many of the countries in our sample, particularly in Asia and Africa, 
the time series on central GOVERNMENT REVENUes and expenditures date back to the colonial period. 
Details on individual country coverage are presented in Appendix Table AI.7. In nearly all cases, 
the Mitchell data goes back to the 1800s, enabling us to calculate debt-to-REVENUE ratios for many of 
the earlier crises. 

Richard Bonney’s European State Finance Data Base (ESFDB), which brings together the 
data provided by many authors is an excellent source for the larger European countries for the prei800 
era, as it offers considerable detail on GOVERNMENT REVENUes and expenditures, not to mention 
extensive bibliographical references. 

2. National Accounts 
Besides the standard sources, such as the IMF, UNITED—NATIONS, and World Bank, which 
provide data on national accounts for the post–World War II period (with different starting points 
depending on the country), we consult other multicountry databases such as OXLAD for earlier 
periods. As with other time series used in this study, the constructed national account series 
(usually for pre–World War I) from many scholars around the world, such as Brahmananda for 
India, Baptista for VENEZUELA, and Yousef for Egypt, are incorporated into our collection. 

III. Public Debt and its Composition 
One would think that with at least 250 sovereign external default episodes DURING 1800– 
2007 and at least 70 cases of default on domestic public debt (not to mention the significant 
economic disruption associated with these events), it would be relatively straightforward to find a 
comprehensive long time series on public sector debt.24 Yet, this is not the case—far from it. 
GOVERNMENT debt is among the most elusive of economic time series. 

24 These numbers are a lower bound, since they do not include the many sovereign defaults prior to 1800 and, 
as regards domestic defaults, we have only begun to skim the surface, see Reinhart and Rogoff (2008). 


 For the advanced economies, the most comprehensive data comes from the OECD, which 
provides time series on general GOVERNMENT debt since 1980. However, this data has several 
important limitations: it only includes a handful of emerging markets; 

for many advanced 
economies (France, Finland, Greece, and the U.K., to name a few), the data actually begins much 
later in the 1990s, which cannot be considered as much of a time series; 

and only total debt is 
reported, with no particulars provided of the composition of debt (domestic versus foreign) or its 
maturity (long-term versus short-term). To state that the IMF’s well-known World Economic 
Outlook (WEO) database extends to public debt requires a stretch of the imagination.25 Data is only 
provided for the G-7 from 1980 onward (out of 180 countries covered in the WEO). 

The most comprehensive data on public debt comes from the World Bank’s Global 
Development Finance (GFD, known PREVIOUSLY as the World Debt Tables). It is an improvement 
on the other databases in that it begins (for most countries) in 1970 and it provides extensive detail 
on the particulars of external debt. Yet, GFD also has serious limitations. There are no advanced 
economies included in the database (nor newly-industrialized countries (such as Israel, Korea, or 
Singapore, for that matter) to facilitate comparisons. Unlike data from the IMF and the World 
Bank for exchange rates, prices, GOVERNMENT finances, etc., there is no data prior to 1970. Last, but 
certainly not least, these data only cover external debt. In a few countries, such as Panama or Côte 
D’Ivoire, external debt is a sufficient statistic on GOVERNMENT liabilities, since domestic public debt 
levels are relatively trivial. As Reinhart and Rogoff (2008) illustrate, however, for most countries 
domestic debt accounts for an important share of total GOVERNMENT debt. The all-country average 
oscillates between 40 to 80 % DURING 19000000—2006.26 

In search of the elusive data on total public debt, we examined the archives of the global 
institutions’ predecessor, THE—LEAGUE—OF—Nations, and found that this institution collected 
information on, among other things, public domestic and external debt in its Statistical Yearbook 

25 This description comes from the IMF’s web site: “Download time series data for GDP growth, inflation, 
unemployment, payments balances, exports, imports, external debt, CAPITAL flows, commodity prices, more.” 
26 For some countries, such as THE—NETHERLANDS[NIEDERLANDE], Singapore, and the United States, practically all public debt is 
domestic. 


(1926–1944). While, neither the IMF nor the World Bank continued this practice AFTER THE WAR, the 
newly-formed UNITED—NATIONS (UN) inherited the data collected by THE—LEAGUE—OF—Nations and in 
1948 its DEPARTMENT—OF—Economic Affairs, published a special volume on public debt, spanning 
1914–1946. From that time onwards THE—UN continued to collect and publish the domestic and 
external debt data in the same format as their pre-war predecessor on an annual basis in their 
Statistical Yearbooks. As former colonies became independent nations, the database expanded 
accordingly. This practice continued —UNTIL 1983, at which time the domestic and external public 
debt series were discontinued altogether. In total, these sources yield time series that span 1914– 
1983 for the most complete cases. It covers advanced and developing economies. For the most 
part, it also disaggregated domestic debt into its long-term and short-term components. To the best 
of our knowledge, this data is not available electronically in any database, HENCE it required going 
to the original publications. 
This data provides the starting point for our public debt series, 
which have been (where possible) extended to 
19140000             the period prior to and 
19830000             —POST-. 

For data prior to 1914 (including several countries that were then colonies)27, we consulted 
numerous sources, both country-specific statistical and GOVERNMENT agencies and individual 
scholars. Data Appendix II provides details or the sources by country and time period. When no 
public debt data is available prior to 1914, we proceed to approximate the foreign debt stock by 
reconstructing debt from individual international debt issues. This debenture data also provide a 
proximate measure of gross international CAPITAL inflows. Much of the data come from scholars 
including Lindert and Morton, Marichal, Miller, and Wynne, among others. From these data, we 
construct a foreign debt series (but, not total debt).28 This exercise allows us to examine standard 
debt ratios for default episodes for several newly-independent nations in Latin America as well as 
Greece and important defaults such as that of China in 1921, and Egypt and Turkey in the 1860s– 
1870s. These data are most useful for filling holes in the external debt time series, when countries 

27 For Australia, Ghana, India, Korea, SOUTH—AFRICA, among others, we have put together debt data for much of 
the colonial period. 
28 Flandreau and Zumer (2004) are an important data source for Europe, 1880–1913. 



first tap international CAPITAL markets. Their usefulness (as measures of debt) is acutely affected by 
repeated defaults, write-offs, and debt restructurings that introduce disconnects between the 
amounts of debt issued and the subsequent debt stock.29 

For some countries (or colonies in the earlier period) where we have only relatively RECENT 
data for total public debt, but have reliable data going much further back on central GOVERNMENT 
REVENUes and expenditures, we calculate and cumulate fiscal deficits to provide a rough 
approximation to the debt stock.30 

To update the data for 
19830000            —POST, we mostly rely on GFD for external debt. 
Two very valuable RECENT studies facilitate the update: Jeanne and Guscina (
2006) compile detailed date on the composition of domestic and external debt for 19 important emerging markets for 
19800000—2005; 

Cowan, Levy-Yeyati, Panizza, Sturzenegger (2006) perform a similar exercise for all the 
developing countries of the Western hemisphere for 19800000—2004. Last, but certainly not least, are 
the official GOVERNMENT sources themselves, which are increasingly forthcoming in providing 
domestic debt data, often under the IMF’s 1996 initiative, Special Data Dissemination Standard. 

IV. Global variables 
Global variables (for lack of a better name, since this term is of fairly RECENT vintage) have 
two components: those indicators that are, indeed, global in scope—namely, world commodity 
prices, and country-specific key economic and financial indicators for the world’s financial centers 
DURING 18000000—2007. For commodity prices, we have time series since the late 1700s from four 
different sources (see Data Appendix I). The key economic indicators include the current account 
deficit, real and nominal GDP, and short- and long-term interest rates for the relevant financial 
center —OF—THE—TIME (i.e., the U.K. prior to World War I and the U.S, subsequently). 

V. Varieties of Economic Crises and their Dates 
29 Even under these circumstances, they continue to be a useful measure of gross CAPITAL inflows, as there was 
relatively little private external borrowing nor bank lending in the earlier sample. 
30 Indonesia prior to 1972 is a good example where this exercise was particularly useful. 



To identify crisis episodes, we used two approaches, one is quantitative in nature while the 
other is based on a chronology of events. This approach is similar to that followed in Kaminsky 
and Reinhart (1999), who used quantitative thresholds to date currency crises and events to date 
banking crises. Details follow. 

1. Crises defined by quantitative thresholds: currency crashes, debasement, and inflation 
Inflation crises 

Since we want to study the incidence of expropriation in its various forms, we are not only 
interested in dating the beginning of an inflation or currency crisis episode but its duration as well. 
Many of the high-inflation spells can be best described as chronic—lasting many years. In our 
earlier work (Reinhart and Rogoff, 2004), which classified exchange rate arrangements for the 
post–World War II period, we used a 12-month inflation threshold of 40 or higher % to define 
a “freely falling” episode. In this study, which spans a much longer period before the widespread 
creation of fiat currency, inflation rates well below 40 % PER—ANNUM were considered as 
inflation crises. Thus, we adopt an inflation threshold of 20 % PER—ANNUM. Median inflation 
rates before World War I were well below those of the more RECENT period: 0.5 for 15000000—1799; 


0.71 for 1800–1913; 

and 5.0 for 19140000—2006. Furthermore, as the last column of Table A1 
highlights, hyperinflations are of modern vintage, with Hungary 1946 holding the record in our 
sample. 
Currency crashes 

To date currency crashes, we follow a variant of Frankel and Rose (1996), who focus 
exclusively on the exchange rate depreciation. This definition is the most parsimonious, as it does 
not rely on other variables such as reserve losses and interest rate hikes. Mirroring our treatment of 
inflation episodes, we are not only concerned here with the dating of the initial crash (as in Frankel 
and Rose, 1996 and Kaminsky and Reinhart, 1999) but with the full period in which annual 
depreciations exceed the threshold. Hardly surprising, the largest crashes shown in Table A1 are 


similar in timing and orders of magnitudes as the inflation profile. The “honor” of the record 
currency crash, however, goes to Greece in 1944. 


Table A1. Defining Crises: A Summary of Quantitative Thresholds 

Crisis type Threshold Period Maximum Inflation An annual inflation rate 20 % or higher. We 15000000—1790 173.1 
also examine separately the incidence of more extreme cases where inflation exceeds 40 % PER—ANNUM. 
1800–1913 
19140000—2006 
159.6 
9.63E+26 
Currency An annual depreciation versus THE—US $ (or the 1800–1913 275.7 crashes relevant anchor currency—historically the UK 
pound, the French franc, or THE—GERMAN—DM and 19140000—2006 3.37E+09 presently the euro) of 15 % or more. 
Currency A reduction in the metallic content of coins in 12580000—17990000 –56.8 debasement: 
Type I 
circulation of 5 % or more. 1800–1913 –55.0 
Currency A currency reform where a new currency replaces a The most extreme episode in our 
debasement: much-depreciated earlier currency in circulation. 
sample is the 
1948 Chinese Type II conversion at a rate of 3.000.000 to 1.000.000

Currency debasement 

The predecessor of modern inflation and foreign exchange rate crises was currency 
debasement DURING the long era when the principal means of exchange were metallic coins. 
Debasements were particularly frequent and large DURING wars. Indeed, drastic reductions in the 
silver content of the currency provided many sovereigns with their most important source of 
financing. Figure A1, which depicts the track record for Russia and Austria DURING the Napolenic 
Wars, is indeed representative of many more episodes. 

