The Legacy of War on Fiscal Capacity∗
Didac Queralt†
November 1, 2016
The most recent version of this paper is available here.
Abstract
States make war, and wars make states. The second clause of Tilly’s dictum assumes
that the fiscal effort that states exert to wage war persists over time. This paper
investigates the effect of inter-state war on long-term fiscal capacity as a function
of two types of war financing instruments: taxes and external loans. Tax-financed
war exerts lasting effects on state capacity, as new taxes require enhancements of the
state apparatus as well as complementary fiscal innovations. Loan-financed war may
not contribute to long-term state capacity, as countries might default once war is over,
thus preempting any persistent fiscal effect. Importantly, the way war is financed might
be endogenous. To address this possibility, I exploit crashes in the nineteenth-century
international financial markets, which temporarily dried capital flows around the globe
and precluded states from borrowing regardless of their (un)observed characteristics.
The analysis shows that states that fought war while international lending dried have
today higher fiscal capacity ratios, whereas states that waged war while having access to
external lending do not. Results imply that the advent of a truly global credit market,
dated as early as 1815, facilitated external financing of the means of war but weakened
incentives to conduct tax reform. Ultimately, the globalization of credit undermined
the relationship between war- and state-making.
∗
First Draft: June 2015. I am grateful to Ben Ansell, Laia Balcells, Thomas Brambor, Carles Boix, Mark
Dincecco, Hector Galindo, Francisco Garfias, Scott Gates, Margaret Levi, Pilar Nogues-Marco, Shanker
Satyanath, Peter Schram, Ken Scheve, David Stasavage, Joachim Voth, Tianyang Xi, and seminar participants at Stanford, Lund, Sciences Po, Carlos III, IPEG, Institut d’Economia de Barcelona, and Peking
University for comments and suggestions.
†
Institute of Political Economy and Governance, iPEG-Barcelona; [email protected]
1
1
Introduction
War is devastating but it also offers an unmatched opportunity to transform the state.
The magnitude of the resources that a country needs to amass in order to wage a military
campaign offers rulers the incentives to invest in state-making while reducing domestic resistance to taxation. War clears the path to fiscal centralization (Dincecco, 2011; Tilly, 1990),
the professionalization of the tax administration (Ardant, 1975), and the adoption of various forms of revenue generating policy, such as mercantilism (Findlay and O’Rourke, 2007),
excises (Brewer, 1988), and income taxes (Scheve and Stasavage, 2010). Fiscal innovations
are often accompanied by complementary organizations, including treasuries, national custom services, and central banks (O’Brien, 2001), improved budgeting technologies (Dincecco,
2011), and new mechanisms of conscription (Fischer and Lundgreen, 1975) that contribute
to expand the scope of the state. Far from disappearing, the financial innovations that make
war possible are expected to exert lasting effects on the extractive capacity of the state (Ardant, 1975; Besley and Persson, 2011; Brewer, 1988; Desch, 1996; Dincecco and Prado, 2012;
Hoffman and Rosenthal, 2000; Mann, 1980; Rosenthal and Wong, 2011). In other words,
states make wars as much as war makes states.
The bellicist hypothesis of the creation of the modern state has received a lot of attention.
Both qualitative and quantitative evidence suggest that this hypothesis accounts particularly
well for the process leading to state-building in the Western World (Bonney, 1999; Dincecco,
2011; Ertman, 1997; Gennaioli and Voth, 2015; Hintze, 1975). By contrast, the evidence
is mixed in other regions: some scholars find positive evidence for the bellicist hypothesis
in Latin America (Thies, 2005), Asia (Barkey and Parikh, 1991; Stubbs, 1999), and Africa
(Thies, 2007), while others find negative results: Centeno (2002), Kurtz (2013) and (Soifer,
2016) in Latin America; He (2013) and Taylor and Botea (2008) in Asia; and Herbst (2000)
and Dincecco, Fenske and Onorato (2016) in Africa. The lack of traction of the bellicist
hypothesis in the periphery is attributed to a combination of moderate incidence of interstate warfare and strong ethnic divisions, both of which are expected to weaken the incentives
2
to invest in fiscal capacity (Besley and Persson, 2011).
In this paper, I advance a new explanation for the mixed evidence of the bellicist hypothesis around the globe. In particular, I argue that war itself is not as important as the way it
is financed. That is, I claim that the effect of war on long-term fiscal capacity is conditioned
on the ultimate mix of taxes and loans. States that do not only rely on debt to wage war,
but implement fiscal reforms to raise revenue through taxation, should benefit the most from
war-making, at least in the long-run. Financing war through taxation requires investing in
the fiscal capacity of the country, involving the transformations that are associated with the
bellicist hypothesis: that is, fiscal centralization, the enhancement of the tax administration, and the adoption of new taxes and complementary fiscal institutions that, ultimately,
rubricate the legitimacy of the state to assess private wealth and monitor compliance. These
reforms are expected to outlast war. The main reason for that is political: national rulers
are reluctant to cede power back over the newly created fiscal institution once war comes to
an end, as it serves them to consolidate their power vis-à-vis local elites and other political rivals (Besley and Persson, 2011; Dincecco, Federico and Vindigini, 2011; Gennaioli and
Voth, 2015; Tilly, 1990).
By contrast, financing war with loans, particularly if these are external —as it is usually
the case in the developing world—, does not necessarily lead to any of the above. Some
states invest in enhancing the tax capacity to service debt once war is over, while others do
not, thus preempting any significant enhancement of fiscal capacity with respect to prewar
years. Reputation concerns do not suffice to prevent default (Bulow and Rogoff, 1989;
Reinhart and Rogoff, 2009). First, defaults are accepted in reasonable circumstances: e.g.
losing a war (Slantchev, 2012; Tomz, 2007). Second, even serial defaulters eventually regain
access to international markets, either after substantial debt forgiveness or in exchange for
state monopolies and land properties (Jorgensen and Sachs, 1988; Marichal, 1989). One
way or the other, war-making does not necessarily translate into an enhanced capacity to
tax. Additionally, access to external credit, specially when it is inexpensive, might lead
3
to excess borrowing and destabilize national accounts in ways that prevent post-war fiscal
consolidation (Centeno, 2002; Tin-bor Hui, 2004).
To sum up, I argue that while financing war with taxes (even if only partially) makes a
clear contribution to state making, the effect of financing war with loans, mainly, is uncertain.
To test this hypothesis, this paper assesses the effect of types of war financing on longterm fiscal capacity, while controlling for alleged causes of the weak traction of the bellicist
hypothesis outside Europe: i.e. low warfare and high ethnic divisions. In particular, I
investigate the effect of participating in pre-modern inter-state war between 1816 and 1913
on current fiscal capacity, measured by the percentage of personal income tax to GDP, as
well as the size of the tax administration, value-added taxes, and modern census technologies.
To avoid sample selection issues that characterize previous work, the analysis includes over
a hundred countries from all around the globe.
Rulers cannot impose new taxes without negotiating (Levi, 1988). The adoption of
new taxes comes with payoffs, chief among them, political compensations in the form of
representation (Bates and Lien, 1985). To minimize political payoffs, rulers are expected
to borrow from abroad when they have the opportunity, that is, when they have access to
the international capital markets (Slantchev, 2012; Shea, 2013). Based on this theoretical
corpus, the empirical analysis assumes that, whenever rulers have access to external credit,
they will borrow to finance the means of war.1
Building on this assumption, I first compare the effect of fighting war when a given
country has access to the international capital market vis-à-vis times in which the country
has no access because it is in default. In the latter case, I expect the ruler’s incentives
to invest in domestic taxation to be strongest, thus contributing to expand the stock of
fiscal capacity permanently. Results confirm the expectation. More importantly, they are
robust to alternative sources of revenue, these being domestic borrowing, money printing,
and financial repression.
1
A notable exception is France under Napoleon.
4
A second battery of empirical analyses addresses the endogeneity of having access to
external loans in the nineteenth century. Specifically, I exploit crashes in the international
capital market to identify periods in which states cannot borrow from abroad irrespective
of their (un)observed characteristics. This quasi-natural experiment, allows me to study
what is the effect of fighting war in periods in which international capital dries relative to
fighting wars in periods in which international capital flows. Results show that fighting
war when the international lending market is down is associated with higher fiscal capacity
today. To the contrary, making war while international credit flows is at best inconsequential.
Results are robust to sample changes, and various war and statehood definitions, as well as
to initial and contemporaneous factors that condition the effect of war on long-term fiscal
capacity: population density (Karaman and Pamuk, 2013), ethnic fractionalization (Besley
and Persson, 2011; Besley and Reynal-Querol, 2014; Kurtz, 2013), war intensity measured
by the number of casualties (Dincecco and Prado, 2012; Rasler and Thompson, 1985), the
outcome and location of war, military alliances, colonial origins, geographic controls, and
World War I participation (Scheve and Stasavage, 2010).
Once access to international lending is exogenized, I address potential selection issues
into war. That is, countries that go to war despite the unavailability of external loans might
be different in ways that affect fiscal capacity today. This form of endogeneity is addresses
threefold: first, omitted variable bias is minimized by controlling for initial state capacity,
measured by a state antiquity index (Bockstette, Chanda and Putterman, 2002) and the
capacity to conduct a modern census by 1820. Second, anticipation issues are addressed by
focusing on wars initiated right before the sudden-stop of external credit, thus disconnecting
the decision to go to war from availability of external credit. Third, I investigate the effect
of war on fiscal capacity by focusing on countries that do not choose to go to war, but are
dragged into it. Additionally, the Appendix includes a reduced-form model in which war by
country i is instrumented by war participation by its adjacent neighbors. These analyses
confirm that war make states if it is substantially financed with taxes, not loans only.
5
A follow-up empirical section addresses the short-term effects of war-making as a function
of types of war-financing instruments. If the effect of war on fiscal capacity is cumulative, we
should expect some evidence of war-making on fiscal capacity by the end of the period under
consideration. In the absence of reliable, crossnational tax data, three alternative proxies
to state capacity are used: having conducted a modern census by 1913, the length of rail
lines and primary school enrollment on the verge of WWI. Results suggest that countries
that fought wars lacking (having) access to external loans between 1816 and 1913 present a
higher (lower) probably of having conducted a modern census at the eve of World War I, as
well as a more (less) dense railroad network, and higher (lower) enrollment ratios in primary
school.
The last section addresses transmission mechanisms. First, I show that the effect of premodern war manifests throughout the entire twentieth century. Second, I advance two reinforcing channels of transmission, one being political, the other bureaucratic. Overall, results
imply that the rulers’ incentives to fight back domestic opposition in order to adopt more
extractive taxes and intrusive fiscal administration are strongest when rulers lack cheaper
financial alternatives. Financing wars with domestic taxes seems to be a last, politically
costly resort, that nevertheless turns beneficial in the long-run.
This paper contributes to a growing industry that revisits Tilly’s dictum by conditioning
the effect of war on initial conditions. For instance, Karaman and Pamuk (2013) argue
that the effect of warfare on state capacity is conditional on the complementarities between
urbanization and regime type. Similarly, Kurtz (2013) claims that Latin America does not
capitalize the war effort because it lacks enough political cohesiveness to invest in public
goods such as a fiscal capacity.2 Fascinating as they stand, these accounts do not address
the radically different international context in which countries in the periphery are created as
compared to European nations: one in which external credit, even for new countries in the
periphery, is unprecedentedly cheap as a result of excess savings generated by the industrial
2
Refer to Besley and Persson (2011) for a formalization of the argument.
6
revolution in Western Europe (Taylor, 2006).
In this paper, I vindicate Charles Tilly’s theory and conceptual toolkit while adapting
it to the distinct international context of the long-nineteenth century. A close reading of
his work suggests that state building is a function of both war-making and domestic capital
access. European powers (e.g. Britain, the Netherlands, France) capitalized the fiscal effort
of war because they disproportionately borrowed domestically. In the absence of an efficient
international lending market that supplied inexpensive capital, as soon as the sixteenth
century, European rulers turned to domestic merchants to raise the means of war, either
by taxing or borrowing from them (alternatively, capital-intensive cities were coercively
annexed for the same purposes). The inward-turn of war financing led to political reform
that eventually ended in early forms of limited government (arguably, some states went
further than others).
Countries in the periphery did not face the same capital constraints as their European
counterparts had when they were involved in state-building. From their very inception, states
in the periphery had access to unprecedentedly inexpensive external loans despite their low
institutionalization and lack of international reputation (Tomz, 2007). Access to easy money,
I argue, weakens the incentives to develop domestic credit institutions, expand taxation, and
undertake political reform conducive to responsible fiscal policy. In other words, cheap
external credit facilitates the means of war while preempting fiscal (and political) reform
associated with war-making.
Others have pointed out the perverse effects of cheap credit on the incentives to tax
domestically: Centeno (2002, p.130-3) suggests this kind of artificial wealth distorted rulers’
incentives to finance war with taxes in post-independence Latin American. Similarly, Thies
(2007, p.728) speculates with this possibility to explain the mild effect of warfare on state
capacity in post-colonial African. Shea (2013) emphasizes the changing incentives to tax
when rulers have access to external credit at affordable prices. In this paper, I advance the
political economy underlying the choice of loans vs. taxes, articulate the implications for
7
long-term fiscal capacity, and test them against historical series while addressing endogeneity
of credit-access and war-making. Ultimately, the paper offers a better understanding of the
conditions under which war exerts positive and lasting effects on state building. Interestingly too, the perverse effects of cheap external credit advanced in this paper speak to the
scholarship analyzing the effect of different forms of non-tax revenue on political accountability: natural resources (Morrison, 2009; Ross, 2001), foreign aid (Bueno de Mesquita and
Smith, 2013; Moss, Pettersson Gelander and van de Walle, 2006), and ores from the colonies
(Drelichman and Voth, 2011).
What remains of the paper is organized as follows: First, I articulate the political economy
of war financing. Second, I model current fiscal capacity as a function of war participation
and credit access in the long-nineteenth century. These models address stepwise endogeneity
of credit access and war participation. Third, I investigate the incremental nature of fiscal
capacity building by showing the effect of war-making and credit acces at the eve of WWI.
Fourth, I advance the mechanisms of transmission. Lastly, I revisit the bellicist hypothesis
in light of the empirical evidence.
2
The Political Economy of War Financing
The two most common instruments of war financing are taxes and loans (Slantchev,
2012; Sprague, 1917).3 Resorting to one or the other is a matter of possibility —has the
state enough capacity to tax its citizens and/or access to lending markets?—as much as of
political opportunity —who gains and who loses upon borrowing and taxing (Flores-Macı́as
and Kreps, 2013; Stasavage, 2011).