Finally, we also date currency “reforms” or conversions and their magnitudes. 
Such conversions form a part of every hyperinflation episode in our sample, 
in effect, it is not unusual to have several conversions in quick succession. 
For example, 
19860000—19940000    in its struggle with hyperinflation, Brazil had no less than 4 conversions 
However, when it comes to the 
magnitude of a single conversion, the record holder is China in 1948, with a conversion rate of 
three-million to one. Conversions also follow spells of high (but not necessarily hyper inflation), 
and these cases are also included in our list of modern debasements. 


Figure A1. Changes in the silver Content of the Currency: Austria and 
Russia DURING the Napolenic Wars, 17990000—1815 
1765 1770 1775 1780 1785 1790 1795 1800 1805 1810 1815 

The same quantitative methodology could be applied to date the burst of asset price bubbles 
(equity or real estate) that are so commonplace in the run-up to banking crises, but we do not 
encompass such episodes in this study and leave it for future research.31 

2. Crises defined by events: banking crises and domestic and external debt defaults 
In this section, we describe the criteria used in this study to date banking crises, external 
debt crises, and their little known or understood domestic debt crises counterparts. 

Banking Crises 

With regard to banking crises, our analysis stresses events. 
The MAIN reason for following this approach has to do with the lack of long time series data that allows us to date banking or 

31 See Kaminsky and Reinhart (1999) for the construction of thresholds to date equity price crashes and 
Reinhart and Rogoff (2008) for a depiction of the behavior of real estate prices ABEND—DES—AM banking crises in 
industrialized economies. 

-60 
-50 
-40 
-30 
-20 
-10 
0 
10 
20 
Per cent 
Ru ssian 
ru b le 
Austria n 
kre u ze r 
i

financial crises quantitatively along the lines of inflation or currency crashes. For example, the 
relative price of bank stocks (or financial institutions relative to the market) would be a logical 
indicator to examine. However, this is problematic, particularly for the earlier part of our sample as 
well as for developing countries (where many domestic banks do not have publicly traded equity). 

 If the beginning of a banking crisis is marked by bank runs and withdrawals, then changes 
in bank deposits could be used to date the crises. This indicator would have certainly done well in 
dating the numerous banking panics of the 1800s. Often, however, THE—BANKing problems do not 
arise from the liability side, but from a protracted deterioration in asset quality, be it from a collapse 
in real estate prices or increased bankruptcies in the nonfinancial sector. In this case, a large 
increase in bankruptcies or nonperforming loans could be used to MARK the onset of the crisis. 
Indicators of business failures and nonperforming loans are also usually available sporadically, if at 
all; 

the latter are also made less informative by banks’ desire to hide their problems for as long as 
possible. 

Given these data limitations, we MARK a banking crisis by two types of events: (1) bank runs 
that lead to the closure, merging, or takeover by the public sector of one or more financial 
institutions (as in VENEZUELA in 1993 or Argentina in 2001); 

and (2) if there are no runs, the closure, 
merging, takeover, or large-scale GOVERNMENT assistance of an important financial institution (or 
group of institutions), that marks the start of a string of similar outcomes for other financial 
institutions (as in Thailand 1996–97). We rely on existing studies of banking crises and on the 
financial press; 

ACCORDING—TO—these studies the fragility of THE—BANKing sector was widespread DURING 
these periods. 

Many country-specific studies (such as Camprubi, 1957, for Peru; 

Cheng, 2003, and 
McElderry, 1976, for China; 

and Noel, 2002, for Mexico) pick up banking crisis episodes not 
covered by the multicountry literature and contribute importantly to this chronology, but the MAIN 
sources for cross-country dating of crises are as follows: 
1970 —POST For  , the comprehensive and well-known study by Caprio and Klingebiel—which the authors updated through 2003—is 
authoritative, especially when it comes to classifying banking crises into systemic or more benign categories; 

Kaminsky and Reinhart (1999), and Jacome (2008) for Latin America round out the 
sources. For pre–World War II, Kindleberger (1989), Bordo et al. (2001), and Willis (1926) 
provide multicountry coverage on banking crises. 

We relegate a summary discussion of the limitations of this event-based dating approach to 
Table A2, while the years in which THE—BANKing crises began are listed in Table A3—unfortunately, 
for many of the early episodes it is difficult to ascertain how long the crisis lasted. 

Table A2. Defining Crises by Events: A Summary 

Type of Crisis Definition and/or Criteria Comments 
Banking crisis 
Type I: 
systemic/severe 
Type II: 
financial 
distress/ milder 
We MARK a banking crisis by two types of events: (1) bank runs that lead to the 
closure, merging, or takeover by the public sector of one or more financial 
institutions; 

and (2) if there are no runs, the closure, merging, takeover, or 
large-scale GOVERNMENT assistance of an important financial institution (or group 
of institutions), that marks the start of a string of similar outcomes for other financial institutions. 
This approach to dating the beginning of a banking crisis is not without drawbacks. 
It could date a crisis too late, because the financial problems usually begin well before 
a bank is finally closed or merged; 

it could also date a crisis too early, because the worst part of a crisis may come later. 
Unlike the external debt crises (see below), which have well-defined closure dates, it is often difficult or impossible to accurately pinpoint the year in which a crisis ended. 

Debt crises: 
External 

Debt crisis: 
Domestic 

A sovereign default is defined as the failure to meet a principal or interest payment on the due date (or within the specified grace period). The episodes 
also include instances where rescheduled debt is ultimately extinguished in terms less favorable than the original obligation. 
The definition given above for external debt applies. In addition, domestic debt crises have involved the freezing of bank deposits and or forcible 
conversions of such deposits from $ to local currency. 

While THE—TIME—OFdefault is accurately classified as a crisis year there are a large number of cases where the final resolution 
with the creditors (if it ever did take place) 
seems interminable. Fort his reason we also work with a crisis dummy that only picks up the 1. year. 

There is at best some partial documentation of RECENT defaults on domestic debt provided by Standard and Poors. Historically, it is very 
difficult to date these episodes and in many cases (like banking crises) it is impossible to ascertain the date of the final resolution. 


External Debt Crises 

External debt crises involve outright default on payment of external debt obligations 
(Argentina 2001 holds the record for the largest default), repudiation (as in Mexico 1867, when an 
over 100.000.000 pesos of debt, issued by Maximillian, was repudiated by the Juarez GOVERNMENT), 
or the restructuring of debt into terms less favorable to the lender than those in the original contract 
(for instance, India’s little-known external restructurings in 1958–1972. 

These events have received considerable attention in the academic literature from leading 
modern-day economic historians, such as Michael Bordo, Barry Eichengreen, Marc Flandreau, 
Lindert and Morton, and Alan Taylor.32 Relative to early banking crises (not to mention domestic 
debt crises—which have been all but ignored in the literature) much is known about the causes and 
consequences of these rather dramatic episodes. The dates of sovereign defaults and restructurings 
are those listed in Tables 2–5. For post-1824, the dates come from several Standard and Poors studies. However, these are incomplete, missing numerous post-war restructurings and early 
defaults so this source has been supplemented with additional information from Lindert and Morton 
(1989), MacDonald (2003), Purcell and Kaufman (1993), Suter (1992), and Tomz (2007). Of 
course, required reading in this field includes Winkler (1933) and Wynne (1951). 
Methodology 

While THE—TIME—OFdefault is accurately classified as a crisis year there are a large number of 
cases where the final resolution with the creditors (if it ever did take place) seems interminable. 
Russia’s default following the revolution holds the record, lasting 69 years. Greece’s default in 
1826 shut it out from international CAPITAL markets for 53 consecutive years, while Honduras’s 1873 
default had a comparable duration.33 Looking at the full default episode is, of course, useful for 
characterizing the borrowing/default cycles, calculating hazard rates, etc. But it is hardly credible 
that a spell of 53 years could be considered a crisis—even if they weren’t exactly prosperous years. 
Thus, in addition to constructing the country-specific dummy variables to cover the entire episode, 

32 This is not meant to be an exhaustive list of the scholars that have worked on historical sovereign defaults. 
33 At present, Honduras remains in default since 1981, 27 years. 


we also employ two other qualitative variables. 
The 1. of these only enters as a crisis the year of default; 

while the 2. creates a seven-year window centered on the default date. The rationale is 
that neither the three years that precede a default nor the three years that follow it can be considered 
a “normal” or “tranquil” period. As in Reinhart and Rogoff (2008), this allows us to analyze the 
behavior of various economic and financial indicators surrounding the crisis. 

Domestic Debt Crises 

Information on domestic debt crises is scarce but it is not because these crises do not take 
place. Indeed, as Reinhart and Rogoff (2008) show, domestic debt crises typically take place 
against much worse economic conditions than the average external default. Usually domestic debt 
crises do not involve external creditors, perhaps this may help explain why so many episodes go 
unnoticed. Of course, this is not always the case, Mexico’s much-publicized near-default in 1994– 1995 would have been a “famous” domestic default crisis indeed, since the dollar-linked 
GOVERNMENT debt, the Tesebonos, that were on the verge of default, were issued under domestic law and were part of domestic debt. One can only speculate that if the Tesobonos had not been so 
widely held by nonresidents, perhaps this crisis would have received less attention. Since 1980, Argentina has defaulted three times on its domestic debt—the two defaults that coincided with 
defaults in external debt (1982 and 2001) did attract considerable international attention. The largescale 1989 default that did not involve a new default on external debt and did not involve 
nonresidents is scarcely known in the literature. Even the many defaults on domestic debt DURING the Great Depression in advanced economies and developing ones are not well documented. 

Another feature that characterizes domestic defaults is that references to arrears or suspension of payments on domestic debt are often relegated to footnotes. Lastly, some of the 
domestic defaults that involved the forcible conversion of foreign currency deposits into local currency have occurred DURING banking crises, hyperinflations, or a combination of the two 
(Bolivia, Peru, and Argentina are in this list). 


The approach toward constructing categorical variables follows that PREVIOUSLY described for external debt default. Like banking crises and unlike external debt defaults, for many episodes 
of domestic default the endpoint for the crisis is not well known. 


Table A3. Banking Crises Dates and CAPITAL Mobility: 18000000—2007 

High-Income Middle Income Low Income 
Country (ies) BeginningYear Country (ies) BeginningYear Country (ies) BeginningYear 


CAPITAL Mobility: Low-Moderate, 18000000—1879 

France 1802 

France 1805 

UK 
1810 

UK 
1815 

Denmark 1813 

US 
1818 

UK, US 1825 

US 
1836 

Canada, UK 1837 

UK 
1847 

Belgium 1848 

UK, US 1857 
India 1863 

ITALY, UK 1866 

Austria, US 1873 Peru 1873 
SOUTH—AFRICA 1877 

CAPITAL Mobility: High, 18800000—1914 

Germany 1880 

France 1882 
Mexico 1883 

US 
1884 

Denmark 1885 

ITALY 
1887 

1889 France 

Portugal, UK, 1890 
Argentina, 1890 

US 
Brazil, Chile, 
SOUTH—AFRICA 

Germany, 1891 

ITALY, Portugal 

Australia 1893 

NETHERLANDS[NIEDERLANDE], 1897 

Sweden 

Norway 1898 
Chile 1899 

Finland 1900 
Brazil 1900 

Germany, 1901 

Japan 

Denmark, 1907 
Mexico 1907 

France, ITALY, 

Japan, Sweden, 

US 
Chile 1908 
Mexico 1913 India 1913 

Belgium, 1914 
Argentina, 1914 

France, ITALY, 
Brazil 

Japan, 

NETHERLANDS[NIEDERLANDE], 

Norway, UK, 

US 
CAPITAL Mobility: Low, 19150000—1919 
Chile 1915 


Table A3. Banking Crises Dates and CAPITAL Mobility: 18000000—2007 (continued) 

High Income Middle Income Low Income 
Country (ies) BeginningYear Country (ies) BeginningYear Country (ies) BeginningYear 


CAPITAL Mobility: Moderate, 1920–1929 

Portugal 1920 Mexico 1920 
Finland, ITALY, 1921 India 1921 
NETHERLANDS[NIEDERLANDE], 
Norway 
Canada, Japan, 1923 China 1923 
Taiwan 
Austria 1924 
Belgium, 1925 Brazil, Chile 1926 
Germany 
Japan, Taiwan 1927 
US 1929 Brazil, Mexico 1929 India 1929 

CAPITAL Mobility: Low, 1930–1969 

France, ITALY 1930 

Belgium, 1931 
Argentina, 1931 

Finland, 
Brazil, China 

Germany, 

Greece, 

Portugal, 

Spain, Sweden 

Belgium 1934 
Argentina, 1934 
China 

ITALY 1935 
Brazil 1937 

Belgium, 1939 

Finland 
India 1947 
Brazil 1963 

CAPITAL Mobility: Moderate, 1970–1979

 Uruguay 1971 
UK 1974 Chile 1976 Central African 1976 
Republic 
Germany, 1977 SOUTH—AFRICA 1977

Israel, Spain 

 19780000             VENEZUELA 


Table A3. Banking Crises Dates and CAPITAL Mobility: 18000000—2007 (continued) 

High-Income Middle Income Low Income 
Country (ies) BeginningYear Country (ies) BeginningYear Country (ies) BeginningYear 


CAPITAL Mobility: High, 19800000—2007 
Argentina, 1980 
Chile, 
Ecuador, 
Egypt, 
Mexico, 1981 
Philippines 
Uruguay 
Hong Kong, 1982 Colombia, 1982 
Singapore Turkey 
Canada, Korea, 1983 Morocco, 1983 
Kuwait Peru, 
Taiwan Thailand 
UK, US 1984 
Argentina, 1985 
Brazil, 
Malaysia 
Denmark, New 1987 Bolivia, 1987 
Zealand, Cameroon, 
Norway Costa Rica, 
Nicaragua 
Lebanon, 1988 
Panama 
Australia 1989 Argentina, El 1989 
Salvador, 
SOUTH—AFRICA, 
Sri Lanka 
ITALY 1990 Algeria, 1990 
Brazil, Egypt, 
Romania 
Czech 1991 Georgia, 1991 
Republic, Hungary, 
Finland, Poland, 
Greece, Slovak 
Sweden, UK Republic 
Japan 1992 Albania, 1992Bosnia-
Herzegovina, 
estonia, 
Indonesia 
Slovenia, Cape Verde, 1993 
Macedonia VENEZUELA 

Congo (Dem. 1982 
Rep.), Ghana 
Equatorial 1983 
Guinea, Niger 

Mauritania 1984 
Guinea, 1985 
Kenya 

1986 
Bangladesh, 1987 
Mali, 
Mozambique, 
Tanzania 
Benin, 1988 
Burkina Faso, 
Central 
African 
Republic, 
Cote D’Ivoire, 
Madagascar, 
Nepal, 
Senegal 

Sierra Leone 1990 

Djbouti, 1991 
Liberia, Sao 
Tome 

 Angola, Chad, 1992 

China, Congo, 
Kenya, Nigeria 

Guinea, 1993 
Eritrea, India, 
Kirgyz 
Republic, 
Togo 


Table A3. Banking Crises Dates and CAPITAL Mobility: 18000000—2007 (continued) 

High Income Middle Income Low Income 
Country (ies) BeginningYear Country (ies) BeginningYear Country (ies) BeginningYear 


CAPITAL Mobility: High, 19800000—2007 
France 1994 Armenia, 1994 Burundi, 1994 

Bolivia, Congo (Rep.), 

Bulgaria, Uganda 

Costa Rica, 

Jamaica, 

Latvia, 

Mexico, 

Turkey 
UK 1995 Argentina, 1995 Guinea-Bissau, 1995 

Azerbaijan, Zambia, 

Brazil, Zimbabwe 

Cameroon, 

Lithuania, 

Paraguay, 

Russia, 

Swaziland, 

Croatia, 1996 Myanmar 1996 

Ecuador, Yemen 

Thailand 
Taiwan 1997 Indonesia, 1997 Vietnam 1997 

Korea, 

Malaysia, 

Mauritius, 

Philippines, 

UKRAINE 

Colombia, 1998 

Ecuador, 

El Salvador 

Russia 

Bolivia, 1999 

Honduras, Peru 

Nicaragua 2000 

Argentina, 2001 

Guatemala 

Paraguay 2002 

Uruguay 

Dominican 2003 

Republic 

Guatemala 2006 
US 2007 


Data Appendix I. Macro Time Series 

This data appendix covers the macro time series used, while a separate appendix is devoted 
to the database on GOVERNMENT debt. 

Abbreviations of Frequently-used Sources (Additional sources listed in tables below) 

DIA: Diaz (et. al) 
ESFDB: European State Finance Data Base 
GFD: Global Financial Data 
GPIHG: Global Price and Income History Group 
IISH: International Institute of Social History 
IFS: International Financial Statistics, IMF. 
KRV: Kaminsky, Reinhart, and Vegh 
MAD: Maddison 
MIT: Mitchell 
OXF: Oxford Latin American History Database 
RR: Reinhart and Rogoff 
TED: Total Economy Database 
WEO: World Economic Outlook, IMF 

List of Tables 

Table AI.1 Prices: Consumer or cost-of-living indices 
Table AI.2 Modern Nominal Exchange Rates 
Table AI.3 Early Silver-based Exchange Rates 
Table AI.4 Silver Content of Currencies 
Table AI.5 Nominal and Real Gross National Product and Output Index 
Table AI.6 Gross National Product: PPP in constant $ 
Table AI.7 Central GOVERNMENT Expenditure and REVENUE 
Table AI.8 Total Exports and Imports 
Table AI. 9 Global Indicators and Financial Centers 


Table AI. 
1 PRICES: Consumer or cost-of-living indices 
(unless otherwise noted) 

COUNTRY PERIOD COVERED SOURCES COMMENTARY 
Algeria 18690000—1884 Hoffman et. al. Wheat prices 
19380000—2007 GFD/WEO 
Angola 19140000—1962 MIT 
19910000—2007 WEO 
Argentina 17750000—1812 Gardner Buenos Aires only 
18640000—1940 WILLIAMSON et. al. 
18840000—1913 Flandreau & Zumer 
19000000—2000 OXF 
19130000—2000 Diaz et. al. 
19130000—2007 GFD/WEO 
Australia 18180000—1850 Butlin, Vanplew, New Wales, food prices 
GPIHG 
18500000—1983 Shergold, GPIHG Sidney, food 
18610000—2007 GFD/WEO 
Austria 14400000—1800 Allen Vienna 
18000000—1914 Hoffman et. al. Wheat prices 
18800000—1913 Flandreau & Zumer 
19190000—2007 GFD/WEO 
Belgium 14620000—1913 Allen Antwerp 
18350000—2007 GFD/WEO 
Bolivia 19360000—2007 GFD/WEO 
Brazil 17630000—1820 Garner Rio de Janeiro only 
18300000—1937 WILLIAMSON et. al. Rio de Janeiro only 
18610000—2000 Diaz et. al. 
19120000—2007 GFD/WEO 
Canada 18670000—1975 CanStat 
19100000—2007 GFD/WEO 
Central African 19560000—1993 MIT 
Republic 
19800000—2007 WEO 
Chile 17540000—1806 Garner Santiago only 
18100000—2000 Diaz e. al. 
19000000—2000 OXF 
19130000—2007 GFD/WEO 
China 15000000—1910 Peng Rice prices 
16380000—1936 Wang Rice prices 
18670000—1935 Hsu Wholesale prices 
19260000—1948, GFD/WEO 
19780000—20070000    0000     
Colombia 18630000—1940 WILLIAMSON et. al. 
19000000—2000 OXF 
19230000—2007 GFD/WEO 
Costa Rica 19370000—2007 GFD/WEO 
Cote D’Ivoire 19510000—2007 GFD/WEO 
Denmark 17480000—1800 Hoffman et. al. Wheat prices 
18150000—2007 GFD/WEO 
Dominican Republic 19420000—2000 OXF 
19800000—2007 WEO 
Ecuador 19390000—2007 GFD/WEO 


Table AI.1 Prices: Consumer or cost-of-living indices-continued 
(unless otherwise noted) 

Country Period covered Sources Commentary 
Egypt 18590000—1941 WILL 
19130000—2007 GFD/WEO 
19150000—1999 GFD 
El Salvador 19370000—2000 OXD 
19800000—2007 WEO 
Finland 18600000—2001 Finnish Historical 
National Accounts 
19800000—2007 WEO 
France 14310000—1786 Allen 
18400000—1913 
18070000—1935 Dick Retail prices 
18400000—2007 GFD/WEO 
Germany 14270000—1765 Allen Munich 
16370000—1855 Hoffman et. al. Wheat prices 
18200000—2007 GFD/WEO 
Greece 18330000—19380000 Kostelenos et. al. GDP deflator 
19220000—2007 GFD/WEO 
Ghana 19490000—2007 GFD/WEO 
Guatemala 19380000—2000 OXD 
19800000—2007 WEO 
Honduras 19380000—2000 OXD 
19800000—2007 WEO 
Hungary 19230000—2007 GFD/WEO 
India 18660000—2000 Diaz et. al. 
18730000—1939 WILLIAMSON et. al. 
19130000—2007 GFD/WEO 
Indonesia 18200000—1940 WILLIAMSON et. al. 
19480000—2007 GFD/WEO 
ITALY 15480000—1645 Allen Naples 
17340000—1806 
17010000—18600000 DeMaddalena Wheat prices, Milan 
18610000—2007 GFD/WEO 
Korea 16900000—1909 Jun & Lewis Rice prices in the 
southern region of 
Korea 
19060000—1939 WILLIAMSON et. al. 
19480000—2007 GFD/WEO 
Japan 16000000—1650 Kimura Rice prices, Osaka 
18180000—1871 Bunko Rice prices, Osaka 
18600000—1935 WILLIAMSON et. al. 
19000000—2007 GFD/WEO 
Kenya 19470000—2007 GFD/WEO 
Malaysia 19480000—2007 GFD/WEO 
Mauritius 19460000—2007 GFD/WEO 


Table AI.
1 Prices: 
of 
CONSUMER—INDEX[COST—OF—LIVING—INDEX]
 INDICEs-continued (unless otherwise noted) 

Country                Period covered            Sources                 Commentary 