Taxation is politically delicate, as it involves some form of extraction from elites, the
masses, or both. In taxing the wealthy, rulers can rarely impose new taxes without their
consent. Often times, taxes have to be negotiated and consulted with economic elites (Levi,
1988; Tilly, 1990). In pre-modern Europe, rulers offered elites political payoffs that, ul3
Printing money, financial repression, selling offices, and confiscation are addressed below.
8
timately, established modern political rights (e.g. the Magna Carta), and Parliamentary
representation, later on (Bates and Lien, 1985).4 Taxing the masses was not easier, especially when it was accompanied by the military draft: in such circumstances, political
concessions were required to prevent tax revolts from below (Ardant, 1975; Hintze, 1975;
Tilly, 1993). Ultimately, power-sharing institutions were the “price and outcome” of bargaining with different members of subject population in overcoming resistance to financing
with taxation the means of war (Tilly, 1990, p.64).
Financing war by borrowing from domestic sources might come with similar political
costs (North and Weingast, 1989). However, domestic borrowing requires levels of capital
accumulation that cannot be taken for granted, let alone in the New World.5 When domestic
credit markets are small, rulers may borrow from abroad, a practice that, despite being
common in pre-industrial Europe (Stasavage, 2011), accelerated after the Napoleonic Wars
in parallel to the globalization of financial markets (Reinhart and Rogoff, 2009; Lindert and
Morton, 1989; Suter, 1992).
Crucially, borrowing from abroad does not suffer from the same political costs and administrative challenges attached to taxation. That is, rulers do not have to concede political
rights or representation to international lenders; a good margin suffices. External borrowing
does not come with the uncertainties of tax yields either, thus facilitating the planification
of military campaigns (Slantchev, 2012). Lastly, external loans prevent sudden tax hikes
that might disrupt household allocation decisions, while passing the tax burden to next generations and minimizing political opposition to war (Barro, 1979). Given the short-term
advantages of financing wars with external loans, it is hardly surprising that nineteenthcentury warfare in the periphery (Asia, Eurasia, Latin- and North America, and Eastern
and Southern Europe) was heavily financed with external debt (Centeno, 2002; Flandreau
4
Elites’ resistance was also smoothed out with trade privileges (Ekelund and Tollison, 1981; Queralt,
2015), and the sale of offices (Hoffman, 1994). Either way, financing wars through taxation impinges a
political cost on the ruler, as it weakens his advantage position vis-à-vis the economic elites.
5
For the challenges to create domestic financial markets in the periphery, see Calomiris and Haber (2014),
della Paolera and Taylor (2013), and Taylor and Williamson (1994).
9
and Flores, 2012; Marichal, 1989).
Having access to external credit is consequential to understanding the conditions under
which wars make states precisely because taxes and loans may not exert the same lasting
effect on fiscal capacity. The bellicist hypothesis implicitly assumes that states service debt
following military conflict: that is, rulers exert a fiscal effort (e.g. enhance tax collection)
to honor their debt once war is over. However, debt service is uncertain. It depends on,
first, the financial capability of the state —e.g. war losers are less capable to meet fiscal
obligations (Centeno, 2002; Gennaioli and Voth, 2015; Slantchev, 2012)—, and second and
most importantly, the ruler’s willingness to repay (Reinhart and Rogoff, 2009; Tomz, 2007).
Some honor their debt in full and on time, others do not.
Certainly, few countries outrightly repudiate their debt (e.g. Turkey and Mexico in the
second half of the nineteenth century, or Russia in the early twentieth century); most renegotiate it (Lindert and Morton, 1989). However, renegotiating debt weakens the incentives
to invest in fiscal capacity. First, settlements might not involve a transfer of money. Instead
of raising taxes to repay, rulers may exchange public properties (including Crown monopolies and revenues, mining, or lands) for old bonds. We find examples of this in the Latin
American world throughout the nineteenth century (Marichal, 1989; Vizcarra, 2009). Second, default might come with substantial debt forgiveness, a common practice in the period
considered in the empirical analysis (Lindert and Morton, 1989). For instance, the 1870’s
default settlements in Latin American represented effective debt relief of almost 50% for
Bolivia, Chile, Peru, and Paraguay, and 40% in Costa Rica.6 Third, even when debt is
not forgiven, renegotiations usually involve reductions in interest and extensions of maturities that, in turn, may relax the incentives to enhance the extractive capacity of the state
(Marichal, 1989).
All in all, financing war with loans does not necessarily translate into an enhanced fiscal
capacity. By contrast, the more war is financed with taxes, the stronger fiscal capacity
6
Data from Marichal (1989, Table 4) and Jorgensen and Sachs (1988).
10
should be after military conflict. Financing war with taxes implies financial innovations
that transform the physiology of the state (Ardant, 1975): namely, new and professional
administrations, central banks, fiscal unifications or advances in indirect and direct taxation
outlast war, thus permanently enhancing the fiscal capacity of the state with respect to
prewar levels. Key to explain the legacy of war on state capacity, this collection of “selfstrengthening reforms” (Tin-bor Hui, 2004) tend to outlast war. Once in place, it is in the
best interest of the national ruler to keep them around, as they serve her to consolidate power
vis-à-vis local elites and political opponents (Besley and Persson, 2011; Dincecco, Federico
and Vindigini, 2011; Gennaioli and Voth, 2015; Tilly, 1990). Ultimately, the more wars are
financed with taxes relative to other means, the more likely is to achieve high fiscal capacity
in the long-run.
To illustrate the basic intuition behind this logic, I briefly present some evidence for Chile,
one of the countries with higher state capacity in Latin American today.7 Chile participated
in three wars in the nineteenth century: the Confederation War, 1836-1839, against Peru and
Bolivia; the Chincha Islands War, 1865-1867, against Spain; and the Pacific War, 1879-1883,
against Peru and Bolivia again. The first war was a smaller one, but the latter two required
a vast mobilization of resources at a national scale.8 Importantly, these wars were fought in
different financial contexts: the Confederation War and the Pacific War (first and last) were
fought while Chile was in default. By contrast, the Chilean-Spanish War was fought while
the country had access to the international financial market.
In light of the political payoffs of taxation, rulers are generally inclined to finance war with
loans rather than taxes. More specifically, I expect rulers to resort to taxation only when
they are pushed by circumstances: that is, when they are precluded from more politically
neutral options such as external borrowing. The way Chile financed war in the nineteenth
century is consistent with this logic. Figure 1 plots the share of tax revenue and public
7
And one of the few for which historical series of fiscal outcomes are available.
The Confederation War did not achieve the 1,000 battle deaths, a convention to be included in standard
war databases.
8
11
foreign debt as percentage of GDP from 1833 to 1913. The years in which Chile was at
war are shaded. However, I differentiate wars fought while Chile was in default (light gray)
—thus excluded from the international markets—, from wars fought while Chile had access
to the international credit market (dark gray).
Figure 1 here
The first lesson drawn from Figure 1 is that wars are financed with both debt and taxes.
However, consistent with the argument advanced in this paper, the debt/tax mix is less
favorable to taxes when rulers have access to the international credit market. Take the
two larger wars, the Chilean-Spanish War, 1865-1867, and the Pacific War, 1879-1883: In
1865, Chile was allowed to borrow from international lenders, and it did. Chile financed war
against Spain with external loans, which rose over 350% with respect to prewar years. In
stark contrast, tax revenue remained virtually unchanged during this period. Things were
different in 1879: Chile was again at war, but this time the country was in default, thus
excluded from the international credit market. Because war costs were pressing, Chile had
to finance the Pacific war out of its own pocket. Among other fiscal reforms, “[i]n May of
1879, in desperation, Congress passed the mobiliaria, the income tax it had rejected the
previous year” (Collier and Sater, 2004, p.147). Following this reform, tax revenue in Chile
reached unprecedented levels, and, importantly, yields never went back to prewar levels.
That is, the fiscal effort exerted to wage war outlasted conflict precisely because it involved
investment in fiscal capacity. Importantly, significant tax reforms were only adopted when
rulers were forced to by circumstances.
The Chilean example suggests that the effect of war on fiscal capacity hinges on the
financial instrument used to wage war. The rest of this paper investigates whether this logic
generalizes around the globe, taking into account potential endogeneity issues in access to
external credit and the decision to go to war.
12
3
Data
Cross-national conflict-specific data regarding how war is financed is not available in any
systematic way. In order to test how types of war financing affect fiscal capacity in the
long-run, I propose the following strategy: comparing the relative impact of war in periods
in which countries have access to external capital to periods in which they do not. The logic
of this test is based on the political economy of war financing: When external funding is not
an option, I expect the incentives to resort to domestic taxation to be strongest. That is,
it is only when they are pushed by circumstances, that rulers assume the political costs of
raising taxes to finance war.
In order to identify periods in which countries do not have access to external loans, I follow
two strategies. First, I focus on episodes of default (or endogenous access to credit), and
investigate how war fought while being in default (thus, forcing rulers to invest in domestic
taxation) affect long-term fiscal capacity as compared to war fought while having access
to international lending. Second, I exogenize external credit access by exploiting crashes
in the international financial market, which temporarily precludes access to external loans
irrespective of (un)observed characteristics of the country.
In order to estimate the effect of warfare on fiscal capacity, I follow Dincecco and Prado’s
(2012) strategy. These scholars use nineteenth-century measures of warfare and war casualties as instruments of current fiscal system. Dincecco and Prado (2012) find that countries
that fought more wars and suffered the largest number of casualties during the 1816-1913
period have higher ratios of direct taxes to GDP by the year 2000. The lower cut-off, 1816,
is deliberately picked to maximize the number of cases in the sample. Most countries in the
periphery are created only in the nineteenth century. The upper cut-off, 1913, serves three
purposes: First, by focusing on pre-modern wars, one guarantees that the fiscal effort that
a country exerts is driven by military need. The boom in welfare spending following World
War I (Lindert, 2004) makes it harder to identify the effect of war on fiscal capacity, as recipients of social programs create their own demand for higher taxation. In other words, by
13
dropping post-1913 wars, we make sure that the fiscal contract is mainly military. Second,
the two World Wars are unprecedented in lethalness, resource mobilization, but also in the
rules of social fairness. Scheve and Stasavage (2010) show that even elite’s resistance to high
taxation disappeared in light of the unparalleled human costs, thus rendering the Great War
hardly comparable to conventional warfare. Third, and related, given the huge financial
costs of the Great War,9 most participants were countries with high fiscal capacity to begin
with. Including total wars in the analysis would only exacerbate problems of selection.
Even though the design follows Dincecco and Prado (2012), I model the impact of premodern warfare on current fiscal capacity differently. Specifically, I condition the lasting
effect of war on types of financial instruments, not just casualties, while using three finer
proxies for current fiscal capacity:10
Among the various income taxes, I work with the Personal Income Tax (PIT), which
requires the capacity to enforce income-tax withholding, and a sophisticated organization
to assess and monitor compliance. PIT data (normalized to GDP) is drawn from various
sources. Chief among them is the IMF Global Financial Statistics (GFS).12 Consistent with
the theoretical claims, I work with PIT raised by the central government, as war is expected
to makes states by centralizing fiscal powers. To minimize influence of abnormal values, I
work with the average PIT value as a percentage of GDP from 1995 to 2005.13
Since PIT might capture both capacity and willingness to tax, a second outcome variable
is considered, one that emphasizes the infrastructural component of fiscal capacity: the Size
9
Refer to Centeno (2002, p.21) and Ardant (1975, p.224) for a detailed account of the unprecedented
costs of WWI.
10
Models will control for war casualties, nevertheless. First, I use the ratio of personal income taxes to
GDP. By many, it is considered the most sophisticated tax to date (Tilly, 1990; Webber and Wildavsky,
1986). Unlike trade taxes or excises, the income tax requires a sophisticated bureaucratic apparatus capable
of assessing private sources of income and monitor compliance of an atomized tax base.11 In light of its
implementation challenges, this tax sets a clear benchmark from which we can establish how far each country
has gone in building tax capacity.
12
IMF data represents almost 80% of the data. Appendix Table A-6 shows that data augmentation with
alternative sources does not bias the estimates of interest. Further data details can be found in Appendix
Section A.
13
This decade maximizes the sample size. For robustness, Appendix Table A-6 reports outcomes for
slightly different time periods.
14
of the Tax Administration circa 2005, measured as the number of staff employed by the tax
administration per thousand capita. Third, for robustness purposes, Appendix Table A-12
report models of Value-Added Tax (VAT) as a percentage of GDP, even though this form of
indirect taxation might require lower levels of bureaucratic sophistication than the income
tax (Bird and Gendron, 2007). Results are equivalent for the three outcome variables.
The nineteenth century is popularly known for being a peaceful time. However, the
hundred-year peace is only a phenomenon of the developed world (Flandreau and Flores,
2012). War did take place in this period, but mostly outside Western Europe. Appendix
Figure A-2 plots the location of these wars based on current state borders. That Figure
confirms that, consistent with the hundred-year peace, there is little military conflict in
the European core. However, countries in other regions, most prominently Asia and Latin
America, were at war for almost half of the period (some against European powers).
Most wars in this period were inter-state, involving European powers but also noninternationally recognized states (Butcher and Griffiths, 2015). Wars were fought against
colonial powers and also between neighboring countries, specially in Africa, Latin America
and Southeast Asia. In an effort to move beyond the experience of the developed world
with war-making, I work with Wimmer and Min’s (2009) war database, which includes all
military disputes by internationally and non-internationally recognized states around the
world since 1800.14
The use of non-internationally recognized states in the analysis assumes that theses political entities exert a fiscal effort in financing war comparable to recognized states. This is the
case of, for instance, the wars of independence in Latin America (Centeno, 2002; Marichal,
1989), the African wars before and after the arrival of the Europeans (see Reid (2012) and
Gardner (2012); Frankema (2011), respectively), or the inter-state wars over succession disputes in Southeast Asia (Butcher and Griffiths, 2015). Importantly, results do not hinge on
this assumption, as shown in both Table 6, in which only states recognized by the interna14
To make it into this dataset, military conflicts must experience more than 1,000 casualties.
15
tional system by the time they go to war are considered, and Table 8, in which Wimmer and
Min’s (2009) data are replaced by the more conservative war list in Sarkees and Wayman’s
Correlates of War.15
Wimmer and Min’s data stand out in three additional ways: first, wars are mapped onto
current state boundaries, making it possible to track which states should inherit the legacy
of war making, as well as investigate the effect of fighting war at home and abroad.16 Second,
Wimmer and Min (2009) distinguish civil from secessionist war. As part of the robustness
tests, the latter type (defined as fights against the political center with the aim to establish
an independent state) is considered. After all, secessionist war may contribute to revenue
maximization in a similar fashion than inter-state wars.17 Third, in Wimmer and Min (2009)
non-proxy wars fought by colonial subjects against third territories are attributed to the
colonial subject, and not to the metropolis, thus maximizing the match between war makers
and fiscal outcomes: e.g. wars fought by Egypt under Ottoman rule are not attributed to
Turkey but Egypt, which led the military campaign.