17860000—18210000    Mexico CONSUMER—INDEX[COST—OF—LIVING—INDEX]Garner Zacatecas 
18770000—19400000    Mexico CONSUMER—INDEX[COST—OF—LIVING—INDEX]WILLIAMSON et. al. 
19180000—20070000    Mexico CONSUMER—INDEX[COST—OF—LIVING—INDEX]GFD/WEO 
19390000-20070000    Morocco CONSUMER—INDEX[COST—OF—LIVING—INDEX]GFD/WEO 
18700000—19400000    Myanmar (Burma CONSUMER—INDEX[COST—OF—LIVING—INDEX]WILLIAMSON et. al. 
19390000—20070000    GFD/WEO 
15000000—18000000    NETHERLANDS[NIEDERLANDE] CONSUMER—INDEX[COST—OF—LIVING—INDEX]Van Zanden 
18000000—19130000    Van Riel 
18800000—2007 GFD/WEO 
18570000—2004 New Zealand CONSUMER—INDEX[COST—OF—LIVING—INDEX]Statistics New Zealand 
19800000—2007 WEO 
19370000—2007 Nicaragua CONSUMER—INDEX[COST—OF—LIVING—INDEX]GFD/WEO 
#19530000—20070000    NIGERIA CONSUMER—INDEX[COST—OF—LIVING—INDEX] GFD/WEO 
15160000—2005 Norway  CONSUMER—INDEX[COST—OF—LIVING—INDEX]Grytten 
19800000—2007 WEO 
19390000—2000 Panama CONSUMER—INDEX[COST—OF—LIVING—INDEX] OXD 
19800000—2007 WEO 
19380000—2007 Paraguay CONSUMER—INDEX[COST—OF—LIVING—INDEX]GFD/WEO 
17500000—1816 Garner Potosi 
17900000—1841 Peru CONSUMER—INDEX[COST—OF—LIVING—INDEX]Garner Lima 
18000000—1873 Diaz et. al. 
19130000—2000 
19800000—2007 WEO 
18990000—1940 Philippines CONSUMER—INDEX[COST—OF—LIVING—INDEX]WILLIAMSON et. al. 
19370000—2007 GFD/WEO 
17010000—1815 Poland Hoffman et. al. Oats Prices-Warsaw 
18160000—1914 Allen Warsaw 
19210000—1939 GFD/WEO 
19830000—2007 
17280000—1893 Portugal  Wheat prices Hoffman et. al.
18810000—1997 Bordo et. al. 
19800000—2007 WEO 
17790000—1831 Hoffman et. alRomania . Wheat prices, Wallachia 
19710000—2007 WEO 
18530000—1910 Borodkin Russia Wheat and rye flour, S—Petesburg 
18800000—1913 Flandreau & Zumer 
19170000—1924, 19270000—1940, GFD/WEO 
19440000—1972, 19910000—2007 
19480000—2007 Singapore GFD/WEO 
18950000—2007 SOUTH—AFRICA GFD/WEO 
15000000—1650 Hamilton Spain Valencia 
16510000—1800 Hamilton, Hoffman et. Wheat, eggs, and linen al. prices 
18000000—2000 Diaz et. al. 
19800000—2000 WEO 
19390000—2007 Sri Lanka GFD/WEO 


Table AI.1 Prices: Consumer or cost-of-living indices-concluded 
(unless otherwise noted) 

Country Period covered Sources Commentary 
Sweden 17320000—1800 Hoffman et. al. Wheat prices 
18000000—2000 Edvinsson 
19800000—2007 WEO 
Taiwan 18970000—1939 WILLIAMSON et. al. 
19800000—2007 WEO 
Thailand (Siam) 18200000—1941 WILLIAMSON et. al. 
19480000—2007 GFD/WEO 
Tunisia 19390000—2007 GFD/WEO 
Turkey 14690000—1914 Pamuk Istanbul 
18540000—1941 WILLIAMSON et. al. 
19220000—2007 GFD/WEO 
United Kingdom 14500000—1999 Van Zanden Southern England 
17810000—2007 GFD/WEO 
United States 17200000—1789 Historical Statistics of Wholesale prices 
the United States 
17740000—2003 Historical Statistics of 
the United States 
19800000—2007 WEO 
Uruguay 18700000—1940 WILLIAMSON et. al. 
19290000—2000 OXF 
19800000—2007 WEO 
VENEZUELA 18300000—2002 Baptista 
19140000—2007 GFD/WEO 
Zambia 19380000—2007 GFD/WEO 
Zimbabwe 19200000—1970 Mitchell 
19300000—2007 GFD/WEO 


Table AI.2 Modern Nominal Exchange Rates 
(Domestic currency units per US $ and other currencies noted) 


Country Period covered Source Other relevant rates 
Algeria 18310000—2007 GFD/IFS French francs/euro 
Angola 19210000—2007 GFD/IFS 
Argentina 18800000—1913 Flandreau & Zumer French francs 
18850000—2007 GFD/IFS 
Australia 18350000—2007 GFD/IFS UK pound 
Austria 18140000—2007 GFD/IFS UK pound, German DM 
Belgium 18300000—2007 GFD/IFS French francs 
Bolivia 18630000—2007 GFD/IFS 
Brazil 18120000—2007 GFD/IFS UK pound 
Canada 18580000—2007 GFD/IFS UK pound 
Central African 19000000—2007 GFD/IFS French francs 
Republic 
Chile 18300000—1995 Braun et. al. UK pound 
18780000—2007 GFD/IFS 
China 18480000—2007 GFD/IFS UK pound 
Colombia 19000000—2000 OXF UK pound 
19190000—2007 GDF/IFS 
Costa Rica 19210000—2007 GDF/IFS 
Denmark 18640000—2007 GDF/IFS UK pound, German DM 
Dominican Republic 19050000—2007 GDF/IFS 
Ecuador 18980000—2000 OXF, Picks 
19800000—2007 IFS 
Egypt 18690000—2007 GFD/IFS UK pound 
El Salvador 18700000—2007 GFD/IFS 
Finland 19000000—2007 GFD/IFS German DM 
France 16190000—1810 ESTDB/Course of the UK pound 
Exchange 
18000000—2007 GFD/IFS UK pound, German DM 
Germany 16980000—1810 ESTDB/Course of the UK pound 
Exchange 
17950000—2007 GFD/IFS 
Greece 18720000—1939 Lazaretou UK pound, German DM 
19010000—2007 GFD/IFS 
Guatemala 19000000—2007 GFD/IFS 
Honduras 18700000—2007 GFD/IFS 
Hungary 19000000—2007 GFD/IFS Austrian SCHILLING 
India 18230000—2007 GFD/IFS UK pound 
Indonesia 18760000—2007 GFD/IFS Dutch guilder 
ITALY 18160000—2007 GFD/IFS UK pound, German DM 
Japan 18620000—2007 GFD/IFS UK pound 
Kenya 18980000—2007 GFD/IFS UK pound 
Korea 19050000—2007 GFD/IFS Japanese yen 
Malaysia 19000000—2007 GFD/IFS UK pound 
Mauritius 19000000—2007 GFD/IFS UK pound 
Mexico 18140000—2007 GFD/IFS UK pound, French franc 
18230000—1999 GFD 


Table AI.2 Modern Nominal Exchange Rates-concluded 
(Domestic currency units per US $ and other currencies noted) 

Country Period covered Source Other relevant rates 
Morocco 18970000—2007 GFD/IFS French franc/euro 
Myanmar (Burma) 19000000—2007 GFD/IFS UK pound 
NETHERLANDS[NIEDERLANDE]16980000—1810 ESTDB/Course of the UK pound 
Exchange 
17920000—2007 GFD/IFS German DM 
New Zealand 18920000—2007 GFD/IFS UK pound 
Nicaragua 19120000—2007 GFD/IFS 
Nigeria 19000000—2007 GFD/IFS UK pound 
Norway 18190000—2007 GFD/IFS Swedish krona, German 
DM 
Panama 19000000—2007 GFD/IFS 
Paraguay 19000000—2000 OXD Argentine peso 
19800000—2007 IFS 
Peru 18830000—2007 GFD/IFS UK pound 
Philippines 18930000—2007 GFD/IFS Spanish peseta 
Poland 19160000—2007 GFD/IFS 
Portugal 17500000—1865 Course of the Exchange Dutch grooten 
17940000—2007 GDF/IFS UK pound, German DM 
Romania 19210000—2007 GDF/IFS 
Russia 18150000—2007 GFD/IFS French franc 
18140000—2007 GFD 
19000000—2000 OXF/IFS 
Singapore 18340000—2007 GFD/IFS UK pound 
SOUTH—AFRICA 19000000—2007 GFD/IFS UK pound 
Spain 18140000—2007 GFD/IFS German DM 
Sri Lanka 19000000—2007 GFD/IFS UK pound 
Sweden 18140000—2007 GDF/IFS UK pound, German DM 
Taiwan 18950000—2007 GDF/IFS UK pound, Japanese yen 
Thailand (Siam) 18590000—2007 GFD/IFS UK pound 
Tunisia 19000000—2007 GFD/IFS French franc 
Turkey 18590000—2007 GFD/IFS UK pound 
United Kingdom 16190000—1810 ESTDB/Course of the French franc 
Exchange 
16600000—2007 GFD/IFS 
United States 16600000—2007 GFD/IFS 
Uruguay 19000000—2007 GFD/IFS 
VENEZUELA 19000000—2007 GFD/IFS 
Zambia 19000000—2007 GFD/IFS UK pound 
Zimbabwe 19000000—2007 GFD/IFS UK pound 


Table AI.3 Early Silver-based Exchange Rates 
(Domestic currency units per UK pence) 

Country Period Covered Source Currency/Commentary 
Austria 13710000—18600000 RR, Table A4 kreuzer, Vienna 
Belgium 13490000—1801 RR, Table A4 Hoet 
France 12580000—1789 RR, Table A4 livre tournois 
Germany 13500000—18300000 RR, Table A4 composite pfenning 
ITALY 12890000—1858 RR, Table A4 lira fiorentina 
NETHERLANDS[NIEDERLANDE]13660000—1800 RR, Table A4 Composite 
Portugal 17500000—1855 RR, Table A4 REIS 
Russia 17610000—1840 RR, Table A4 common ruble 
Spain 13510000—1809 RR, Table A4 Composite 
Sweden 1523-1573 RR, Table A4 MARK ortug 
Turkey 15550000—1914 RR, Table A4 Akche 

Table AI.4 Silver Content of Currencies 

Country Period Covered Sources Currency/Commentary 
Austria 13710000—18600000 ALLEN—DULLES ,Unger kreuzer, Vienna 
Belgium 13490000—1801 Korthals Hoet 
France 12580000—1789 ALLEN—DULLES ,Unger livre tournois 
Germany 13500000—1798 ALLEN—DULLES ,Unger pfenning/Frankfurt 
14170000—18300000 pfenning/AUGSBURG 
ITALY 12890000—1858 Malanima lira fiorentina 
NETHERLANDS[NIEDERLANDE]1366-1575 ALLEN—DULLES ,Unger Flemish grote 
14500000—1800 Van Zanden Guilder 
Portugal 17500000—1855 Godinho REIS 
Russia 17610000—1840 Lindert, Mironov common ruble 
17610000—1815 Assignatzia 
Spain 13510000—1650 ALLEN—DULLES ,Unger dinar, Valencia 
15010000—1800 vellon MARAVEDI, New 
CasTILLE 
16300000—1809 Montfort Real 
Sweden 1523-1573 Soderberg MARK ortug 
Turkey 15550000—1914 Ozmukur & Pamuk Akche 
United Kindgom 12610000—1918 ALLEN—DULLES ,Unger PENCE 
United States 18000000—1979 ALLEN—DULLES ,Unger $ 