Altogether, I consider 147 armed conflicts between 1816 and 1913: 114 of them are interstate wars, and 33 are secessionist wars. For each of these wars, I establish whether access
to external credit is possible, either by identifying periods of default or exploiting crashes in
the international lending market. Specifically, I count the number of years at war in which
countries had access to the international capital market during this period, and the years in
which they did not. To account for characteristics of war other than duration, I control for
the number of casualties and location.
In order to establish whether a country actually had access to the international financial
markets, the first set of empirical analyses focuses on periods in which states were in default,
as listed in Reinhart and Rogoff (2009). These authors define sovereign default as the failure
15
Refer to Appendix A for further details.
Most wars can be easily matched to current states. A minority cannot: these are extinct political
entities the territory of which overlap with more than one modern state. Refer to Appendix A for further
details about matching old to new political units.
17
Civil wars are excluded from the analysis because their contribution to state building is yet to be
established. Refer to Appendix Section A for further details.
16
16
of a government to meet a principal or interest payment on the due date (or within the
specified grace period). Among the main causes of default, there is war, which reinforces the
main insight of the theoretical discussion: financing war with loans does not guarantee an
improvement in the fiscal capacity of the state with respect to prewar levels.
Reinhart and Rogoff code periods of external default starting as early as 1800 for 68
countries, as defined by their current territory. I work with 63 out the 68 countries in
their sample, all for which full data is available. The sample includes countries of the five
continents and accounts for approximately 90% of world GDP by 1913. The median duration
of default episodes in the period under consideration is six years (Reinhart and Rogoff, 2009,
p.81). Critically, while in default, countries are excluded from the international lending
market (Tomz, 2007), which I expect to strengthen the ruler’s incentives to invest in the tax
capacity of the state.
Given the levels of income tax today, and wars and default episodes in the long nineteenthcentury, I exploit cross-sectional variation in order to estimate:18
P ITi,1995−2005 = α + β1 (#years at war between 1816-1913 | no access to external loans)
+β2 (#years at war between 1816-1913 | access to external loans)
+X i δ + γ + ρ + i
(1)
where X denotes a vector of controls, γ and ρ are batteries of colonial origins and region
fixed effects, and is the error term. First, in the absence of external loans, I expect
war-making to strengthen the ruler’s incentives to invest in fiscal capacity, contributing to
long-term fiscal capacity, β1 > 0. Second, in light of the commitment problems of war debt
repayment, I expect a null (if not negative effect) of war-making when countries wage war
while having access to external credit, β2 6 0. A negative sign for β2 would suggest that
the fiscal disequilibrium associated with excess borrowing combined with the exchange of
state monopolies for default settlements can fully reverse the effect of war on state-making.
18
The analysis is cross-sectional. The data structure is not time-series-cross-sectional, as there is no
time-varying tax data for the nineteenth century for most of the countries in the sample.
17
Also importantly, the expectation β2 6 0 works against the Ricardian Equivalence, which
implicitly assumes no commitment problem of debt repayment. If borrowing and taxes are
equivalent in the long-run, we should expect β1 ≈ β2 > 0, everything else constant.
As part of Expression 1, all models below include: first, a battery of Region fixed effects
that account for continent-specific characteristics in the frequency of war, access to credit,
and statehood timing. Second, a battery of Colonial Origins indicators, as I expect access
to external credit of colonies, their opportunities to go to war, as well as the tax structure
that they build up to be influenced by the metropolis.19 For reference, Appendix Table A-13
investigates whether British colonies (and military allies) are comparable to the remaining
countries, given their unequal relationship with the financial capital of the world.
All models include a vector of potential confounders affecting the level of PIT today as
well as war participation, credit access, or both, back in the nineteenth century: First, I
consider a measure of initial wealth, as wealthier countries are more likely to go to war and
have stronger fiscal capacity in the first place (Tilly, 1990; Gennaioli and Voth, 2015). In
the absence of systematic GDP data for the early nineteenth century, I follow Acemoglu,
Johnson and Robinson (2005) and Dincecco and Prado (2012) and include a measure of
Population Density as of 1820, which is argued to be the best proxy of a country’s wealth
in the early industrial revolution (Tilly, 1990, p.17). Second, I also include two geographic
characteristics that could affect both sides of the equation. The first one, Sea Access, is
the percentage of the land surface area of each country that is within 100km of the nearest
ice-free coast (Nunn and Puga, 2012). I expect sea access to correlate with trade activity
(thus access to international lending) and monetization, a precondition for income taxation
(Tilly, 1990). By the same token, I expect territories with sea access to be military valuable,
thus increasing their likelihood of experiencing war. The second geographic control is the
percentage of territory that is Desert (Nunn and Puga, 2012). I expect deserts to inhibit
19
Accominotti, Flandreau and Rezzik (2011) show that UK colonies accessed the international lending
markets in the same terms as the metropolis, as it was perceived that London would assume the default risk.
See Obstfeld and Taylor (2003) for an opposite view. Following Persson and Tabellini (2003) and Dincecco
and Prado (2012), I include three colonial origins dummies: British, Iberian, and all other colonies.
18
industrial growth, and preempt monetization. But desert territory might also work as a
natural barrier to foreign invasion, thus reducing the frequency of war.20 Lastly, I control
for a close substitute to tax revenue that could also shape the incentives to go to war (or
being attacked): being an Oil Producer (Wimmer and Min, 2009). Arguably, this variable
gains relevance for the later years of the period under consideration.
4
Endogenous Access to External Credit
To establish a benchmark, column 1 in Table 1 tests for the unconditional version of the
bellicist hypothesis: that is, does long-term fiscal capacity increase in the number of years
at war in the long-nineteenth century, holding everything else constant? Or more generally,
does war make states? Results are mixed (consistent with what others have found): the
coefficient for # of Years at War between 1816-1913 is positive but not significant.
Table 1
Column 1 should be compared to column 2 and remaining specifications, in which I
distinguish the effect of war fought while in default, β1 , from war fought while having access
to international credit markets, β2 . Both point estimates are positive, but, consistent with
the political economy of war financing, only the former is significantly different from zero.
A one-standard deviation increase in the number of years at war while in default increases
income tax to GDP in 0.41 points. This is a 15% increase with respect to the PIT’s sample
mean.
On the contrary, column 2 suggests that wars that are fought when countries have access
to international markets do not exert any persistent effect on fiscal capacity. This is consistent with the commitment problem above indicated. Nothing guarantees that once war
is over, countries service debt within the pre-established timeframe and conditions. Some
20
Other geographic controls are endogenous and should not be included in the analysis: e.g. size. Others
correlate with military capacity but not with the dependent variable: e.g. terrain ruggedness.
19
countries honor their debt (by enhancing its fiscal capacity as to amass the required funds),
others do not.
Column 3 controls for the baseline propensity to default. To this end, I include the #
Years in Default between 1816-1913 of each observation. The two coefficients of interest
remain virtually identical. The remaining of Table 1 considers potential confounders, while
making sure not to control for endogenous variables or bad controls: for instance, based
on the political economy of war financing, Current Levels of Democracy, which strongly
correlate with PIT, might result from tax-financed war. Similarly, Current Levels of GDP
per Capita, which also correlates with PIT, are argued to result from war making in the
past (Dincecco and Prado, 2012; Gennaioli and Voth, 2015). Keeping that in mind, next I
consider only covariates that potentially condition war-making in the past and influence the
tax structure today.21
The first potential confounder, being a Great Power in the nineteenth century, is examined in column 4. This control accounts for the idiosyncratic paths of state- and war-making
in the United Kingdom, France, Germany, Italy, Austria-Hungary, and Russia.22 These
countries were major military and economic powers in the nineteenth century, and could be
driving the results. The coefficient of this indicator variable is positive, as one would expect,
but is not statistically significant. Importantly, β̂1 and β̂2 remain the same as in column 2
and 3.
Figure 2 offers a visual intuition of these estimates. The left-panel shows the partial
correlation between levels of PIT today and # Years at War while in Default, or β̂1 : this
relationship is positive and statistically significant. Four cases appear as potential outliers
in Figure 2: Peru, Argentina, Mexico and Prussia. Appendix Table A-7 reruns the model
without these observations. Once outliers are dropped, β̂1 ’s magnitudes triples. That is,
Table 1 offers the lower bound of β̂1 . The right-panel in Figure 2 plots β̂2 . Consistent with
21
For reference, Appendix Table A-15 reports models including bad controls. Results hold.
In establishing which country is a Great Power in this period, I follow Flandreau and Flores (2012).
Austria and Hungary are treated as two independent countries. Refer to Appendix A for details.
22
20
the commitment problems associated with debt-financed war, this estimate is positive but
does not reach standard levels of statistical significance.23
Figure 2 here
War causes destruction, but damages vary greatly depending on the location of military
engagement. The tax base can be badly hurt when military conflict takes place within
national borders, thus inhibiting investment in fiscal capacity. Additionally, countries might
invade others for extractive purposes. The location of war is thus likely to be a confounding
variable. To address this logic, column 5 in Table 1 controls for the location of conflict. In
particular, War Location is the sum of the years at war fought abroad minus the years at
war fought at home for the entire 1816-1913 period. This variable is positive when a country
fight more wars abroad than at home; negative, when military disputes at home are more
frequent than abroad; or zero, when countries never go to war.24 The coefficient for this
variable is positive, as one would expect, but not statistically significant. Importantly, the
coefficients β̂1 and β̂2 do not change despite the potential post-treatment bias induced by
this control.25
All wars are not created equal. Bloodier wars might overcome resistance to taxation,
while maximizing the ruler’s incentives to invest in fiscal capacity. To address this possibility,
column 6 includes a control for the intensity of warfare, measured by the number of battle
deaths within the period, or Casualties in 1816-1913 (Dincecco and Prado, 2012). This
variable is not statistically significant, even though its presence, if only marginally, pushes
down the magnitude of β̂1 , the effect of war fought while in default.
Next, I control for Ethnic Fractionalization and Civil Wars. The former might be an impediment to invest in fiscal capacity (Besley and Persson, 2011), while ethnically fragmented
countries might also be perceived as more vulnerable to foreign military intervention. Eth23
Appendix Table A-7 shows that, once we drop the four outliers, β̂2 becomes statistically significant at
90%. Yet, its magnitude is one-order of magnitude smaller than β̂1 .
24
One case only fought the same number of military campaign abroad than at home.
25
Results are virtually identical if the total number of wars fought abroad or at home are used separately.
21
nic fractionalization is measured as of the 2000s, and is potentially endogenous to war. A
long history of Civil Wars is a strong predictor of negative patterns of development (Besley
and Reynal-Querol, 2014), while lack of political stability might be penalized by the credit
market. Controlling for civil war, however, is far from ideal, as sometimes they result from
inter-state wars (although the opposite might happen too). At the risk of incurring in posttreatment bias, columns 7 and 8 control for the level of ethnic fractionalization today and
the number of years at civil wars between 1813-1916.26 The marginal effect of both controls
are positive, but they are not statistically significant. Importantly, the inclusion of these
variables does not change the effect of fighting wars while being in default.27
Lastly, Scheve and Stasavage (2010) show that progressive taxation, such as PIT, accelerated dramatically among countries that participated in World War I (WWI ). Including
this covariate in the empirical model might lead to a post-treatment bias if countries that
frequently went to war in the nineteenth century and developed higher fiscal capacity by 1914
selected into WWI. Still, one might be tempted to include a WWI indicator to check whether
the coefficients of interest survive this control. As reported in column 9, they do: fighting
war while being on default is positively related to higher fiscal capacity today, whereas fighting war while having access to external credit, does not. Participating in WWI is positive
but does not reach conventional levels of statistical significance.28
4.1
Alternative Endogenous Sources of War Financing
There are three additional, arguably less frequent, ways to finance war: domestic borrowing, monetary expansion (also known as printing money), and financial repression. Appendix
H addresses the effect of the domestic borrowing and monetary expansion empirically. Re26
To minimize bias, civil wars that take place simultaneously to inter-state wars are not considered. These
are a minority, nevertheless.
27
Appendix Table A-9 includes a control for the Federal structure of the state as of today, which might
reflect cumulated ethnic fractionalization.
28
The WWI indicator takes value 1 for all countries that actively participated in WWI (i.e. suffered
military casualties). This coefficient achieves conventional levels of significance in later models, when the
sample size increases.
22
sults strengthen Table 1’s: war-making is even more consequential when it is fought in periods
of external and domestic default and no monetary expansion. Likewise, access to domestic
credit and instances of monetary expansion do not cancel the effect of fighting war while in
default. Other policy, such as financial repression, office selling or confiscation introduce a
downward bias, if any, on the main coefficient of interest, β1 . That is, if rulers prioritize
fiscal repression when they lack access to external finance, we should not expect a positive
coefficient for the # Years at War while in Default, precisely because fiscal repression is
implemented as to avoid fiscal capacity building.
5
Exogenous Access to External Credit
Going to war and being in default are not randomly assigned. There might be unobserved
elements that make states more (less) likely to go to war and more (less) likely to be in default
that affect their fiscal capacity today. So far, the first source of bias is addressed by the initial
wealth proxy (i.e. population density as of 1820), which accounts for who goes to war more
often in the first place: richer countries. The Great Power indicator and Region fixed effect
contribute to minimize bias. The second source of endogeneity, namely, who is in default in
the nineteenth century, is addressed next by exploiting shocks in the international lending
market since 1816. As it will become clear, crashes in the international financial market dry
capital flows at a global scale, a phenomenon known as sudden-stops of credit (Calvo, 1988).
Key for the identification strategy, sudden-stops preclude countries from external borrowing
irrespective of their (un)observed characteristics. In other words,
“Banking crises in global financial centers (and the credit crunches that accompany
them) produce a ’sudden-stops’ of lending to countries at the periphery [...]. Essentially,
capital flows from the ’north’ dry up in a manner unrelated to the underlying economic
fundamentals in emerging markets.” (Reinhart and Rogoff, 2009, p.74).
In this section, I use sudden-stops as a form of exogenous variation of access to external
23
credit, which structures the incentives to invest in fiscal capacity for countries at war. Importantly, this identification strategy is superior to using bond spreads to measure access to
external credit (e.g. Shea 2013), as spreads are intrinsically endogenous.