Table AI.5 Nominal and Real Gross National Product and Output Index 
(Domestic currency units) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Algeria 19500000—2007 GFD/WEO/IFS 
Angola 19620000—2007 GFD/WEO/IFS 
19690000—2007 
Argentina 18840000—1913 Flandreau & Zumer Nominal 
18750000—2000 Diaz et. al. Index of total PRODUCTION 
(1995=100) 
19000000—2000 OXF Real (base=1970) 
19000000—2007 GFD/WEO 
Australia 17980000—2007 GFD/WEO Nominal 
18200000—2000 Diaz.et. al. Index of total PRODUCTION 
(1995=100) 
Austria 
Belgium 18350000—2007 BNB, Centre d'études Nominal 
économiques de la KUL 
Bolivia 
Brazil 18610000—2007 GFD/WEO Nominal 
18500000—2000 Diaz et. al. Index of total PRODUCTION 
(1995=100) 
19000000—2000 OXF (base=1970) 
Canada 
Central African 
Republic 
Chile 18100000—2000 Diaz et. al. Index of total PRODUCTION 
(1995=100) 
China 
NNP 19620000—1999 GFD 
Colombia 19000000—2000 OXF Real, (base=1970) 
19250000—1999 GFD 
Costa Rica 19470000—1999 GFD 
Cote D’ Ivoire 
Denmark 18180000—1975 Nordic Historical 
National Accounts 
Dominican Republic 
              Ecuador 
18860000—19450000    Egypt Yousef 
19520000—20070000    Egypt GFD/WEO 
18210000—18590000    Egypt Landes Cotton output 
              El Salvador 
18600000—20010000    Finland Nordic Historical National Accounts 
              France 
             Germany 
18330000—1939             Greece  Kostelenos et. al. 
18800000—1913 Flandreau & Zumer 
GNI 19270000—1999 GFD 
19480000—1999 GFD 
Guatemala 
Honduras 


Table AI.5 Nominal and Real Gross National Product and Output Index 
(Domestic currency units) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
India 19000000—1921, 19480000—2007 GFD/WEO 
18610000—1899 Brahamanda Real, per capita 
18200000—2000 Diaz et. al. Index of total PRODUCTION 
18150000—1913 Indonesia VanZanden 
19100000—1970 Java Bassino and Van der Eng 
19210000—1939, 
19510000—1999 GFD 
19110000—1938, 
#19530000—1999 GFD 
19110000—19400000    KOREA Myun Soo Cha and Nak Thousand yen, GNI also Kim calculated GNI 
#19530000—19990000    KOREA GFD 
19100000—1970 Malaysia Bassino and Van der Eng 
19490000—1999 GFD 
18200000—2000 Mexico DIA Index of total PRODUCTION (1995=100) 
19000000—2000 OXF 
19000000—2000 OXF Real, (base=1970) 
19250000—1999 GFD 
19130000—1970 Myanmar (Burma) Bassino and Van der Eng 
19500000—1999 GFD 
18000000—1913 NETHERLANDS[NIEDERLANDE]National Accounts of THE—NETHERLANDS[NIEDERLANDE]
18300000—2003 Norway Grytten 
19000000—2000 Peru OXF Real (base=1970) 
19000000—2000 OXF Nominal 
19420000—1999 GFD 
19100000—1970 Philippines Bassino and Van der Eng 
1946-2997 GFD/WEO 
Poland 
Portugal 
18850000—1913 Russia Flandreau & Zumer Nominal GNI 19280000—1940, 19450000—1995 GFD 
19790000—1997 GFD 
19920000—1999 GFD PRODUCTION 
19110000—1999 SOUTH—AFRICA GFD 
Spain 
Lanka 19000000—1970 Sri Bassino and Van der Eng 
17200000—2000 Sweden Edvinsson Real, per capita 
18000000—2000 Nominal and real 
19100000—1970 Taiwan Bassino and Van der Eng 
19460000—2007 Thailand (Siam) GFD/WEO 
19100000—1970 Bassino and Van der Eng 


Table AI.5 

NOMINAL AND REAL GROSS NATIONAL PRODUCT AND OUTPUT INDEX (DOMESTIC CURRENCY UNITS) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Tunisia 
Turkey 19230000—2005 Nominal 
19500000—1999 GFD 
United Kingdom 18300000—1999 GFD GNI 
19480000—1999 GFD 
United States 17900000—2002 Historical Statistics of Real, per capita the United States 
19480000—1999 GFD 
Uruguay 19350000—1999 GFD 
19550000—2000 OXF 
19000000—2000 OXF Real (base=1970) 
GNI 19550000—1999 GFD 
VENEZUELA 18300000—2002 Baptista 
19000000—2000 OXF Real (base=1970) 
19500000—2007 GFD/WEO 
Zambia 
Zimbabwe 


Table AI.6 
Gross National Product: PPP in constant $ (also available on a per capita basis) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Algeria 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
Angola 19500000—2005 MAD/TED 
Argentina 18750000—2000 Diaz et. al (base=1996) 
19000000—2005 MAD/TED 
18700000—2005 RR Interpolation 18710000—1899 
Australia 18200000—2006 MAD/TED 
Austria 18700000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—18690000  
Belgium 18460000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1845 
Bolivia 19450000—2005 MAD/TED 
19360000—2005 RR Interpolation 19360000—19440000  
Brazil 18200000—2000 DIA (base=1996) 
18700000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—18690000  
Canada 18700000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—18690000  
Central African 19500000—2003 MAD 
Republic 
Chile 18100000—2000 Diaz et. Al. (base=1996) 
18200000—2005 MAD/TED 
China 19290000—19380000 MAD/TED 
19500000—2006 
Colombia 19000000—2005 MAD/TED 
Costa Rica 19200000—2005 MAD/TED 
Denmark 18200000—2006 MAD/TED 
Dominican Republic 19500000—2005 MAD/TED 
19420000—2005 RR Interpolation 19420000—1949 
Ecuador 19390000—2005 MAD/TED 
19000000—2000 OXF (base=1970) 
19000000—2005 RR Interpolation 19000000—19380000 
Egypt 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
El Salvador 19000000—2000 OXF (base=1970) 
Finland 18600000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1859 
France 18200000—2006 MAD/TED 
Germany 18500000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1849 
Greece 19210000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1920 
Guatemala 19200000—2005 MAD/TED 
Honduras 19200000—2005 MAD/TED 
Hungary 18240000—2006 MAD/TED 
18700000—2006 RR Interpolation 18710000—1923 


Table AI.6 Gross National Product: PPP in constant $ 
(also available on a per capita basis) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
India 18840000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1883 
Indonesia 18700000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—18690000  
Japan 18700000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—18690000  
Kenya 19500000—2005 MAD/TED 
Korea 19110000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1910 
Malaysia 19110000—2005 MAD/TED 
18200000—2006 RR Interpolation 18210000—1910 
Mauritius 19500000—2005 MAD/TED 
Mexico 19000000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1899 
Morocco 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
Myanmar (Burma) 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
Panama 19450000—2005 MAD/TED 
19390000—2005 RR Interpolation 19390000—1944 
Paraguay 19390000—2005 MAD/TED 
Peru 18950000—2005 MAD/TED 
Philippines 19020000—2005 MAD/TED 
18700000—2005 RR Interpolation 18710000—1901 
Poland 19290000—19380000 MAD/TED 
19500000—2006 
18700000—2005 RR Interpolation 18710000—1928 
Portugal 18650000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1864 
Romania 19260000—19380000 MAD/TED 
19500000—2006 
Russia 19280000—2006 MAD/TED 
Singapore 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
SOUTH—AFRICA 19500000—2005 MAD/TED 
19050000—2005 RR Interpolation 19050000—1949 
Spain 18500000—2006 MAD/TED 
18200000—2005 RR Interpolation 18210000—1849 


Table AI.6 Gross National Product: PPP in constant $ 
(also available on a per capita basis) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Sweden 18200000—2006 MAD/TED 
Thailand (Siam) 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
Tunisia 19500000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1949 
Turkey 19230000—2005 MAD/TED 
United Kingdom 18300000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—1829 
United States 18700000—2006 MAD/TED 
18200000—2006 RR Interpolation 18210000—18690000  
Uruguay 18700000—2005 MAD/TED 
VENEZUELA 19000000—2005 MAD/TED 
18200000—2005 RR Interpolation 18210000—1899 
Zambia 19500000—2005 MAD/TED 
Zimbabwe 19500000—2005 MAD/TED 
19190000—2005 MAD/TED 


Table AI.7 Central GOVERNMENT Expenditure and REVENUE 
(Domestic currency units unless otherwise noted) 

Country Period covered Sources Commentary 
Algeria 18340000—1960 MIT REVENUes begin in 1830 
19640000—1975 
19940000—1996 
19630000—2003 KRV 
Angola 19150000—1973 MIT 
19800000—2003 KRV 
Argentina 18640000—1999 MIT 
18800000—1913 Flandreau & Zumer 
19630000—2003 KRV 
Australia 18390000—1900 MIT REVENUes begin in 1824, 
New South Wales and 
other provinces circa 
1840 
19010000—1997 MIT Commonwealth 
19650000—2003 KRV 
Austria 17910000—1993 MIT World War I and II, 
missing data 
19650000—2003 KRV 
Belgium 18300000—1993 World War I missing 
data 
19650000—2003 KRV 
Bolivia 18880000—1999 MIT REVENUes begin 1885 
19630000—2003 KRV 
Brazil 18230000—1994 IBGE/MIT 
19800000—2003 KRV 
Canada 18060000—1840 MIT Lower Canada 
18240000—1840 Upper Canada 
18670000—1995 Canada 
19630000—2003 KRV 
Central African 19060000—1912 MIT 
Central African 1Republic 19250000—1973 
19630000—2003 KRV 
18100000—1995 Chile Braun et. al. (base=1995) 
18570000—1998 MIT 
19630000—2003 KRV 
19270000—19360000    China Cheng Nationalist GOVERMENT 
19630000—2003 KRV 
Colombia 19050000—1999 MIT 
19630000—2003 KRV 
Costa Rica 18840000—1999 MIT 
19630000—2003 KRV 
Cote D’Ivoire 18950000—1912 MIT 
19260000—1999 
19630000—2003 KRV 
Denmark 18530000—1993 MIT 
19650000—2003 KRV 


Table AI.7 Central GOVERNMENT Expenditure and REVENUE 
(Domestic currency units unless otherwise noted) 

Country Period covered Sources Commentary 
Dominican Republic 19050000—1999 MIT 
19630000—2003 KRV 
Ecuador 18840000—1999 MIT 
19790000—2003 KRV 
Egypt 18210000—1879 Landes 
18520000—1999 MIT 
19630000—2003 KRV 
El Salvador 18830000—1999 MIT 
19630000—2003 KRV 
Finland 18820000—1993 MIT 
19650000—2003 KRV 
France 16000000—1785 ESFDB 
18150000—1993 MIT 
19650000—2003 KRV 
Germany (Prussia) 16880000—1806 ESFDB 
Germany 18720000—1934 MIT REVENUes end in 1942 
19460000—1993 West Germany 
19790000—2003 KRV 
Greece 18850000—1940 MIT Expenditure begins in 
19540000—1993 1833 and again in 1946 
19630000—2003 KRV 
Guatemala 18820000—1999 MIT 
19630000—2003 KRV 
Honduras 18790000—1999 MIT 
19630000—2003 KRV 
Hungary 18680000—1940 MIT 
India 18100000—2000 MIT 
19630000—2003 KRV 
Indonesia 18210000—1940 Mellegers NETHERLANDS[NIEDERLANDE]East Indies, 
florins, high GOVERNMENT 
18160000—1939 MIT 
19590000—1999 
19630000—2003 KRV 
ITALY 18620000—1993 MIT 
19650000—2003 KRV 
Japan 18680000—1993 MIT 
19630000—2003 KRV 
Kenya 18950000—2000 MIT 
19700000—2003 KRV 
Korea 19050000—1939 MIT Japanese yen 
19490000—1997 South Korea 
19630000—2003 KRV 
Malaysia 18830000—19380000 MIT Malaya 
19460000—1999 
19630000—2003 KRV 
Mauritius 18120000—2000 MIT 
19630000—2003 KRV 


Table AI.7 Central GOVERNMENT Expenditure and REVENUE 
(Domestic currency units unless otherwise noted) 