5.1
International Financial Crashes in the Nineteenth Century
Most of the international credit in the long nineteenth century was channeled through
the London Stock Exchange (LSE). London took over Amsterdam as the financial center of
Europe by the turn of the eighteenth century (Neal, 1990). The LSE’s financial leadership
consolidated throughout the nineteenth century, when it became the world’s leading capital
exporter, far exceeding the combined capital exports of its nearest competitors, France and
Germany (Feis, 1930). Table 2 reports the best approximation of the market shares in
lending throughout the nineteenth century. These data indicate that for the long nineteenth
century, the British were “the bankers of the world” (Obstfeld and Taylor, 2004).29 At its
peak, the British share of total global foreign investment was almost 80%. This contrasts
with the US share of global assets in 2000, 25%, and even with the US maximum share of
50% circa 1960 (ibid.). All in all, in the nineteenth century, the LSE played an unmatched
role in financing the world.
Table 2 here
Conveniently enough, the LSE was not immune to crises. Table 3 enumerates the onset of
all banking panics and stock crashes experienced by Britain in the long-nineteenth century,
as listed in Reinhart and Rogoff (2009). Given Britain’s central position in the international
lending market, crashes in London rapidly spread to Paris, Frankfurt and New York. Contagion took different routes, including arbitrage in commodities and securities, and movement
of money in various forms (specie, bank deposits, bill of exchange), cooperation among monetary authorities, and pure psychology (Kindleberger and Aliber, 2005, ch.7). One way or
29
This conclusion is shared by many others: e.g. Reinhart and Rogoff (2009), and Tomz (2007).
24
another, financial crashes in London dried international lending at a global scale (Bordo,
2006).
Table 3 here
Importantly, the causes of the financial collapses in the nineteenth century can be found
in the British economy, not abroad. This is certainly the case for the major crises of 1825,
1847, 1857, and 1866, but less true for the 1890 panic, when a big financial imbalance in
Argentina put a halt to British lending (Kindleberger and Aliber, 2005).30 More importantly,
British panics did not respond to defaults by borrowers, which would cast doubts about the
exogeneity of these shocks. Most of the countries that defaulted in the nineteenth century
were in the periphery. Although the defaulted quantities were significant relative to their
home economies, from a global prospective, they were a “sideshow” (Eichengreen, 1991,
p.151). All things considered, we can safely treat the periods of sudden-stops as exogenous
to every country except for Great Britain and, arguably, 1890 Argentina.
For illustration purposes, Figure 3 shows the evolution of British capital exports since
1865 (earlier data do not exist), while indicating the years of banking panics and stock
crises as coded by Reinhart and Rogoff (2009). Figure 3 reflects the boom-and-boost cycles
preceding and following a banking crisis, as exemplified by the financial crisis of 1873 and
1890. Prior to each boost, lending was ferocious. Once the debt bubble exploits, international
capital flows temporarily dry across the board. Precisely, it is within periods of sudden-stops
that I expect rulers to have stronger incentives to finance military campaigns by means other
than external borrowing: namely, taxes. But, unlike the case of default episodes (previous
section), the incentive system is now structured by exogenous factors.31
Figure 3 here
30
For the domestic origins of banking panics, see Neal (1998) for 1825 crisis, Dornbusch and Frenkel
(1982) for 1847 crisis, and Mahate (1994) for 1866 crisis. See Marichal (1989) for an exonerative assessment
of Argentina in the 1890 crisis.
31
Based on Figure 3, banking crises might be more damaging than stock market crises. Appendix Table
A-10 includes an additional test in which only banking crises are considered.
25
Crucial for the quasi-experimental setting, financial bursts are predictable only ex post, as
Reinhart and Rogoff (2009) convincingly argue. But suppose that some rulers had inside information and banked external loans in anticipation to sudden-stops. If the theory advanced
in Section 2 is correct and the incentives to invest in fiscal capacity are a function of credit
access, anticipation implies that rulers will not invest in fiscal capacity even if the financial
markets are down. That is, anticipation creates an attenuation bias on the coefficient of
interest, β1 in Expression 1.
As Figure 3 also indicates, markets do not rebound immediately after a financial crisis.
Although the final resolution of financial crises varies (Chwieroth and Walter, 2013), before
WWI, sudden-stops lasted four years on average (Catao, 2006). Accordingly, I establish
4-year windows (including the year at which the crisis begins) within which I assume that
countries do not have access to external credit.32 For each of these lapses of time, I count
the number of wars that a country was involved in. To fully test the theoretical expectation,
I also compute the number of years at war that a country fights while credit flows in the
international market. Importantly, the sample size grows with respect to the previous section,
as it is not constrained by Reinhart and Rogoff (2009)’s default data coverage.
Table 4 here
To evaluate the validity of the exogenous credit shock, Table 4 compares the frequency
and duration of war in periods in which access to credit is endogenous vs. periods in which
it is exogenous. The statistics for the endogenous access to credit suggests that countries
strategically choose when they go to war: specifically, 87% of wars coincide with periods in
which countries are not in default. Likewise, wars are shorter when countries are in default:
2.0 years compared to 2.3 years when countries have access to external finance. These figures
change when we evaluate scenarios of exogenous access to credit: 52% of wars now coincide
with periods in which the international lending market is down, whereas the duration of
war is also balanced: 1.9 years in periods of sudden-stops compare to 2.0 years when credit
32
Refer to Appendix Table A-10 for longer windows.
26
flows.33 Altogether, these numbers confirm that sudden-stops are unanticipated and that
they do not condition the decision to go to war, nor to retreat from it. If we judge war by its
frequency and duration, Table 4 suggests that we are comparing animals of the same kind
when tackling with wars waged in periods in which international lending flows with wars
waged in episodes of sudden-stop of credit.
5.2
The Effect of Sudden-Stops
The analysis in Table 5 uses the periods in which the LSE does not issue credit as a
means to identify scenarios in which countries at war have stronger incentives to enhance its
fiscal foundations. The model specification follows Expression 1. This time, however, Great
Britain —the world’s banker—is dropped so as to maximize exogeneity.34
Table 5 here
Column 1 establishes the benchmark model, in which the simplest, unconditional bellicist
hypothesis is tested against the data. Results are now mildly positive: at a 90% confidence
interval, PIT today increases in the number of years at war in the long nineteenth century,
keeping everything constant. Column 1 should be compared to column 2, in which I distinguish wars fought while having exogenous access to external credit from wars fought in the
midst of a sudden-stop.
Column 2 suggest that the effect on current fiscal capacity critically depends on how war
is financed. On the one hand, the effect is positive when it leaves the ruler out of options
and pushes her to invest in fiscal capacity: a one standard deviation increase in the number
of years fought while international lending stops, increases 1.3 points the average PIT today,
equivalent to a 43.3% increase with respect to the sample mean. On the other hand, the
effect of war-making when rulers have access to external lending is virtually the reverse: a
33
For a visual intuition of the same result, refer to Appendix Figure A-6.
Appendix Table A-13 shows results upon dropping British colonies, British military allies, even every
war involving British participation.
34
27
one-standard deviation increase in the number of years at war when credit is available is
associated with a decrease of 0.9 points in PIT as percentage of GDP. This result suggests
that debt-financed war might create fiscal imbalances that are too hard to fix. These should
be strongest among those states that handle over state monopoly revenues to lenders in order
to regain market access after defaulting.
The opposite signs of β̂1 and β̂2 suggest that the effect of war estimated in column 1 is the
average of two radically different worlds. Indeed, this result advances our understanding of
the conditions under which wars make states. The remaining columns in this and subsequent
tables establish how robust this result is to endogeneity and measurement issues.
Columns 3 to 8 in Table 5 include additional controls, one at a time: the # of Years in
Default, being a Great Power, War Location, War Casualties, Ethnic Fractionalization, and
Civil wars, in this order. Additionally, now I control for Average War Duration, as it is no
longer collinear with having access to external credit, as shown in Table 4. Across different
specifications, the two coefficients of interest, β̂1 and β̂2 , remain virtually unchanged with
respect to column 2.35
Figure 4 offers an visual intuition of β̂1 and β̂2 . The left panel shows the partial correlation
between PIT as a Percentage of GDP and # of Years at While Credit Stops, or β̂1 . There are
arguably three outliers in this relationship: Russia, Georgia, and France. Appendix Table
A-7 shows that their exclusion does not affect the size and precision of β̂1 . The left panel
plots β̂2 . Russia and Georgia are again outliers. Appendix Table A-7 shows that they are
indeed influential, as once we drop them, β̂2 is not statistically significant any more. This
null result for β̂2 is consistent with some of the robustness tests that follow.
Figure 4 here
Lastly, in column 9, I control for participation in WWI, despite the problems of endogeneity discussed above. Results hold. More interestingly, the coefficient for WWI sets a
35
Notice that we do not have to adjust for the total number of years in which capital flows or dries, as
this is a common shock to every country and is picked up by the intercept.
28
meaningful benchmark to compare conventional war-making in the long nineteenth century
vis-à-vis total war : the latter’s marginal effect on fiscal capacity is 1.2 points, whereas a
one-standard deviation increase in the number of years at war while not having access to
external credit increases PIT today by 1.3 points. Results are virtually equivalent, meaning
that sufficient years of conventional war, as long as they are (at least partially) financed with
domestic taxes, might exert lasting effects close to total war participation.
To sum up, Table 5 suggests that war does not necessarily make states. It all depends
on the incentives that rulers have to invest in the fiscal capacity of the state, which, I argue,
should be stronger when they lack access to external loans, and weaker when they have access
to cheap credit in the international lending markets. Before discussing the implications of
this result, Tables 6-8 address additional measurement and endogeneity issues.
5.3
Sovereign States, Secessionist War, and Alternative Dependent Variable
So far, wars are attributed to the corresponding 2001 nation state irrespective of whether
that territory had achieved statehood by 1913. It could be argued that wars fought in
territories that were not recognized by the international system exert a different (or null)
effect on the fiscal capacity of the state. For instance, colonies might not invest in their
military campaigns as much as the metropolis. To address this possibility, columns 1 to 3
in Table 6 rerun the analysis considering only countries that were sovereign by war time.36
These models also control for Great Power status and War Location. Results, despite the
reduction of the sample size, are similar to those reported in Table 5. Sovereign states that
waged war while international lending flowed are not associated with high fiscal capacity
today. To the contrary, sovereign states that waged war in the midst of a sudden-stop have,
on expectation, higher tax capacity today.
Table 6 here
36
Sovereign status is drawn from Boix, Miller and Rosato (2013).
29
The second battery of sensitivity analyses moves in the opposite direction by considering
secessionist war along inter-state wars. Secessionist wars is fought by entities that are, by
definition, not sovereign. Secessionist war might or might not seek the formation of an
independent modern nation-state. Next, I consider secession war that pursued this goal, as
established by Wimmer and Min (2009).
Many developing countries waged secessionist war in the nineteenth century. Thus, considering these wars makes the sample more representative of the universe of states at war in
the period under consideration. Importantly, as reported in the bottom of Table 4, once secessionist wars are considered, the frequency and duration of war in periods of sudden-stops
and credit-flow are fully balanced, thus maximizing comparability. Columns 4 to 6 in Table 6
suggest that waging war, either inter-state or secessionist, while international credit markets
are operative, is not associated with higher fiscal capacity. On the contrary, countries that
fought wars in the midst of a sudden-stop, regardless of international statehood recognition,
are associated with higher levels of fiscal capacity today.
The third battery of sensitivity tests addresses the choice of the outcome variable: personal income tax as a share of GDP, arguably, captures both capacity and willingness to tax.
To address this potential issue, I use an alternative proxy of fiscal capacity, one that emphasizes capacity over willingness: the size of the tax administration (US AID, 2012). First,
professionalized bureaucracies are necessary to assess and collect taxes, as well as to resist
the natural aversion to having one’s sources of income examined and monitored (Daunton,
2001; Mares and Queralt, 2015). Second, modern tax bureaucracies were created for and by
war (Acemoglu, Ticchi and Vindigni, 2011; Brewer, 1988) and tend to be fairly persistent
(Fischer and Lundgreen, 1975; Schumpeter, 1918). Third, the tax administration, filled by
public servants and subject to stricter controls, is relatively sheltered from spurious, fleeting interests of passing incumbents and, as a result, should change only slowly over time.
Altogether, the size of the tax apparatus captures the underlying capacity to monitor and
assess private income, that is, the fiscal capacity of the state. Consistently, the size of the
30
tax administration is a strong predictor of total tax revenue, as shown in Appending Figure
A-1.
In columns 7 to 9, the size of the tax administration, measured by the tax staff per thousand capita circa 2005, is regressed on the same covariates used to model income taxation.37
Results mimic previous ones: waging war while not having access to external credit (exogenized by instances of sudden-stops) is associated with a more staffed tax administration
today. War waged while credit flows is not, if any, results suggest that the opposite might
happen.
Additionally, Appendix Table A-12 reports models of Value-Added Tax (VAT) revenue
as a percentage of GDP. VAT is arguably easier to implement than the income tax (Bird
and Gendron, 2007), and it may not capture accumulated investment in fiscal capacity as
precisely as income tax does. Still results for VAT are equivalent to those presented so
far. Altogether, Table 6 shows that results in Table 5 are robust to sample changes, and
alternative war definitions and outcome variables.
6
Addressing War Endogeneity
The decision to go to war may be endogenous. First, countries that go to war might
have greater administrative capacity to begin with (i.e. omitted variable bias). Second, the
type of countries that decide to go to war when there is no access to credit may be different
in ways that are relevant to future tax collection capacity from those which choose to wait
until loans are available (i.e. selection bias). I address both issues stepwise.
6.1
Initial State Capacity
First, countries that are frequently at war may have greater capacity to conscript and
tax. These capacities may already be captured by the Great Power indicator and the proxy
37
Despite the smaller N, the sample still includes countries of the five continents.
31
of initial wealth: Population Density as of 1820. After all, we know that the income level is
the strongest predictor of war participation (Gennaioli and Voth, 2015; Tilly, 1990). Nevertheless, next I minimize this potential source of endogeneity by considering two covariates
associated with initial state capacity: an index of state antiquity (Bockstette, Chanda and
Putterman, 2002), and a new one on census capacity. Following Tilly’s logic, State Antiquity
should correlate with accumulated military capacity: that is, older states exist because they
won war in the past. Second, the capacity to successfully conduct a modern census should
correlate (if not facilitate) preparation for war, if only because modern censuses tend to follow earlier enumarations that assess taxable wealth and the conscription base. To this end,
I have collected information about the first census ever conducted in every country in the
sample. To control for initial administrative capacity, I create the indicator variable Modern
Census by 1820, which equals 1 if a country x has conducted a modern census by 1820.38
Table 7 here
Results are reported in columns 1 and 2 of Table 7. The two new covariates hold a
positive coefficient, as expected, but are not statistically significant.39 Importantly, once we
control for both proxies of initial state capacity, the coefficients of interest, that is, β̂1 and β̂2 ,
remain positive and negative, respectively, and statistically significant. That is, independent
of observable initial capacity to prepare for war and raise taxes, only countries that fought
war when the international lending market was down are associated with higher levels of
fiscal capacity in the long run.
6.2
Ongoing Wars
Countries that go to war despite the credit dry may be different from countries that wait
for markets to lend again. Table 4 (and Appendix Figure A-6) suggests that there is no much
38
See Appendix for further details.
They reach conventional level of statistical significance when I exclude other covariates, with which they
correlate.
39
32
strategic timing of war making once we exogenize credit access: the frequency and duration
of war inside and outside sudden-stop periods are virtually balanced. Still, we might want
to address selection bias by considering only wars that are initiated while the market is still
lending and, eventually, dries as a result of a financial crisis. That is, these are wars that
are initiated without the expectation of a sudden-stop.
Columns 3 and 4 in Table 7 report the results of this test. The estimate for # of
Years at War while Credit Stops decreases by 30%, approximately, with respect to Table
5, suggesting that the latter may be somewhat upward biased. Still, the new estimate is
positive and statistically significant in both specifications. On the other hand, the effect of
fighting wars while having access to the international market is no longer negative, but zero,
which is still inconsistent with the unconditional interpretation of Tilly’s dictum.
For further robustness, Appendix Table A-14 addresses selection bias in a reduced-form
framework, where war participation by country x in times of sudden-stops and credit-flow
are instrumented by war-making by the immediately adjacent countries. Results hold.
6.3
Involuntary War, COW and War Outcome
A potentially more interesting way to address selection into war is to analyze the effect of
war-making by states that did not choose to go to war, but were dragged into it. In particular,
one could argue that countries that initiate war might be different from those that are
attacked in ways that shape current fiscal capacity. Based on this logic, this analysis focuses
on the differential effect of war-making and credit access for countries that are attacked only,
(i.e. the non-initiators). The identification assumption here is that initiators do not strike
first in anticipation of a likely attack.
Table 8 here
To conduct this test, I rely on Correlates of War (COW) data, which identifies the
initiator of each military conflict. The COW dataset includes fewer inter-state wars than
33
Wimmer and Min (2009), as it follows a stricter criterium about what a state is in the
nineteenth century.40 Accordingly, the sample of inter-state wars is now made of 37 conflicts,
and 174 country-year-wars in total. 78 of them were fought when credit flowed, and 96 when
credit suddenly stopped. The average duration was 1.57 (sd = 1.04) and 1.76 (sd = 1.22)
years, respectively.
On the bright side, COW indicates which side eventually wins war. This is substantively
compelling, as military outcomes potentially affect the incentives to invest in fiscal capacity:
e.g. winners might extract from losers. To this end, Net Victory indicates the number of
wars won between 1816 and 1913 by country x net of wars lost within the same period.
Countries that did not fight any war have a value of 0.41
Table 8 begins by replicating the differential effect of war and credit access for the entire
COW sample, including initiator and non-initiators. The effects reported in columns 1 and
2 are slightly lower than those estimated in previous tables. Based on column 1, a onestandard deviation increase in the number of wars fought while not having access to credit
increases PIT today by 0.8 points (equivalent to a 27% increase with respect to the sample
mean). Most probably, the decrease in the predicted effect results from the sample selection
of COW, which includes richer countries, for which additional years at war should exert
a relatively smaller effect. Importantly, columns 1 and 2 imply that results are robust to
sample change.
In columns 3 and 4, I estimate the effect of war and credit access for countries that did
not choose to go to war, but were pushed into it: that is, the non-initiators. Results are
similar to the preceding ones, only bigger. Countries that were dragged into war in the midst
of a sudden-stop of credit present higher levels of fiscal capacity today. The effect vanishes
when countries are allowed to borrow external loans to wage war. Importantly, results are
robust to military conflict outcomes: winning or losing wars do not significantly modify the
40
Refer to Appendix Section A for further details.
Three countries won the same number of wars that they lost: Bulgaria, Spain, and Turkey. All other
zeros correspond to countries that fought no war within the period.
41
34
differential effect of war-making and credit access on long-term fiscal capacity.
7
Short-term Effects
I have argued that tax-financed war exerts long-term effects on fiscal capacity, as it pushes
rulers to conduct institutional reforms, such as enhancing the tax bureaucracy, adopting new
taxes, or building up a central bank. Consistent with this logic, we should observe some
effects in the short-term too, as fiscal capacity is a cumulative process. In the absence of
tax data for current developing economies in the early twentieth century, I focus on three
measures of state capacity that should correlate with tax capacity: the ability to conduct
a modern census, the length of open rail lines, and primary education enrollment, all dated
as of 1913. The first measure is clearly a requirement to adopt modern forms of direct
taxation, as it establishes the potential tax base. The second measure, rail lines length,
captures Mann’s (1984) notion of “infrastructural power” of the state, as it facilitates the
state’s presence throughout the territory. Importantly, Dincecco, Fenske and Onorato (2016)
and Queralt (2015) show that the railroad network correlates with fiscal capacity. The third
measure, primary schooling enrollment, captures a cornerstone characteristic of the modern
state, public-funded mass education, that requires an institutionalized bureaucratic structure
to recruit instructors and standardize curricula (Gellner, 1983).
Table 9 here
Table 9 reports the effects of war-making on short-term state capacity proxies as a function of exogenous credit access. In columns 1 and 2, a probit model regresses having a modern
census by the end of the period of reference on war-making and external credit availability,
plus controls. Results suggest that waging war during the long-nineteenth century in the absence of external loans increases the probability of having a modern census by 1913. On the
contrary, being at war while having access to external credit does not translate into higher
probability of having conducted a modern census by the end of the period of reference.
35
Columns 3 and 4 fit an OLS model in which the date of adoption of the first modern
census is regressed on key regressors. In this model, high values of the dependent variable
imply delays in census adoption. These columns show that fighting wars in times of suddenstop shortens delay of adoption (or, if preferred, accelerates it). Fighting wars having access
to credit does not. If any, it increases delay, consistent with commitment problems of debt
repayment.
Results in column 5 to 7, in which I model the length of rail lines by 1913, mimic previous
results.42 Importantly, results are robust to early state capacity and early levels of rail line
lengths (measured as of 1850 to maximize the sample size). In the same vein, columns 8
to 10 show that war-making while credit stops does not predict higher enrollment ratios by
1913, whereas war-making while credit stops does.43 Results are robust to enrollment ratios
by 1820.
8
Transmission
To complete the analysis, next I address whether and why the effect of war finance travels
over time. To this end, I investigate the effect of war and credit access from World War II
until 1995. Given data constraints, to proxy fiscal capacity I rely on the share of total tax
revenue that is not derived from trade taxes. This variable measures the state’s effort to
raise revenue through sophisticated taxes, such as the income tax or the value-added tax
rather than tariffs, a tax-handle that low fiscal capacity countries frequently use (Queralt,
2015).44 Second, given the small N, I specify a less populated model in which I limit controls
to having adopted a census by 1820, having access to oil revenue, and having a colonial past
(models can be found in Appendix A-16).
Figure 5 here
42
Rail length data is drawn from Comin and Hobijn (2010).
Enrolment data is drawn from Lee and Lee (2016).
44
Tax data is drawn from Cagé and Gadenne (2016). Average non-trade tax to total tax revenue in
1986-1995 is 81%.
43
36
Figure 5 summarizes the results of this test. For each decade, it shows the marginal
effect of years at war between 1816 and 1913 on post-WWII fiscal capacity as a function of
exogenous credit access in the long nineteenth century (refer to Appendix Table A-16 for
details). Results suggest that fighting pre-modern war while having access to credit is not
associated with post-WWII fiscal capacity, whereas fighting war while not having access to
credit is. This results suggest that the effects of pre-modern war shown in Tables 5-8 do
persist until today.
The question is why. I argue that the institutional reform associated with tax-financed
war persists through two mutually-reinforcing channels: one is political, the other is bureaucratic. First, rulers are revenue maximizers (Levi, 1988), thus they have a strong interest to
preserve extractive institutions once military conflict is over. As it was argued in Section 2,
rulers have few incentives to assume the political costs of enhancing tax capacity. But once in
place, having the capacity to extract income through taxation is a valuable asset that makes
it self-preserving. Revenue might be used to extract from the opposition and/or build political coalitions throughout strategic spending. Fiscal centralization also consolidates central
power vis-à-vis subnational elites. Key for persistence, national rulers are reluctant to cede
power back over the newly created fiscal institution after war, thus locking the state into
a centralized fiscal equilibrium (Dincecco, Federico and Vindigini, 2011). Importantly, the
interest of rulers to raise revenue does not vary between regime types. This has been shown
qualitatively (Levi, 1988) and quantitatively (Cheibub, 1998). Consistently, Appendix Table
A-17 shows that the patterns in Figure 5 hold after controlling for regime type (and despite
problems of bad control bias).
Second, modern bureaucracies were created for and by war (Acemoglu, Ticchi and Vindigni, 2011; Brewer, 1988). Professionalized bureaucracies were necessary to assess and
collect taxes, as well as to resist the natural aversion to having one’s sources of income
examined and monitored (Daunton, 2001; Mares and Queralt, 2015). Once created, bureaucracies are there to stay, grow bigger, and, arguably, become a state within the state
37
(Schumpeter, 1918). Tilly (1990, pag.115) summarizes this logic as follows:
“The organizations that were necessary to amass revenue [for war] developed interest,
rights, perquisites, needs, and demands requiring attention on their own. [...] Bureaucracies developed their own interests and power bases throughout Europe.”
That is, institutions that were originally built up to finance the means of war gave rise to a
corpus of bureaucrats that naturally developed a vested interest in safeguarding institutional
survival. Bureaucracies maximize this goal by increasing their size and financial endowment
(Niskanen, 1994, ch.4). Accordingly, we can expect tax bureaucracies to oppose disinvestment in administrative capacity, carrying on, ultimately, the effect of war-making on current
fiscal capacity. Columns 7-9 in Table 6, where tax administration size is regressed on past
warfare and credit access, lean support to this mechanism.
Together, political and bureaucratic interest facilitate the transmission of fiscal capacity across generations, thus linking tax-financed pre-modern warfare and long-term fiscal
capacity.45
9
Discussion
Contrary to the unconditional characterization of the bellicist hypothesis, that is, more
war, more state, I argue —along Tilly’s original work—that the effect of war on state-building
ultimately depends on how warfare is financed. Specifically, I claim that financing war with
taxes makes states for certain, whereas financing wars with loans might not, as there are
commitment problems with debt repayment. The research hypothesis has been tested with
cross-national historical data, including developed as well as developing economies to avoid
sample selection issues, and evaluated in both the short- and long-run. To address the
endogeneity of war financing, the empirical analysis exploits sudden-stops of credit, and
shows that war fought while not having access to external credit is positively associated
45
After WWI, there is a third mechanism for the survival of investments in fiscal capacity: demands of
welfare policy (Peacock and Wiseman, 1961).
38
with higher fiscal capacity in the long- (and short) run. By contrast, financing wars with
external capital does not translate into higher tax capacity in the long- (or short) run, and it
can result detrimental, possibly due to some perverse conditions of default settlements: e.g.
handing over state monopolies and revenues to lenders. To address the endogeneity of war
participation —unlikely conditional on sudden-stops—, I control for initial state capacity,
focus on ongoing wars (that is, wars that are initiated while the market is still lending and,
eventually, dries as a result of a financial crisis), and on countries that are dragged into war
(i.e. the non-initiators). Results hold across specifications, war characteristics (war outcome,
average, location, fatalities and military alliances), as well as alternative dependent variables:
current income tax, value-added tax, and size of the tax administration; 1913 census capacity,
railroad network and school enrollment rates; and non-trade taxes for the period between
1945 and 1995. The powers conferred by fiscal centralization to the national ruler, combined
with the vested interest in self-perpetuating by the same bureaucracies that were once created
to finance war, are advanced as the mechanisms of transmission.
This is not the first work that qualifies the bellicist hypothesis. In evaluating its applicability to Latin America, Kurtz (2013) claims that this continent did not have enough societal
cohesiveness to invest in fiscal capacity, nor sufficiently robust central governments to make
domestic extraction profitable. Likewise, Karaman and Pamuk (2013) argue that the effect
of warfare on state capacity in pre-modern Europe is conditional on the complementarities
between urbanization and regime type. Interesting as they are, these works do not account
for the radically different international context in which countries in the periphery are created
as compared to those faced by European nations.
The vast majority of states in the periphery are founded only after 1815, coinciding with
the globalization of financial markets, which results from the income growth in the wake
of the industrial revolution, as well as Britain’s capacity to spin off excess savings (Neal
and Weidenmier, 2003; Reinhart and Rogoff, 2009). The volume of crossborder loans in the
nineteenth century is unprecedented: scaled by the size of the world economy, international
39
capital flows between 1880 and 1914 are three times as large as in the 1980s (Eichengreen,
1991, p.150).
Unlike European states, from their very inception the new states in the periphery had
access to unprecedented levels of inexpensive external loans, despite their weak institutionalization, frequent government turnover, and lack of reputation in the international markets
(Tomz, 2007).46 The “lending frenzy” (Taylor, 2006) lasted only temporarily. By the end
of the nineteenth century, as a results of (inevitable) defaults in the periphery, markets did
update the premia for proven lemons (Lindert and Morton, 1989; Tomz, 2007). By then,
however, many wars had already been fought.
Cheap external credit, I argue, unravels the relationship between war-making and statemaking for three reasons: first, it allows war to be financed without raising taxes domestically,
thus inhibiting political reform often associated with a well-articulated tax system: namely,
representative political institutions with budgeting veto powers (Bates and Lien, 1985). Second, readily available, inexpensive external credit preempts the development of domestic
credit markets, thus the formation of a corpus of domestic lenders with whom to strike bargains conducive to political reform and responsible fiscal policy (North and Weingast, 1989;
Stasavage, 2011). Third, the globalization of lending markets exacerbates the commitment
problems associated with debt servicing. It facilitates the possibility of refinancing debt with
more debt instead of investing in fiscal capacity, thus heightening debt burden rather than
solving it. Counterintuitively enough, countries in the periphery may have benefited from
less efficient international lending markets, as that would have strengthened the incentives to
raise taxes to finance the means of war, stimulate domestic borrowing, and conduct political
reform associated with responsible fiscal policy —namely, what European counterparts were
pushed to do, only centuries before, when credit markets were oligopolistic and expensive
(Homer and Sylla, 2005, ch.9).