Country Period covered Sources Commentary 
Mexico 18250000—1998 MIT 
19630000—2003 KRV 
Morocco 19380000—2000 MIT REVENUes also 19201929 
19630000—2003 KRV 
Myanmar (Burma) 19460000—1999 MIT 
19630000—2003 KRV 
NETHERLANDS[NIEDERLANDE]18450000—1993 MIT 
19650000—2003 KRV 
New Zealand 18410000—2000 MIT 
19650000—2003 KRV 
Nicaragua 19000000—1999 MIT 
19630000—2003 KRV 
Nigeria 18740000—1998 MIT 
19630000—2003 KRV 
Norway 18500000—1992 MIT 
19650000—2003 KRV 
Panama 19090000—1996 MIT 
19630000—2003 KRV 
Paraguay 18810000—1900 MIT REVENUes through 1902 
19130000—1993 
19630000—2003 KRV 
Peru 18460000—1998 MIT 
19630000—2003 KRV 
Philippines 19010000—2000 MIT
             WWII missing 
data 
19630000—2003 KRV 
Poland 19220000—1937 MIT 
19470000—1993 Expenditure only 
Portugal 18790000—1902 MIT 
19170000—1992 
19750000—2003 KRV 
Romania 18830000—1992 MIT Expenditure begins in 
1862 
Russia 17690000—1815 ESFDB 
18040000—1914 MIT 
19240000—1934 
19500000—1990 
19140000—1921 Katzenellenbaum 
19310000—1951 Condoide National budget 
Singapore 19630000—2000 MIT 


Table AI.7 Central GOVERNMENT Expenditure and REVENUE 
(Domestic currency units unless otherwise noted) 

Country Period covered Sources Commentary 

SOUTH—AFRICA 18260000—1904 
19050000—2000 
19630000—2003 
Spain 15200000—1553 
17530000—1788 
18500000—1997 
19650000—2003 
Sri Lanka 18110000—2000 
19630000—2003 
Sweden 18810000—1993 
19800000—2003 
Taiwan 18980000—19380000 
19500000—2000 
Thailand (Siam) 18910000—2000 
19630000—2003 
Tunisia 19090000—1954 
19650000—1999 
19630000—2003 
Turkey 19230000—2000 
19630000—2003 
United Kingdom 14860000—1815 
17910000—1993 
19630000—2003 
United States 17890000—1994 
19600000—2003 
Uruguay 18710000—1999 
19630000—2003 
VENEZUELA 18300000—1998 
19630000—2003 
19630000—2003 
Zambia 19630000—2003 
Zimbabwe 18940000—1997 
19630000—2003 

MIT 

KRV 
ESFDB 

MIT 
KRV 
MIT 
KRV 
MIT 
KRV 
MIT 

MIT 
KRV 
MIT 

KRV 
MIT 
KRV 
ESFDBMIT 
KRV 
MIT 
KRV 
MIT 
KRV 
MIT 
KRV 
KRV 
KRV 
MIT 
KRV 

Natal begins in 1850Not continuous 

REVENUE begins in 1851 


Table AI.8 Total Exports and Imports 
(local currency units and US$, as noted) 

Country Period covered Sources Currency/ 
Commentary 
Algeria 18310000—2007 GFD/WEO 
Angola 18910000—2007 GFD/WEO 
Argentina 18640000—2007 GFD/WEO Lcu 
18850000—2007 GFD/WEO US$ 
18800000—1913 Flandreau & Zumer Exports 
Australia 18260000—2007 GFD/WEO 
Austria 18310000—2007 
Belgium 18460000—2007 GFD/WEO 
18160000—2007 GFD/WEO US$ 
Bolivia 18990000—1935 GFD Lcu 
18990000—2007 US$ 
Brazil 18210000—2007 GFD/WEO 
18800000—1913 Flandreau & Zumer Exports 
Canada 18320000—2007 GFD/WEO Lcu 
18670000—2007 US$ 
Chile 18570000—1967 GFD/WEO Lcu 
China 18650000—1937 GFD/WEO Lcu 
19500000—2007 
Colombia 18350000—19380000 Lcu 
19190000—2007 GFD/WEO US$ 
Costa Rica 18540000—19380000 GFD/WEO Lcu 
19210000—2007 US$ 
Cote D’Ivoire 18920000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
Denmark 18410000—2007 GFD/WEO Exports begin in 1818, 
lcu 
18650000—2007 US$ 
18890000—19490000    Ecuador GFD/WEO Lcu 
19240000—20070000    US$ 
Egypt 18500000—2007 GFD/WEO Lcu 
18690000—2007 US$ 
El Salvador 18590000—1988 GFD/WEO Exports begin in 1854, 
lcu 
18700000—2007 US$ 
Finland 18180000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
France 18000000—2007 GFD/WEO 
Germany 18800000—2007 GFD/WEO 
Ghana 18500000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
Greece 18490000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
Guatemala 18510000—2007 GFD/WEO 


Table AI.8 Total Exports and Imports 
(local currency units and US$, as noted) 

Country Period covered Sources Currency 
Honduras 18960000—2007 GFD/WEO 
India 18320000—2007 GFD/WEO 
Indonesia 18230000—1974 GFD/WEO Lcu 
18760000—2007 US$ 
ITALY 18610000—2007 GFD/WEO 
Japan 18620000—2007 GFD/WEO 
Kenya 19000000—2007 GFD/WEO 
Korea 18860000—1936 GFD/WEO Lcu 
19050000—2007 US$ 
Malaysia 19050000—2007 GFD/WEO Includes Singapore —UNTIL 
1955 
Mauritius 18330000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
Mexico 17970000—18300000 GFD/WEO UK pound 
18720000—2007 Lcu 
17970000—18300000 US$ 
18720000—2007 
Morocco 19470000—2007 GFD/WEO 
Myanmar (Burma) 19370000—2007 GFD/WEO 
NETHERLANDS[NIEDERLANDE]18460000—2007 GFD/WEO 
Nicaragua 18950000—2007 GFD/WEO 
Norway 18510000—2007 GFD/WEO 
Panama 19050000—2007 GFD/WEO Lcu 
Paraguay 18790000—1949 GFD/WEO US$ 
19230000—2007 
Peru 18660000—1952 GFD/WEO Lcu 
18820000—2007 US$ 
Philippines 18840000—2007 GFD/WEO 
Poland 19240000—2007 GFD/WEO 
Portugal 18610000—2007 GFD/WEO 
Romania 18620000—1993 GFD/WEO Lcu 
19210000—2007 US$ 
Russia 18020000—1991 GFD/WEO Lcu 
18150000—2007 US$ 
Singapore 19480000—2007 GFD/WEO 
SOUTH—AFRICA 18260000—2007 GFD/WEO Lcu 
19000000—2007 US$ 


Table AI.8 Total Exports and Imports 
(local currency units and US$, as noted) 

Country Period covered Sources Currency 
Spain 18220000—2007 GFD/WEO 
Sri Lanka 18250000—2007 GFD/WEO Lcu 
19000000—2007 US$ 
Sweden 18320000—2007 GFD/WEO 
Taiwan 18910000—2007 GFD/WEO 
Thailand (Siam) 18590000—2007 GFD/WEO 
Turkey 18780000—2007 GFD/WEO 
United Kingdom 17960000—2007 GFD/WEO 
United States 17880000—2007 GFD/WEO 
Uruguay 18620000—1930 GFD/WEO 
18990000—2007 
VENEZUELA 18300000—2007 GFD/WEO 
19000000—2007 
Zambia 19080000—2007 GFD/WEO 
Zimbabwe 19000000—2007 GFD/WEO 

Table AI.9 Global Indicators and Financial Centers 

Country Series Period covered Sources 
United Kingdom Current account 18160000—2006 Imlah, Mitchell, and 
balance/GDP United Kingdom 
National Statistics 
Consol rate 17900000—2007 GFD and BANK—OF—ENGLAND 
Discount rate 17900000—2007 GFD and BANK—OF—ENGLAND 
United States Current account 17900000—2006 Historical Statistics of 
balance/GDP the United States, WEO 
60-90 day commercial 18300000—1900 Historical Statistics of 
paper the United States 
Discount rate 19150000—2007 GFD and Board of 
Governors of the 
FEDERAL—RESERVE 
Federal funds rate 19500000—2007 Board of Governors of 
the FEDERAL—RESERVE 
Long-term bond 17980000—2007 Historical Statistics of 
the United States, Board 
of Governors of the 
FEDERAL—RESERVE 
World Commodity prices, 17900000—1850 Gayer, Rostow, and 
nominal and real Schwartz 
18540000—1990 Boughton-IMF 
18620000—1999 Economist 
19800000—2007 WEO 
Sovereign external 13410000—2007 Macdonald, Purcell and 
default dates Kaufman, Reinhart, 
Rogoff, and Savastano, 
Suter, and Standard and 
Poor’s 


Appendix II. 

Public Debt 

This data appendix covers the GOVERNMENT debt series used, while Appendix I is devoted to 
the database on macro time series. 

Abbreviations of frequently-used sources (additional sources listed in tables below) 

CLYPS: Cowan, Levy-Yeyati, Panizza, Sturzenegger 
ESFDB: European State Finance Data Base 
GFD: Global Financial Data, The World Bank 
IFS: International Financial Statistics, IMF. 
LM: Lindert & Morton 
LofN: League of Nations 
MAR: Marichal 
MIT: Mitchell 
RR: Reinhart and Rogoff 
UN: UNITED—NATIONS 
WEO: World Economic Outlook, IMF 
Lcu: local currency units 

List of Tables 

Table AII.1 Public Debentures: External GOVERNMENT Bond Issues 
Table AII.2 Total (Domestic plus External) Public Debt 
Table AII.3 External Public Debt 
Table AII.4 Domestic Public Debt 


Table AII. 1 Public Debentures: External GOVERNMENT Bond Issues 

Country Period covered Sources Commentary 
Argentina 18240000—1968 LM, MAR Includes 1. loan 
19270000—19460000 0000  UN 
18570000—19780000    Australia, attard, LM 
19270000—19460000  UN 
Bolivia 18640000—1930 MAR 
19270000—19460000  UN 
Brazil 18430000—1970 Bazant, LM, MAR, Includes 1. loan 
Summerhill 
19280000—19460000  UN 
Canada 18600000—1919 LM 
19280000—19460000  UN 
Chile 18220000—18300000 LM, MAR Includes 1. loan 
19280000—19460000  UN 
China 18650000—19380000 Huang, Winkler 
Colombia 18220000—1929 MAR 
19280000—19460000  UN 
Costa Rica 18710000—1930 MAR 
Egypt 18620000—1965 Landes, LM Includes 1. loan 
19280000—19460000  UN 
El Salvador 19220000—1930 MAR 
19280000—19460000 0000  UN 
Greece 18240000—1932 Levandis Includes 1. loan 
(Independence loan) 
19280000—1939 UN 
Guatemala 18560000—1930 MAR 
19280000—1939 UN 
Honduras 18670000—1930 MAR 
India 
19280000—1945 UN 
Japan 18700000—1965 LM Includes 1. loan 
19280000—1939 UN 
Mexico 18240000—19460000 0000  Bazant, LM, MAR Includes 1. loan 
19280000—1944 UN 
Panama 19230000—1930 
19280000—1945 UN 
Peru 18220000—1930 MAR Includes 1. loan 
19280000—1945 UN 
Russia 18150000—1916 Crisp, LM, Miller 
SOUTH—AFRICA 19280000—19460000 0000  UN 
Thailand (Siam) 19280000—1947 UN 
Turkey 18540000—1965 Clay, LM Includes 1. loan 
19330000—1939 UN 
Uruguay 18710000—1939 MAR 
19280000—1947 UN 
VENEZUELA 18220000—1930 MAR Includes 1. loan 
19280000—1947 UN 