The perverse effect of inexpensive external credit resonates with the original Tillyian
46
The lending frenzy is sustained on strong information asymmetries, speculative operations, and blatant
fraud (Taylor, 2006). Appendix N provides further evidence of this phenomenon.
40
hypothesis by emphasizing the conditional effect of war on credit access: in Europe, frequent
war-making and the absence of cheap external credit propelled domestic lending, and eventually political reform that addressed commitment problems of debt repayment.47 Together,
frequent warfare and domestic lending allowed territorial states to pursue the “coercivecapital intensive”, fiscal-military strategy that ended in the modern tax state (Tilly, 1990).48
Cheap external credit —which countries in the periphery had since their inception—breaks
the causal chain of the original Tillyan hypothesis. Readily available external credit unravels the necessity to finance war with domestic debt or taxes, and ultimately, preempts the
capacity to capitalize war efforts.
Others have warned about the perverse effects of cheap credit: Centeno (2002), Shea
(2013) and Thies (2007) claim that this form of artificial wealth distorts the incentives to tax
domestically even in times of war. Building on this work, I advance the political economy
of war financing, articulate the implications for long-term fiscal capacity, and test them
against historical data. Interestingly, the perverse incentives associated with cheap credit
are equivalent to those derived from other forms of non-tax revenue: foreign aid (Bueno de
Mesquita and Smith, 2013; Moss, Pettersson Gelander and van de Walle, 2006), oil and gas
revenue (Morrison, 2009; Ross, 2001), and ores from colonies (Drelichman and Voth, 2011).
Altogether, the paper offers a better understanding of the conditions under which war exerts
positive and lasting effects on state building.
I leave for future research investigating the extent to which the mix of internal and
external credit advances our understanding of the heterogeneous paths to state building in
Western Europe between the sixteenth and eighteenth century. Conveniently enough, that
analysis should benefit from rich data sources.
47
Domestic markets were created twofold: by lending from merchants in commercial cities (e.g. Henry
IV, 1598-1610, borrowed from Paris, and marginalized increasingly-expensive Italian lenders), or by coercive
annexation of capital-intensive cities (Stasavage, 2011).
48
To the contrary, states that kept relying on external loans to finance wars found it much harder to
capitalize the effect of war on state-making: e.g. Spain under Phillip II (Drelichman and Voth, 2011).
41
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49
0
10
20
30
Figure 1: Chile at War. An example of war financing as a function of access to the
international financial market. The light-gray area identifies years at war without access
to international lending markets. The dark-gray area identifies years at war with access to
international lending markets. Debt and tax data drawn from Braun et al. (2000).
1820
1840
1860
1880
1900
1920
year
Tax Revenue as % of GDP
s
50
Public Foreign Debt as % of GDP
Figure 2: Partial Correlations of Personal Income Tax and Endogenous WarFinancing. Estimates are drawn from column 4 in Table 1.
Denmark
Italy
Austria
Chile
-4
Venezuela
Colombia
Ecuador
Finland
Indonesia
Thailand
United Kingdom
Spain
New Zealand
United
States of America
Bolivia
Malaysia
Hungary
Honduras
Iceland
Zambia
Kenya
Tunisia
Myanmar
France
Ireland
Uruguay
Guatemala
Philippines
Paraguay
Portugal
Canada
ChinaAustralia
Nicaragua
Netherlands
Greece
Norway
India
Morocco
Poland
Dominican
Republic
El
Salvador
Panama
Japan
Sweden Sri Lanka
Costa Rica
Ivory Coast
Germany
Egypt
South Korea
Switzerland
Nigeria
Romania
Peru
Argentina
Mexico
Russia
-5
0
5
Residuals from Regressing
# Years at War while in Default on Controls
Italy
Austria
Hungary
Germany
Brazil
Chile Thailand
IndonesiaFinland
Venezuela
Colombia
Bolivia
NewSpain
Zealand
United
States
of America
Ecuador
Honduras
Kenya
Malaysia
Peru
Paraguay
China
Iceland
Tunisia
Myanmar
Argentina
Zambia
Uruguay
Ireland
Guatemala
Portugal
Canada
Philippines Australia
Greece
Nicaragua
Netherlands
Norway
Dominican
Republic
Mexico
India
Poland
Morocco
El
Salvador
Panama
Japan
Sweden
Costa
Rica
Sri Lanka
Egypt
Ivory Coast
Switzerland
South Korea
Nigeria
Romania
-4
Brazil
South Africa
Turkey
Belgium
Zimbabwe
4
Residuals from Regressing
PIT as % of GDP on Controls
-2
0
2
Residuals from Regressing
PIT as % of GDP on Controls
-2
0
2
4
Denmark
South Africa
Turkey
Belgium
Zimbabwe
10
0
20
Residuals from Regressing
# Years at War while Having Access to Credit on Controls
coef = .028, (robust) se = .027, t = 1.03
(a) War in Default
(b) War having Credit Access
51
France
Russia
-20
coef = .167, (robust) se = .075, t = 2.21
United Kingdom
40
0
Milions of current Pounds Sterling
50
100
150
200
Figure 3: British Capital Exports from 1865 to 1914. In light-gray: Banking panics
of 1865, 1873, and 1890. In dark-gray: the stock crisis of 1907. Source: Stone (1999).
1865
1870
1875
1880
1885
1890
52
1895
1900
1905
1910
1915
10
Figure 4: Partial Correlations of Personal Income Tax and Exogenous WarFinancing. Estimates are drawn from column 4 in Table 5.
Georgia
-10
-5
0
5
France
10
5
Denmark
Israel
Austria
Zimbabwe
Italy
Finland
Turkey
South Africa
Hungary
Lesotho
Indonesia
Bhutan
Thailand
Yemen
Swaziland
Spain
Iceland
Nepal
New
United
States of America
Chile
PeruZealand
Iran
Portugal
Poland
Uruguay
Zambia
Colombia
Venezuela
Ireland
Mongolia
Lithuania
Brazil Netherlands
Bolivia
Paraguay
Macedonia
Tunisia
Ecuador
Kenya
Norway
Philippines
Nicaragua
Germany
Honduras
Japan
Morocco
Cambodia
Argentina
Guatemala
Switzerland
Greece
Sweden
Estonia
Slovenia
South
Korea
Slovakia
Mexico
Ivory
CoastChina
Armenia
Cyprus
France
Ethiopia
Chad
Congo
Azerbaijan
Latvia
CzechRica
Republic
Croatia
Romania
Canada
Australia
Costa
Mali
Senegal
Malaysia
Democratic
Republic of the Congo
Panama
Myanmar
El Salvador
India
Burundi
Pakistan
Guinea
Bulgaria
Tajikistan
Rwanda
Kazakhstan
Nigeria
Dominican
Republic
Sri Lanka
Lebanon
Bosnia
and Herzegovina
Georgia
Belarus
Moldova
Vietnam
Ukraine
Madagascar
Egypt
Albania Bangladesh
Russia
-5
4
2
0
-2
Russia
Lesotho
Hungary
Indonesia
Spain
Bhutan
Thailand
Brazil
Iceland
Swaziland
Peru
New
Zealand
Iran
Yemen
States of Paraguay
America
Argentina
ChileUnited
Nepal
Netherlands
Zambia
Uruguay
Poland
Portugal
Ireland
Mongolia
Bolivia
Venezuela
Lithuania
Morocco
Colombia
Macedonia
Germany
Norway
Kenya
Tunisia China
Nicaragua
Honduras
Ecuador
Philippines
Greece
Cambodia
Mexico
Guatemala
Estonia
Slovenia
Japan
Cyprus
Switzerland
Slovakia
Sweden
South
Korea
Ethiopia
Ivory
Coast
Chad
Armenia
Senegal
Mali
Canada
Romania
Congo
Latvia
Australia
Azerbaijan
Republic
Malaysia
Croatia
Costa
Rica
Myanmar
ElCzech
Salvador
Democratic
Republic
of the Congo
Bulgaria
Burundi
India
Vietnam
Panama
Guinea
Pakistan
Rwanda
Tajikistan
Kazakhstan
Nigeria
Dominican
Republic
Sri
Lanka
Lebanon
Bosnia and Herzegovina
Belarus
Moldova
Ukraine
Madagascar
Egypt
Albania
Bangladesh
Belgium
Namibia
0
Residuals from Regressing
PIT as % of GDP on Controls
6
Denmark
Israel
Italy
Zimbabwe
Austria
Turkey
South Africa
Finland
-4
Residuals from Regressing
PIT as % of GDP on Controls
Belgium
Namibia
15
-5
Residuals from Regressing
# Years at War while Credit Stops on Controls
0
5
10
15
Residuals from Regressing
# Years at War while Credit Flows on Controls
coef = .27344029, (robust) se = .05554225, t = 4.92
coef = -.19952368, (robust) se = .05680223, t = -3.51
(a) War while Credit Stops
(b) War while Credit Flows
53
# Years at War between 1816 and 1913
while Credit Stopped
-2
0
2
# Years at War between 1816 and 1913
while Credit Flew
-4
Marginal Effect on Non-Trade Tax Revenue
Figure 5: Intermediate Effect: Marginal effects of the Number of Years at War with and
without access to External Credit between 1820 and 1913 on Non-Trade Tax Revenue from
1945 to 1995 (decennial averages).
1950
1960
1970
1980
1990
54
1950
1960
1970
1980
1990
55
Yes
Yes
63
0.756
Colonial Origins FE
Region FE
Observations
R-squared
Yes
Yes
63
0.759
3.390**
(1.359)
0.150**
(0.071)
0.034
(0.025)
3.493**
(1.597)
-0.945
(0.659)
0.021**
(0.008)
-0.057
(0.060)
(2)
Yes
Yes
63
0.760
3.496**
(1.418)
0.167**
(0.074)
0.032
(0.026)
3.420**
(1.656)
-0.919
(0.673)
0.022**
(0.008)
-0.060
(0.060)
-0.009
(0.013)
(3)
Yes
Yes
63
0.761
3.458**
(1.448)
0.168**
(0.076)
0.028
(0.027)
3.378*
(1.723)
-0.928
(0.687)
0.022**
(0.008)
-0.059
(0.060)
-0.008
(0.014)
0.317
(1.549)
(4)
Yes
Yes
63
0.766
3.568**
(1.390)
0.052
(0.058)
0.186**
(0.078)
-0.005
(0.052)
3.384**
(1.593)
-1.016
(0.714)
0.020**
(0.009)
-0.061
(0.065)
-0.017
(0.014)
(5)
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
3.269**
(1.361)
3.389**
(1.578)
-0.822
(0.640)
0.021**
(0.008)
-0.051
(0.062)
0.037
(0.024)
Intercept
WWI Participant
# years at Civil War 1816-1913
Ethnic fractionalization
War Casualties 1816-1913
War Location
Great power
# Years in Default
Desert
Sea Access
Oil Producer
Population Density in 1820
# Years at War with Access to Credit
# Years at War while in Default
# Years at War 1816-1913
(1)
Yes
Yes
54
0.723
3.633**
(1.696)
0.906
(1.868)
0.137*
(0.072)
0.020
(0.043)
3.172*
(1.839)
-0.632
(0.913)
0.022*
(0.011)
-0.078
(0.085)
-0.008
(0.014)
(6)
Yes
Yes
62
0.759
3.338*
(1.822)
0.325
(1.546)
0.159*
(0.081)
0.031
(0.026)
3.473**
(1.707)
-0.944
(0.749)
0.022**
(0.008)
-0.053
(0.074)
-0.008
(0.013)
(7)
Yes
Yes
63
0.766
3.478**
(1.416)
0.066
(0.051)
0.157**
(0.075)
0.020
(0.029)
3.324*
(1.658)
-1.013
(0.682)
0.024***
(0.009)
-0.064
(0.062)
-0.015
(0.014)
(8)
Yes
Yes
63
0.764
0.752
(0.906)
2.813*
(1.604)
0.171**
(0.077)
0.027
(0.025)
2.983
(1.831)
-0.874
(0.667)
0.024***
(0.008)
-0.050
(0.055)
-0.011
(0.014)
(9)
Table 1: Personal Income Tax Today (as % of GDP) as a Function of War and Endogenous Credit Access in the
Long-Nineteenth Century
Table 2: External capital Stock by Country in the Long-Nineteenth Century
United Kingdom
France
Germany
Netherlands
United States
Canada
All
UK/all
World GDP
1825
0.5
0.1
0.3
0.0
0.9
0.56
-
1855
0.7
0.2
0.0
s
0.9
0.78
-
1870
4.9
2.5
0.3
0.0
7.7
0.64
111
1890
12.1
5.2
4.8
1.1.
0.5
0.1
23.8
0.51
128
1914
19.5
8.6
6.7
1.2
2.5
0.2
38.7
0.50
221
Values represent gross foreign assets in current USD
billion. Source: Table 2.1 in Obstfeld and Taylor
(2004).
56
Table 3: Banking Crises and Stock Market Crashes in London, 1816-1913
Banking Crises
Stock Market Crises
1825
1865
1837
1866
1838
1867
1839
1910
1840
1911
1847
1912
1848
1913
1849
1850
1857
1866
1873
1890
Source: Reinhart and Rogoff (2009). 1873 banking
panic added. Results robust to its exclusion (see
Appendix Table A-10).
57
Table 4: Frequency and Duration of War as a function of Credit Access. Refer to
Appendix Figure A-6 for a Visual Illustration.
Endogenous access to credit
Countries in sample: 63
Wars in sample: 111
Total war-year-country: 399, out of which
spa - 348 were fought while having Access to Credit, with avg. duration of 2.34 years (1.88)a
spa - 51 were fought while in Default, with avg. duration of 2.05 years (1.52)
Exogenous access to credit
Countries in sample: 107
Wars in sample: 114
Total war-year-country: 466, out of which
spa - 222 were fought while Credit Flows, with avg. duration of 2.00 years (1.60)
spa - 244 were fought while Sudden-Stop, with avg. duration of 1.90 years (1.24)
Exogenous access to credit including Secessionist Wars
Countries in sample: 107
Wars in sample: 147
Total war-year-country: 624, out of which
spa - 314 were fought while Credit Flows, with avg. duration of 2.02 years (1.54)
spa - 349 were fought while Sudden-Stop, with avg. duration of 2.02 years (1.37)
The difference in war-year-country between the endogenous and exogenous credit cases results
from countries not included in Reinhart and Rogoff (2009) fighting wars against states included
in that sample. a Standard deviation of duration in parenthesis.