Table AII.2 Total (Domestic plus External) Public Debt 
(currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Argentina 18630000—1971 Garcia Vizcaino Lcu 
19140000—1981 LofN/UN Lcu 
19800000—2005 GFD, Jeanne & Guscina 
Australia 18520000—1914 Page 
19140000—1981 LofN/UN Lcu 
19800000—2007 Australian OFFICE—OF—Lcu Financial Management 
Austria 18800000—1913 Flandreau & Zumer Lcu 
19450000—1984 UN Lcu 
19700000—2006 Austrian Federal euros Financing Agency 
Belgium 18300000—2005 BNB, Centre d'études euros économiques de la KUL 
Bolivia 19140000—#1953 LofN/UN Lcu 
19680000—1981 
19910000—2004 CLYPS US$ 
Brazil 18800000—1913 Flandreau & Zumer Lcu 
19230000—1972 LofN/UN Lcu 
19910000—2005 GFD, Jeanne & Guscina 
Canada 18670000—2007 Statistics Canada, Lcu BANK—OF—Canada 
Chile 18270000—2000 Diaz et. al. Lcu 
19140000—#1953 LofN/UN Lcu 
19900000—2007 Ministerio de US$ Hacienda 
China 18940000—1950 Cheng, Huang, RR 
19810000—2005 GFD, Jeanne & Guscina 
Colombia 19230000—2006 Contraloria General Lcu de la Republica 
Costa Rica 18920000—1914 Soley-Guell Lcu 
19140000—1983 LofN/UN Lcu 
19800000—2007 CLYPS, Ministerio de US$ Hacienda 
Cote D’Ivoire 19700000—1980 UN Lcu 
Denmark 18800000—1913 Flandreau & Zumer Lcu 
19140000—1975 LofN/UN Lcu 
19900000—2007 Danmark’s National Lcu Bank 
Dominican Republic 19140000—1952 LofN/UN Lcu 
Ecuador 19140000—1972 LofN/UN Lcu 
19900000—2006 MINISTRY—OF—FINANCE US$ 

Table AII.2 

Total (Domestic plus External) Public Debt
-continued (currency units are noted) 
COUNTRY PERIOD COVERED SOURCE COMMENTARY 
EGYPT 19140000—1959 LofN/UN Lcu 
20010000—2005 MINISTRY—OF—FINANCE Lcu 
El Salvador 19140000—1963 LofN/UN Lcu 
19760000—1983 
19900000—2004 CLYPS US$ 
20030000—2007 Banco Central de US$ Reserva 
Finland 19140000—1983 LofN/UN Lcu 
19780000—20070000    0000     State Treasury Finland Lcu 
France 18800000—1913 Flandreau & Zumer Lcu 
19130000—1972 LofN/UN Lcu 
19990000—2007 Ministère du Budget, Lcu des comptes public 
Germany 18800000—1913 Flandreau & Zumer Lcu 
19140000—1983 LofN/UN Lcu 
19500000—2007 Bundesbank Lcu 
Greece 18690000—1893 Levandis Not continuous, Lcu 
18800000—1913 Flandreau & Zumer Lcu 
19200000—1983 LofN/UN Lcu 
19930000—2006 OECD 
Guatemala 19210000—1982 LofN/UN Lcu 
19800000—2005 CLYPS US$ 
Honduras 19140000—1971 LofN/UN Lcu 
19800000—2005 CLYPS US$ 
Hungary 19130000—1942 LofN/UN Lcu 
19920000—2005 Jeanne & Guscina 
India 18400000—1920 Statistical Abstract Relating to British India 
19130000—1983 LofN/UN Lcu 
19800000—2005 Jeanne & Guscina 
Indonesia 19720000—1983 UN Lcu 
19980000—2005 Bank Indonesia/GDF 
ITALY 18800000—1913 Flandreau & Zumer Lcu 
19140000—1894 LofN/UN Lcu 
19820000—2007 Dipartamento del Lcu Tesoro 
Japan 18720000—2007 Historical Statistics of Lcu 
Japan/BANK—OF—Japan 
Kenya 19110000—1935 Frankel UK pounds 
19610000—1980 LofN/UN Lcu 
19970000—2007 Central BANK—OF—Kenya Lcu 
Korea 19100000—19380000 Mizoguchi & Umemura Yen 
19700000—1984 LofN/UN 
19900000—2004 Jeanne & Guscina 
Malaysia 19470000—1957 UN Lcu 
19760000—1981 
19800000—2004 Jeanne & Guscina 
Mauritius 19700000—1984 LofN/UN Lcu 
19980000—2007 Lcu 


Table AII.2. Total (Domestic plus External) Public Debt-continued (currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Mexico 18140000—19460000 0000  Bazant Not continuous 
19140000—1979 LofN/UN Lcu 
19800000—2006 Direccion General de la Deuda Publica 
Morocco 19650000—1980 UN Lcu 
NETHERLANDS[NIEDERLANDE]18800000—1914 Flandreau & Zumer Lcu 
19140000—1977 LofN/UN Lcu 
19140000—2008 Dutch State Treasury Lcu Agency 
New Zealand 18580000—2006 Statistics New Lcu 
Zealand/NZ Treasury 
Nicaragua 19140000—1945 LofN/UN Lcu 
19700000—1983 
19910000—2005 CLYPS US$ 
Norway 18800000—1914 Flandreau & Zumer Lcu 
19130000—1983 LofN/UN Lcu 
19650000—2007 MINISTRY—OF—FINANCE Lcu 
Panama 19150000—1983 LofN/UN US$ 
19800000—2005 CLYPS US$ 
Paraguay 19270000—1947 LofN/UN Lcu 
19760000—1982 
19900000—2004 CLYPS US$ 
Peru 19180000—1970 LofN/UN Lcu 
19900000—2005 CLYPS US$ 
Philippines 19480000—1982 LofN/UN Lcu 
19800000—2005 GFD, Jeanne & Guscina 
Poland 19200000—1947 LofN/UN 
19940000—2004 GFD, Jeanne & Guscina 
Portugal 18510000—1997 INE-Portugese Statistical Agency 
19140000—1975 LofN/UN Lcu 
19800000—2007 Banco de Portugal In euros from 1999 
Russia 18800000—1914 Crisp, Flandreau & French francs and Lcu Zumer 
19220000—19380000 LofN/UN Lcu 
19930000—2005 Jeanne & Guscina 
Singapore 19690000—1982 UN Lcu 
19860000—2006 Monetary Authority Lcu 
SOUTH—AFRICA 18590000—1914 Page UK pounds 
19100000—1982 LofN/UN Lcu 
19460000—2006 SOUTH—AFRICA Reserve Lcu Bank 
 
Appendix Table AII.2 Total (Domestic plus External) Public Debt-concluded (currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Spain 15040000—1679 ESFDB Not continuous 
18500000—2001 estadisticas Historicas Lcu de españa: Siglos XIXXX 
19990000—2006 Banco de españa Euro 
Sri Lanka 18610000—1914 Page UK pounds 
19500000—1983 UN Lcu 
19900000—2006 Central BANK—OF—Sri Lcu Lanka 
Sweden 18800000—1913 Flandreau & Zumer 
19140000—1984 LofN/UN Lcu 
19500000—2006 Riksgälden Lcu 
Thailand (Siam) 19130000—1984 LofN/UN Lcu 
19800000—2006 Jeanne & Guscina, Bank of Thailand 
Tunisia 19720000—1982 LofN/UN Lcu 
20040000—2007 Central BANK—OF—Tunisia Lcu 
Turkey 19330000—1984 LofN/UN Lcu 
19860000—2007 Turkish Treasury US$ 
United Kingdom 16930000—1786 Quinn Total funded debt 
17810000—1915 Bazant, Page 17870000—1815, not contiuous 
18500000—2007 UK Debt Management 
Office 
United States 17910000—2007 Treasury Direct 
Uruguay 19140000—1947 LofN/UN Lcu 
19720000—1984 
19990000—2007 Banco Central del US$ 
Uruguay 
VENEZUELA 19140000—1982 LofN/UN 
19830000—2005 Jeanne & Guscina 
Zimbabwe 19240000—1936 Frankel UK pounds 
19690000—1982 UN 

Table AII.3 External Public Debt (currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Algeria 19700000—2005 GFD US$ 
Angola 19890000—2005 GFD US$ 
Argentina 18630000—1971 Garcia Vizcaino Lcu 
19140000—1981 LofN/UN Lcu 
19700000—2005 GFD US$ 
Australia 18520000—1914 Page 
19140000—1981 LofN/UN Lcu 
19800000—2007 Australian OFFICE—OF—Lcu Financial Management 
Austria 19450000—1984 UN Lcu 
19700000—2006 Austrian Federal euros 
Financing Agency 
Belgium 19140000—1981 LofN/UN Lcu 
19920000—2007 
Bolivia 19140000—#1953 LofN/UN Lcu 
19680000—1981 
19700000—2005 GFD 
19910000—2004 CLYPS US$ 
Brazil 18240000—2000 IBGE £s and US$ 
19230000—1972 LofN/UN Lcu 
19700000—2005 GFD US$ 
19910000—2005 Jeanne & Guscina US$ 
Canada 18670000—2007 Statistics Canada, Bank Lcu 
of Canada 
Central African Republic 19700000—2005 GFD US$ 
Chile 18220000—2000 Diaz et. al. Lcu 
19700000—2005 GFD US$ 
18220000—1930 RR estimated from 
debentures 
China 18650000—1925 RR estimated from 
debentures 
19810000—2005 GFD US$ 
Colombia 19230000—2006 Contraloria General de Lcu la Republica 
Costa Rica 18920000—1914 Soley-Guell Lcu 
19140000—1983 LofN/UN Lcu 
19800000—2007 CLYPS, Ministerio de US$ Hacienda 
Cote D’Ivoire 19700000—2005 GFD US$ 
Dominican Republic 19140000—1952 LofN/UN Lcu 
19610000—2004 Banco de la Republica US$ 
Ecuador 19140000—1972 LofN/UN Lcu 
19700000—2005 GFD US$ 
` 19900000—2007 MINISTRY—OF—FINANCE US$ 
Egypt 18620000—1930 RR estimated from 
debebtures 
19140000—1959 LofN/UN Lcu 
19700000—2005 GFD US$ 


Table AII.3 External Public Debt-continued (currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
France 19130000—1972 LofN/UN Lcu 
19990000—2007 Ministère du Budget, Lcu 
des comptes public 
Germany 19140000—1983 LofN/UN Lcu 
Greece 19200000—1983 LofN/UN Lcu 
Guatemala 19210000—1982 LofN/UN Lcu 
19700000—2005 GFD US$ 
19800000—2005 CLYPS US$ 
Honduras 19140000—1971 LofN/UN Lcu 
19700000—2005 GDF US$ 
19800000—2005 US$ 
Hungary 19130000—1942 LofN/UN Lcu 
19820000—2005 GDF US$ 
19920000—2005 Jeanne & Guscina 
India 18400000—1920 Statistical Abstract 
relating to British India 
19130000—1983 LofN/UN Lcu 
19800000—2005 Jeanne & Guscina 
Indonesia 19720000—1983 UN Lcu 
19700000—2005 GDF US$ 
ITALY 18800000—1913 Flandreau & Zumer Lcu 
19140000—1984 LofN/UN Lcu 
19820000—2007 Dipartamento del Lcu Tesoro 
Japan 18720000—2007 Historical Statistics of Lcu 
Japan/BANK—OF—Japan 
19100000—19380000 Mizoguchi & Umemura Yen 
Kenya 19610000—1980 LofN/UN Lcu 
19700000—2005 GDF US$ 
19970000—2007 Central BANK—OF—Kenya Lcu 
Korea 19700000—1984 LofN/UN Lcu 
19700000—2005 GDF US$ 
19900000—2004 Jeanne & Guscina US$ 
Malaysia 19470000—1957 LofN/UN Lcu 
19760000—1981 
19700000—2005 GDF US$ 
19800000—2004 Jeanne & Guscina 
Mauritius 19700000—1984 LofN/UN Lcu 
19700000—2005 GDF US$ 
19980000—2007 BANK—OF—Mauritius Lcu 