58
59
Yes
Yes
106
0.551
Colonial Origins FE
Region FE
Observations
R-squared
Yes
Yes
106
0.587
1.331
(0.829)
0.273***
(0.056)
-0.200***
(0.057)
1.238
(1.318)
0.127
(0.468)
0.028***
(0.007)
0.013
(0.045)
(2)
Yes
Yes
106
0.588
1.295
(0.843)
0.275***
(0.056)
-0.200***
(0.057)
1.247
(1.332)
0.108
(0.474)
0.028***
(0.007)
0.014
(0.046)
0.008
(0.010)
(3)
Yes
Yes
106
0.610
1.279
(0.811)
2.712**
(1.166)
0.251***
(0.055)
-0.252***
(0.069)
0.788
(1.396)
0.130
(0.464)
0.029***
(0.007)
0.015
(0.045)
(4)
Yes
Yes
106
0.594
1.345
(0.819)
0.054
(0.040)
0.221***
(0.074)
-0.191***
(0.059)
1.159
(1.311)
0.043
(0.472)
0.027***
(0.008)
0.008
(0.046)
(5)
Yes
Yes
87
0.554
1.347
(1.131)
-0.481
(0.880)
0.303***
(0.081)
-0.206***
(0.068)
2.314
(1.485)
0.156
(0.679)
0.028***
(0.010)
0.028
(0.067)
(6)
(7)
Yes
Yes
105
0.585
1.591
(1.263)
-0.306
(1.254)
0.269***
(0.055)
-0.198***
(0.058)
1.220
(1.572)
0.218
(0.498)
0.026***
(0.008)
0.006
(0.048)
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.
1.250
(0.862)
1.623
(1.365)
0.098
(0.474)
0.027***
(0.007)
0.004
(0.045)
0.052*
(0.028)
Intercept
WWI participant
Average War duration 1816-1913
# Years at Civil War 1816-1913
Ethnic fractionalization
War Casualties 1816-1913
War Location
Great Power
# Years in Default 1816-1913
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
# Years at War while Credit Flows
# Years at War while Credit Stops
# Years at War 1816-1913
(1)
Yes
Yes
106
0.592
1.281
(0.826)
0.066*
(0.037)
0.241***
(0.055)
-0.186***
(0.059)
1.221
(1.318)
0.022
(0.477)
0.029***
(0.007)
0.012
(0.045)
(8)
Yes
Yes
106
0.609
0.739
(0.810)
0.008
(0.124)
0.261***
(0.053)
-0.214***
(0.056)
0.799
(1.246)
-0.006
(0.459)
0.029***
(0.007)
0.011
(0.045)
(9)
Yes
Yes
106
0.587
1.261**
(0.533)
1.327
(0.843)
0.273***
(0.060)
-0.201***
(0.052)
1.243
(1.336)
0.125
(0.466)
0.028***
(0.007)
0.013
(0.045)
(10)
Table 5: Personal Income Tax Today (as % of GDP) as a Function of War and Exogenous Credit Access in the
Long-Nineteenth Century
60
1.999*
(1.173)
Yes
Yes
49
0.825
Colonial Origins FE
Region FE
Observations
R-squared
0.150***
(0.052)
-0.146**
(0.060)
4.399*
(2.419)
0.311
(0.589)
0.027**
(0.011)
0.044
(0.064)
Yes
Yes
49
0.831
0.037
(0.031)
2.272*
(1.121)
0.114
(0.067)
-0.140**
(0.065)
4.542*
(2.484)
0.328
(0.597)
0.025**
(0.012)
0.080
(0.048)
(3)
Yes
Yes
106
0.584
1.158
(0.851)
0.181***
(0.050)
-0.069
(0.074)
1.458
(1.349)
0.015
(0.471)
0.027***
(0.007)
0.013
(0.045)
Yes
Yes
106
0.597
1.111
(0.842)
0.161***
(0.054)
-0.091
(0.085)
1.128
(1.437)
0.029
(0.472)
0.028***
(0.007)
0.013
(0.045)
1.964
(1.257)
Yes
Yes
106
0.584
0.001
(0.026)
1.159
(0.858)
0.180***
(0.055)
-0.069
(0.075)
1.456
(1.359)
0.017
(0.475)
0.027***
(0.007)
0.013
(0.045)
Secessionist War Included
(4)
(5)
(6)
PIT 2000s
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
Yes
49
0.831
1.842*
(1.062)
0.161***
(0.057)
-0.191**
(0.085)
3.859
(2.845)
0.302
(0.620)
0.029**
(0.011)
0.060
(0.057)
1.432
(1.552)
Sovereign States Only
(1)
(2)
(3)
Intercept
War Location
Great Power
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
# Years at War while Credit Flows
# Years at War while Credit Stops
Sample →
Dependent Variable →
(3)
Yes
Yes
79
0.669
-0.116
(0.136)
0.036**
(0.015)
-0.018
(0.019)
0.217
(0.239)
-0.106
(0.097)
0.002
(0.001)
0.000
(0.005)
(7)
Yes
Yes
79
0.672
-0.128
(0.140)
0.035**
(0.014)
-0.021
(0.019)
0.188
(0.255)
-0.104
(0.097)
0.002
(0.001)
0.001
(0.005)
0.136
(0.237)
All States
(8)
Yes
Yes
79
0.669
0.001
(0.005)
-0.118
(0.138)
0.034**
(0.014)
-0.017
(0.019)
0.216
(0.242)
-0.103
(0.100)
0.002
(0.001)
0.001
(0.005)
(9)
Tax Staff 2000s
Table 6: Models of Personal Income Tax Today (as % of GDP) using Conservative Definition of Statehood,
Liberal Definition of Warfare, and Alternative Outcome Variable
Table 7: Personal Income Tax Today (as % of GDP) as a Function of War and Exogenous Credit Access in the Long-Nineteenth Century, with Special Attention
to Omitted Variable Bias and Selection into War
Initial Capacity
(1)
(2)
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
Great Power
Modern Census
0.233***
(0.062)
-0.247***
(0.068)
0.770
(1.425)
0.146
(0.463)
0.027***
(0.007)
0.012
(0.046)
2.814**
(1.195)
0.801
(1.239)
State Antiquity
Intercept
Colonial Origins FE
Region FE
Observations
R-squared
0.239***
(0.079)
-0.241***
(0.091)
0.696
(0.863)
0.156
(0.517)
0.030***
(0.007)
-0.016
(0.045)
2.672**
(1.127)
Ongoing War
(3)
(4)
0.135*
(0.070)
-0.085
(0.078)
1.019
(1.483)
0.225
(0.470)
0.026***
(0.008)
0.004
(0.046)
3.268**
(1.260)
1.349
(1.270)
0.166**
(0.070)
-0.077
(0.078)
0.897
(1.408)
0.178
(0.459)
0.030***
(0.007)
-0.024
(0.033)
3.101**
(1.189)
1.339
(0.832)
0.001
(0.001)
0.564
(0.922)
1.461*
(0.864)
0.002
(0.001)
0.423
(1.035)
Yes
Yes
106
0.613
Yes
Yes
103
0.646
Yes
Yes
106
0.586
Yes
Yes
103
0.617
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
61
Table 8: Personal Income Tax Today (as % of GDP) as a Function of War and
Exogenous Credit Access in the Long-Nineteenth Century, with War Data drawn
from COW’s Inter-State Military Conflict Database
All countries
(1)
(2)
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
Great Power
Modern Census by 1820
0.379***
(0.099)
0.075
(0.173)
1.206
(1.467)
-0.065
(0.445)
0.028***
(0.007)
-0.020
(0.031)
0.602
(1.374)
0.879
(1.206)
Colonial Origins FE
Region FE
Observations
R-squared
Non-Initiators
(3)
(4)
0.453***
(0.152)
0.162
(0.207)
1.260
(1.480)
-0.163
(0.449)
0.027***
(0.007)
-0.021
(0.031)
1.374
(1.275)
1.186
(1.090)
0.748
(0.814)
0.396***
(0.107)
0.062
(0.175)
1.242
(1.489)
-0.059
(0.452)
0.028***
(0.007)
-0.018
(0.032)
0.699
(1.404)
0.922
(1.220)
-0.048
(0.098)
0.711
(0.829)
0.810
(0.815)
0.473**
(0.183)
0.121
(0.251)
1.284
(1.506)
-0.153
(0.455)
0.028***
(0.007)
-0.019
(0.032)
1.481
(1.274)
1.220
(1.136)
-0.035
(0.116)
0.797
(0.825)
Yes
Yes
102
0.651
Yes
Yes
102
0.652
Yes
Yes
102
0.647
Yes
Yes
102
0.647
Net Victory
Intercept
3
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
62
63
No
No
Yes
Yes
102
-
-2.609***
(0.687)
0.115*
(0.059)
-0.053
(0.051)
1.147*
(0.675)
0.689*
(0.361)
0.004
(0.005)
0.025
(0.039)
No
No
Yes
Yes
99
-
-2.542***
(0.802)
0.116**
(0.059)
-0.052
(0.051)
1.175*
(0.712)
0.680*
(0.394)
0.004
(0.005)
0.025
(0.039)
-0.000
(0.001)
No
No
Yes
Yes
103
0.565
170.901***
(14.579)
-3.024***
(0.827)
2.233**
(0.970)
-8.516
(11.975)
-16.277**
(7.817)
-0.325***
(0.120)
-0.972
(0.687)
-0.020
(0.022)
Modern Census
By 1913
Delay
(2)
(3)
Probit
OLS
No
No
Yes
Yes
103
0.567
-2.915***
(0.795)
2.493**
(0.983)
-6.237
(11.800)
-16.132**
(7.827)
-0.330***
(0.122)
-0.981
(0.694)
-0.021
(0.022)
-13.195
(12.933)
171.295***
(14.720)
Delay
(4)
OLS
cc
Yes
Yes
Yes
Yes
62
0.620
5.807***
(1.230)
0.095*
(0.049)
-0.096
(0.070)
0.549
(1.798)
-0.137
(0.517)
-0.001
(0.006)
0.080
(0.052)
Yes
Yes
Yes
Yes
61
0.620
5.916***
(1.868)
0.094*
(0.050)
-0.093
(0.075)
0.594
(1.803)
-0.104
(0.592)
-0.001
(0.007)
0.078
(0.053)
-0.000
(0.001)
Yes
Yes
Yes
Yes
61
0.633
0.092*
(0.049)
-0.118
(0.071)
1.076
(1.847)
-0.080
(0.599)
0.001
(0.007)
0.076
(0.052)
-0.000
(0.001)
1.743
(1.175)
5.974***
(1.841)
Log(Rail Lines)
By 1913
By 1913
By 1913
(5)
(6)
(7)
OLS
OLS
OLS
cc
Yes
No
Yes
Yes
76
0.858
-0.192
(6.082)
0.855*
(0.491)
-0.162
(0.537)
-0.255
(6.521)
-7.755
(5.263)
0.036
(0.056)
0.160
(0.340)
Yes
No
Yes
Yes
76
0.863
6.101
(8.119)
0.935*
(0.508)
-0.135
(0.577)
2.017
(6.893)
-6.316
(5.242)
0.029
(0.053)
0.275
(0.351)
-0.019
(0.016)
Yes
No
Yes
Yes
76
0.865
0.921*
(0.513)
-0.337
(0.645)
0.408
(6.825)
-6.189
(5.329)
0.037
(0.052)
0.286
(0.360)
-0.018
(0.016)
8.335
(8.422)
4.622
(8.427)
Primary Schooling
By 1913 By 1913
By 1913
(8)
(9)
(10)
OLS
OLS
OLS
The Great Power indicator in columns 1 and 2 cannot be estimated due to perfect collinearity. Initial Value of Primary Schooling in 1820 is logged to account for strong
non-linearities. † Initial Value of Railroads correspond to 1850. ‡ To fully account for the topographical characteristics of rail line building, models include additional controls:
land area, tropical weather, and terrain ruggedness. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Initial Level of Dep Variable†
Topographical controls‡
Colonial Origins FE
Region FE
Observations
R-squared
Constant
Great Power
State Antiquity
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
# Years at War while Credit Flows
# Years at War while Credit Stops
By 1913
(1)
Probit
Table 9: Short-term Effects of War Making on State Capacity as a Function of War and Exogenous Credit
Access in the Long-Nineteenth Century
**NOT FOR PUBLICATION**
Supplementary Online Appendices
The Legacy of War on Fiscal Capacity
These appendices contain materials, results and robustness checks that supplement the
main text.
Appendices
A. Data Details
B. Cross-Sectional Distribution of Warfare and Acces to Credit
C. Sensibility Tests involving the Main Dependent Variable
D. Influence of Outliers
E. Influence of Fixed Effects
F. Sensibility Tests addressing Measurement and Sample Decisions
G. Sensibility Test involving Crises Timing and Length of Sudden-Stops
H. Alternative Sources of War Financing
I. Value-Added Tax as Outcome Variable
J. Military Alliances, British Colonies, and British Wars
K. Instrumenting for War-Making
L. Including Bad Controls
M. Intermediate Effects: The Effect of War and Credit Access Over Time
N. Further Evidence of the Exogeneity of Sudden-stops
O. Further Evidence of the Lending Frenzy
P. Appendix-Specific References
i
A. Data Details
Personal Income Tax. The IMF Global Financial Statistics (GFS) PIT data that I use
refers to cash-accounts (as recommended by the IMF). For the few cases that these data is
not available, I use non-cash values, which correlate at .97 with cash-accounts. To minimize
influence of abnormal values, I compute average PIT values as a percentage of GDP for
the years 1995- 2005. This decade maximizes the sample size compared to earlier and later
decades.
Personal Income Tax data is scarce, even for the IMF. Scarcity is indeed indicative of
the sophistication of this tax. Missing values are filled in with various sources. Crucially,
column 1 in Table A-6 shows that data augmentation does not change the point estimates
of interest. That is, models that only use GFS PIT data yield the same results than models
that augment GFS data with additional sources.
Cases not covered by the GFS are filled as follows: for Chile, Nicaragua, Ecuador and
Guatemala, data are drawn from the Inter-American Development Bank Dataset;49 for
Nepal, data are drawn from the Ministry of Finance;50 For Sri Lanka, data are drawn from
the Department of Fiscal Policy;51 for Lebanon, data are available from the Ministry of Finance for the 2000-5 period;52 For Zambia, data are for 2005 only, and are drawn from CMI
Report;53 For Guinea, Rwanda, Chad, Namibia and Yemen, Kenya, Mali, Nigeria, Philippines, Senegal and Vietnam only 2004 data are drawn from the pilot study of the USAID
Fiscal Reform and Economic Governance Project, 2004-2010. Again, results do not change
as a result of the data augmentation (refer to column 1 in Table A-6).