Table AII. External Public Debt-continued 
(currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Mexico 18140000—19460000 0000  Bazant Not continuous 
18200000—1930 RR estimated from 
debentures 
19140000—1979 LofN/UN Lcu 
19700000—2005 GDF US$ 
19800000—2006 Direccion General de la 
Deuda Publica 
Morocco 19650000—1980 UN Lcu 
19700000—2005 GDF US$ 
NETHERLANDS[NIEDERLANDE]18800000—1914 Flandreau & Zumer Lcu 
19140000—1977 LofN/UN Lcu 
19140000—2008 Dutch State Treasury Lcu 
Agency 
New Zealand 18580000—2006 Statistics New Lcu 
Zealand/NZ Treasury 
Nicaragua 19140000—1945 LofN/UN Lcu 
19700000—1983 
19700000—2005 GDF US$ 
19910000—2005 CLYPS US$ 
Norway 18800000—1914 Flandreau & Zumer Lcu 
19130000—1983 LofN/UN Lcu 
19650000—2007 MINISTRY—OF—FINANCE Lcu 
Panama 19150000—1983 LofN/UN US$ 
19800000—2005 CLYPS US$ 
Paraguay 19270000—1947 LofN/UN Lcu 
19760000—1982 
19700000—2005 GFD US$ 
19900000—2004 CLYPS US$ 
Peru 18220000—1930 RR estimated from debentures 
19180000—1970 LofN/UN Lcu 
19900000—2005 CLYPS US$ 
19700000—2005 GFD US$ 
Philippines 19480000—1982 LofN/UN Lcu 
19700000—2005 GFD US$ 
Poland 19200000—1947 LofN/UN Lcu 
19860000—2005 GFD US$ 
Portugal 18510000—1997 INE-Portugese 
Statistical Agency 
19140000—1975 LofN/UN Lcu 
19800000—2007 Banco de Portugal In euros from 1999 
Russia 18150000—1917 RR 
19220000—19380000 LofN/UN Lcu 
19930000—2005 Jeanne & Guscina 
Singapore 19690000—1982 UN Lcu 


Table AII.3 External Public Debt-concluded 
(currency units are noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
SOUTH—AFRICA 18590000—1914 Page UK pounds 
19100000—1983 LofN/UN Lcu 
19460000—2006 SOUTH—AFRICA Reserve Lcu 
Bank 
Spain 18500000—2001 estadisticas Historicas Lcu 
de españa: Siglos XIXXX 
19990000—2006 Banco de españa Euro 
Sri Lanka 19500000—1983 UN Lcu 
19700000—2005 GFD US$ 
19900000—2006 Central BANK—OF—Sri Lcu 
Lanka 
Sweden 19140000—1984 LofN/UN Lcu 
19500000—2006 Riksgälden Lcu 
Thailand (Siam) 19130000—1984 LofN/UN Lcu 
19700000—2005 GFD US$ 
19800000—2006 Jeanne & Guscina, Bank Lcu of Thailand 
Tunisia 19700000—2005 GFD US$ 
20040000—2007 Central BANK—OF—Tunisia Lcu 
19720000—1982 LofN/UN Lcu 
Turkey 18540000—1933 RR estimated from debentures 
19330000—1984 LofN/UN Lcu 
19700000—2005 GFD US$ 
19860000—2007 Turkish Treasury US$ 
United Kingdom 19140000—2007 LofN/UN Lcu 
Uruguay 18710000—1930 RR estimated from debentures 
19140000—1947 LofN/UN Lcu 
19720000—1984 
19700000—2005 GFD US$ 
19800000—2004 CLYPS US$ 
VENEZUELA 18220000—1842 RR estimated from debentures, US $ 
19140000—1982 LofN/UN Lcu 
Zambia 19700000—2005 GFD 
Zimbabwe 19690000—1982 UN Lcu 
19700000—2005 GFD US$ 


Table AII.4 Domestic Public Debt 
(Local currency units unless otherwise noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Argentina 18630000—1971 Garcia Vizcaino Lcu 
19140000—1981 LofN/UN Lcu 
19800000—2005 GFD, Jeanne & Guscina 
Australia 19140000—1981 LofN/UN Lcu 
19800000—2007 Australian OFFICE—OF—Lcu Financial Management 
Austria 19450000—1984 UN Lcu 
19700000—2006 Austrian Federal euros 
Financing Agency 
Belgium 19140000—1983 LofN/UN Lcu 
19920000—2007 BNB, Centre d'études 
économiques de la KUL 
Bolivia 19140000—#1953 LofN/UN Lcu 
19680000—1981 
19910000—2004 CLYPS US$ 
Brazil 19230000—1972 LofN/UN Lcu 
19910000—2005 GFD, Jeanne & Guscina 
Canada 18670000—2007 Statistics Canada, Bank Lcu 
of Canada 
Chile 18270000—2000 Diaz et. al. Lcu 
19140000—#1953 LofN/UN Lcu 
19140000—19460000 0000  UN 
19900000—2007 Ministerio de Hacienda US$ 
China 18940000—1949 RR (from Cheng, Lcu 
Huang, UN) 
Colombia 19230000—2006 Contraloria General de Lcu 
la Republica 
Costa Rica 18920000—1914 Soley-Guell Lcu 
19140000—1983 LofN/UN Lcu 
19800000—2007 CLYPS, Ministerio de US$ Hacienda 
Cote D’Ivoire 19700000—1980 UN Lcu 
Denmark 19140000—1975 LofN/UN Lcu 
19900000—2007 Danmark’s National Lcu 
Bank 
Dominican Republic 19140000—1952 LofN/UN Lcu 
Ecuador 19140000—1972 LofN/UN Lcu 
19900000—2006 MINISTRY—OF—FINANCE US$ 
Egypt 19140000—1959 LofN/UN Lcu 
20010000—2005 MINISTRY—OF—FINANCE Lcu 


Table AII.4 Domestic Public Debt-continued 
(Local currency units unless otherwise noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
France 19130000—1972 LofN/UN Lcu 
19990000—2007 Ministère du Budget, Lcu 
des comptes public 
Greece 19200000—1983 LofN/UN Lcu 
19120000—1941 UN 
Guatemala 19210000—1982 LofN/UN Lcu 
19800000—2005 CLYPS US$ 
Honduras 19140000—1971 LofN/UN Lcu 
19800000—2005 US$ 
Hungary 19130000—1942 LofN/UN Lcu 
19920000—2005 Jeanne & Guscina 
India 18400000—1920 Statistical Abstract 
relating to British India 
19130000—1983 LofN/UN Lcu 
19800000—2005 Jeanne & Guscina 
Indonesia 19720000—1983 UN Lcu 
19980000—2005 Bank Indonesia/GDF 
ITALY 18800000—1913 Flandreau & Zumer Lcu 
19140000—1894 LofN/UN Lcu 
18820000—2007 Dipartamento del Lcu 
Tesoro 
Japan 18720000—2007 Historical Statistics of Lcu 
Japan/BANK—OF—Japan 
19140000—19460000 0000  UN 
Kenya 19610000—1980 LofN/UN Lcu 
19970000—2007 Central BANK—OF—Kenya Lcu 
Korea 19700000—1984 LofN/UN Lcu 
19900000—2004 Jeanne & Guscina Lcu 
Malaysia 19470000—1957 LofN/UN Lcu 
19760000—1981 
19800000—2004 Jeanne & Guscina 
Mauritius 19700000—1984 LofN/UN Lcu 
19980000—2007 BANK—OF—Mauritius Lcu 
Mexico 18140000—19460000 0000  Bazant Not continuous 
19140000—1979 LofN/UN Lcu 
19800000—2006 Direccion General de la 
Deuda Publica 
Morocco 19650000—1980 UN Lcu 
NETHERLANDS[NIEDERLANDE]18800000—1914 Flandreau & Zumer Lcu 
19140000—1977 LofN/UN Lcu 
19140000—2008 Dutch State Treasury Lcu 
Agency 


Table AII.4 Domestic Public Debt-continued 
(Local currency units unless otherwise noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
New Zealand 18580000—2006 Statistics New Lcu 
Zealand/NZ Treasury 
Nicaragua 19140000—1945 LofN/UN Lcu 
19700000—1983 
19910000—2005 CLYPS US$ 
Norway 18800000—1914 Flandreau & Zumer Lcu 
19130000—1983 LofN/UN Lcu 
19650000—2007 MINISTRY—OF—FINANCE Lcu 
Panama 19150000—1983 LofN/UN US$ 
19800000—2005 CLYPS US$ 
Paraguay 19270000—1947 LofN/UN Lcu 
19760000—1982 
19900000—2004 CLYPS US$ 
Peru 19180000—1970 LofN/UN Lcu 
19900000—2005 CLYPS US$ 
Philippines 19480000—1982 LofN/UN Lcu 
19800000—2005 GFD, Jeanne & Guscina 
Poland 19200000—1947 LofN/UN Lcu 
19940000—2004 Jeanne & Guscina Lcu 
Portugal 18510000—1997 INE-Portugese Lcu 
Statistical Agency 
19140000—1975 LofN/UN Lcu 
19800000—2007 Banco de Portugal In euros from 1999 
Russia 19220000—19380000 LofN/UN Lcu 
19930000—2005 Jeanne & Guscina 
Singapore 19690000—1982 UN Lcu 
19860000—2006 Monetary Authority Lcu 
SOUTH—AFRICA 18590000—1914 Page UK pounds 
19100000—1983 LofN/UN Lcu 
19460000—2006 SOUTH—AFRICA Reserve Lcu 
Bank 
Spain 18500000—2001 estadisticas Historicas Lcu 
de españa: Siglos XIXXX 
19990000—2006 Banco de españa Euro 
Sri Lanka 19500000—1983 UN Lcu 
19900000—2006 Central BANK—OF—Sri Lcu 
Lanka 


Table AII.4 Domestic Public Debt-concluded 
(Local currency units unless otherwise noted) 

COUNTRY PERIOD COVERED SOURCE COMMENTARY 
Sweden 19140000—1984 LofN/UN Lcu 
19500000—2006 Riksgälden Lcu 
Thailand (Siam) 19130000—1984 LofN/UN Lcu 
19800000—2006 Jeanne & Guscina, Bank Lcu 
of Thailand 
Tunisia 19720000—1982 UN Lcu 
20040000—2007 Central BANK—OF—Tunisia Lcu 
Turkey 19330000—1984 LofN/UN Lcu 
19860000—2007 Turkish Treasury US$ 
United Kingdom 19140000—2007 LofN/UN Lcu 
United States 17910000—2007 Treasury Direct Lcu 
Uruguay 19140000—1947 LofN/UN Lcu 
19720000—1984 
19800000—2004 CLYPS US$ 
VENEZUELA 19140000—1982 LofN/UN Lcu 
19830000—2005 Jeanne & Guscina Lcu 
Zimbabwe 19690000—1982 UN Lcu 


ZEILEN_5621-