49
IDB (Inter-American Development Bank) and CIAT (Inter- American Center of Tax Administrations).
2012. Latin America and the Caribbean Fiscal Burden Database, 1990-2010. Database n. IDB-DB-101.
Washington, DC.
50
Nepal Rastra Bank, Research Department Government Finance Division. 2014. A Handbook of Government Finance Statistics.
51
Available at http://www.treasury.gov.lk/fiscal-operations/fiscal-data.html. Accessed, March 31, 2015.
52
Available at ttp://www.finance.gov.lb/EN-US/FINANCE/REPORTSPUBLICATIONS/DOCUMENTSANDREPORTS
Accessed on March 31, 2015.
53
Odd-Helge Fjeldstad and Kari K. Heggstad. 2011. The tax systems in Mozambique, Tanzania and
Zambia: capacity and constraints Bergen: Chr. Michelsen Institute (CMI Report R 2011:3) 124 p.
ii
Tax staff. The size of the tax administration is drawn from the USAID Fiscal Reform
and Economic Governance Project, 2004-2010. To maximize the sample size, I combine the
values for 2004, 2007-10. This variable is a strong predictor of total tax revenue to GDP, as
Figure A-1 shows.
0
10
Tax Revenue as % of GDP
20
30
40
50
Figure A-1: Total Tax Revenue vs. Size of the Tax Administration
0
1
2
Tax Staff per Thousand Capita
3
Default. Default data is drawn from Reinhart and Rogoff (2009). Default episodes account
for outright defaults (partial or complete) and rescheduling, with which the debtor forces its
creditors to accept longer repayment schedules, interest rate concessions, or both. Defaults
come to an end when defaulters offer creditor(s) an acceptable payoff: e.g. refinancing
the loan, some form of state monopoly revenue, even land. External debt in Reinhart and
Rogoff (2009, p.11) is indistinctively issued by official (public) and private entities. I work
with 63 out the 68 countries in their sample, all for which complete data is available. The
five countries excluded due to tax-data limitations are: Algeria, Angola, Central African
Republic, Ghana and Taiwan. Strict default periods, as those used in the analysis, are a
conservative estimate of the length of the spans in which countries do not have access to
credit. Defaulters live by their reputation: even today, when third party institutions veil
for investors’ interests, it takes between 2.9 and 4.7 years for an average defaulter to regain
access markets following the end of a default, declining to 2.9 years only recently (Gelos et
iii
al. 2011). And half of defaulting countries do not fully regain market access within seven
years of the end of the default (Richmond and Dias 2009).
Census. A modern census involves periodicity, universality, and individual enumeration
by means of house-to-house visitation. The date of the first modern census is coded from
three sources: Goyer and Draaijer (1992b,c,a).
Military Conflict. The main source is Wimmer and Min (2009). Most wars can be easily
matched to current states thanks to the geographical location provided in Wimmer and Min
(2009). For non-obvious matches, I make the following assumptions:
1. Splits. This refers to wars attributed by Wimmer and Min (2009) to former political
entities that eventually split. Countries affected are: Austria-Hungary, Czechoslovakia,
and Korea. To facilitate matching, entries have been duplicated and attributed evenly
across current political units: Austria and Hungary, Czech Republic and Slovakia, and
North Korea and South Korea, respectively. Example: suppose that Austria-Hungary
fought 10 wars within 1816-1913, then I assign 10 wars to Austria and 10 wars to
Hungary. The assumption is that both entities inherit evenly the fiscal burden and
consequences of warfare.
2. Secessionist wars. Wimmer and Min’s (2009) data attribute war participation to the
colonial power only. I extend this code by attributing war participation to the territory
that seeks independence too.
3. Subnational to National Units. Table A-2 lists political units in Wimmer and Min
(2009) that were eventually incorporated to a larger unit (or merged into one). These
are non-state and sub-state actors that can be easily matched to current nation-states.
4. Tentative matching. Table A-3 lists political units in Wimmer and Min (2009) that
cannot be matched to current units without making too many assumptions. These
iv
cases are not considered in the main analysis. However, results do not change if they
are (refer to column 5 in Table A-9).
5. Unmatched Units. These are former polities that overlap with more than one state
today. They are not considered in the analysis: Bornu (modern Chad, Niger and
Cameroon), Khanate of Kokand (Kazakhstan and Uzbekistan), Mandingo Empire
(eleven states in West Africa), Oyo (various states in West Africa), Zuku, Tukolor
Empire (Mali, Nigeria and Guinea), Bambara Empire (Guinea and Bali), and Principality of Jammu (China, Tibet, Pakistan).
Civil wars are excluded from the analysis because their contribution to state building is
yet to be established. Porter (1994) argues that civil war was positive for state-building in
early-modern Europe. Similarly, Balcells and Kalyvas (2014) suggest that irregular warfare
might serve to state building. However, others find opposite evidence in Africa (Herbst,
2000) and Latin America (Cardenas, 2010; Centeno, 2002). Civil wars are only considered
as a control.
A note on COW vs Wimmer-Min: To enter the COW inter-state war dataset prior
to 1920, territorial units must possess diplomatic relations with both Britain and France. A
considerable large number of states that went to war during the nineteenth century —mainly
outside Europe—had not yet established sufficient relations with both of these states (Griffiths and Butcher, 2013). As a result, they are excluded from the COW inter-state datase.
The COW offers three additional datasets: extra-state, non-state, and civil wars. Wars
against or between colonies and other non-internationally recognized states entities enter
these three auxiliary COW datasets. But, unlike Wimmer and Min (2009), those wars are
not mapped onto current state borders, preventing a clear match between past warfare and
current nation-states.
Table A-1 reports the summary statistics of these and the remaining variables. Right
v
after it, Table A-2 lists quasi-direct matches of sub-national to national units, and Table A-3
lists all tentative matches.
vi
vii
Personal Income Tax as % of GDP 1995-2005
Tax Staff per 1000 capita 2004-10
# Years at War with Access to Credit
# Years at War while in Default
# Years at War 1816-1913 (full sample)
# Years at War while Credit Flows
# Years at War while Credit Stops
# Years at War while Credit Flows– COW
# Years at War while Credit Stops– COW
Modern Census by 1820
Modern Census by 1914
First Modern Census Date
Oil Producer
# Years at Civil War 1816-1913
War Location 1816-1913
Population Density in 1820
Great Power
War Casualties 1816-1913
ln(Rail Lines)
Ethnic Fractionalization
Sea Access
Desert
State Antiquity
Region
British Colony
Iberian Colony
Other Colony
WWI Participant
Variable
2.999
0.702
7.603
1.095
4.346
2.075
2.271
0.757
0.913
0.093
0.607
1888.963
0.692
1.794
0.028
0.205
0.065
0.111
7.804
0.37
36.57
1.862
445.054
2.636
0.187
0.187
0.327
0.374
Mean
3.258
0.557
12.679
2.763
9.851
4.718
5.485
1.612
2.054
0.292
0.491
57.413
0.464
4.48
9.743
0.289
0.248
0.275
2.125
0.273
35.594
5.016
210.295
1.152
0.392
0.392
0.471
0.486
Std. Dev.
0
0.03
0
0
0
0
0
0
0
0
0
1666
0
0
-31
0
0
0
0
0.004
0
0
25
1
0
0
0
0
Min.
15.058
2.398
62
11
60
27
36
9
8
1
1
1984
1
26
58
1.635
1
1.512
12.908
0.9
100
26.132
860.975
6
1
1
1
1
Max.
107
80
63
63
107
107
107
103
103
107
107
107
107
107
107
107
107
88
63
106
107
107
104
107
107
107
107
107
N
Various Sources, see above
Various Sources, see above
Wimmer and Min (2009) and Reinhart and Rogoff (2009)
Wimmer and Min (2009) and Reinhart and Rogoff (2009)
Wimmer and Min (2009)
Wimmer and Min (2009) and Reinhart and Rogoff (2009)
Wimmer and Min (2009) and Reinhart and Rogoff (2009)
Sarkees and Wayman (2010) and Reinhart and Rogoff (2009)
Sarkees and Wayman (2010) and Reinhart and Rogoff (2009)
coded by author from Goyer and Draaijer (1992b,c,a)
coded by author from Goyer and Draaijer (1992b,c,a)
coded by author from Goyer and Draaijer (1992b,c,a)
calculated from Wimmer and Min (2009)
calculated from Wimmer and Min (2009)
calculated from Wimmer and Min (2009)
World Mapper www.worldmapper.org
Flandreau and Flores (2012)
Dincecco and Prado (2012)
Comin and Hobijn (2010)
Wimmer and Min (2009)
Nunn and Puga (2012)
Nunn and Puga (2012)
Bockstette, Chanda and Putterman (2002)
coded by author
coded by author
coded by author
coded by author
coded by author
Source
Table A-1: Summary Statistics and Data Sources
viii
Original unit →
Hanover
Hesse Electoral
Hesse Grand Ducal
Baden
Bavaria
Wuerttemburg
Saxony
Mecklenburg Schwerin
Modena
Papal States
Tuscany
Two Sicilies
Senegal (Kingdoms of Jolof and Futa Toro)
Sri Lanka (Kingdom of Kandy)
Sudan (Kingdom of Sinnar)
Sudan (Mahdiyya state)
Matched to
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Germany
Italy
Italy
Italy
Italy
Senegal
Sri Lanka
Sudan
Sudan
space
Original unit →
Syria (Arab Kingdom of Syria)
Algeria (Barbary states)
Afghanistan (Durrani Kingdom)
Benin (Kingdom of Dahomey)
Benin Empire
Argentina (United Provinces of Rio de la Plata)
Georgia (Kingdom of Kartli-Kakheti)
Madagascar (Merina Kingdom)
Mali (Tukulor Empire)
Yugoslavia (Kingdom of Serbia)
South African Republic
Orange Free State
Tibet
Transvaal
Xhosa
Republic of Vietnam
Matched to
Syria
Algeria
Afghanistan
Benin
Benin
Argentina
Georgia
Madagascar
Mali
Serbia
South Africa
South Africa
China
South Africa
South Africa
Vietnam
Table A-2: Quasi-Direct Matches between Political Units listed in Wimmer-Min 2009 and Modern Nation-States
Table A-3: Tentative Matches. These are political units listed in Wimmer-Min that
cannot be directly matched to current states. They are not used in the main text analysis,
but results are robust to their inclusion, as shown in column 4 in Appendix Table A-9.
Original unit
Aceh Sultanate
Ashanti Kingdom
Buganda
Emirates of Kano
Khanate of Kiva
Kingdom of Bharatpur
Kingdom of Lahore
Balinese Kingdom of Lombok
Maratha Empire
Sanusi Empire
Sokoto
Zulu
Zulu Kingdom
Ndebele Kingdom
Kingdom of Sindh
Kingdom of Waalo
ix
Matched to
Indonesia
Ghana
Uganda
Nigeria
Uzbekistan
India
Pakistan
Indonesia
India
Lybia
Nigeria
South Africa
South Africa
Zimbabwe
Pakistan
Senegal
B. Cross-Sectional Distribution of Warfare and Acces to Credit
First, Table A-4 reports the distribution of war and endogenous access to credit. This
first sample is upper bounded by default data coverage in Reinhart and Rogoff (2009).
Second, Table A-5 reports the breakdown of war participation and exogenous access to
credit. This sample is larger and is upper bounded by data availability of the income tax
dependent variable. Third, the actual location of warfare is plotted in Figure A-2. Darker
areas indicate higher frequency of war in territory x. Fourth, Figure A-3 plots the distribution
of war participants regardless of war location. Again, darker areas indicate higher rates of
participation (regardless of the location of conflict). These two figures show that most wars
were fought outside Europe but involved at least one European power.
Table A-4: Endogenous access to Credit and War Participation: This table lists the
# Years at War having Access to International Markets (W&A), and # Years at War while
being in Default (W&D). N = 63
Argentina
Australia
Austria
Belgium
Bolivia
Brazil
Canada
Chile
China
Colombia
Costa Rica
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Finland
France
Germany
Greece
Guatemala
Honduras
W&A
7
0
3
1
10
15
0
3
27
1
0
3
0
1
8
4
0
60
8
2
0
0
W&D
9
0
0
0
1
0
0
5
0
0
0
0
0
0
1
0
0
0
0
1
3
2
W&A
3
0
0
0
0
13
0
5
0
0
1
5
6
7
0
1
0
0
0
7
2
0
Hungary
Iceland
India
Indonesia
Ireland
Italy
Ivory Coast
Japan
Kenya
Malaysia
Mexico
Morocco
Myanmar
Netherlands
New Zealand
Nicaragua
Nigeria
Norway
Panama
Paraguay
Peru
Philippines
x
W&D
0
0
0
0
0
0
0
0
0
0
8
0
0
0
0
2
0
0
0
0
11
0
Poland
Portugal
Romania
Russia
South Africa
South Korea
Spain
Sri Lanka
Sweden
Switzerland
Thailand
Tunisia
Turkey
United Kingdom
United States of America
Uruguay
Venezuela
Zambia
Zimbabwe
W&A
0
0
1
39
4
0
6
2
0
0
10
2
17
58
5
1
0
0
0
W&D
0
0
0
2
0
0
4
0
0
0
0
0
2
0
0
0
0
0
0
xi
Albania
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Belarus
Belgium
Bhutan
Bolivia
Brazil
Bulgaria
Burundi
Cambodia
Canada
Chad
Chile
China
Colombia
Congo
Costa Rica
Croatia
Cyprus
Czech Republic
Democratic Republic of the Congo
Denmark
Dominican Republic
Ecuador
Egypt
El Salvador
Estonia
Ethiopia
Finland
France
Georgia
W&F
0
3
0
0
1
0
0
0
0
0
6
3
1
0
4
0
0
5
13
1
0
0
0
0
0
0
1
0
1
7
3
0
4
0
24
9
W&S
0
13
0
0
2
0
0
0
1
1
5
12
2
0
0
0
0
3
14
0
0
0
0
0
0
0
2
0
0
2
1
0
4
0
36
1
Germany
Greece
Guatemala
Guinea
Honduras
Hungary
Iceland
India
Indonesia
Iran
Ireland
Israel
Italy
Ivory Coast
Japan
Kazakhstan
Kenya
Latvia
Lebanon
Lesotho
Lithuania
Macedonia
Madagascar
Malaysia
Mali
Mexico
Moldova
Mongolia
Morocco
Myanmar
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Nigeria
W&F
3
1
2
0
2
1
0
0
0
4
0
0
5
0
4
0
0
0
0
0
0
0
4
0
0
4
0
0
1
4
0
0
1
0
2
0
W&S
5
2
1
0
0
2
0
0
0
5
0
0
8
0
1
0
0
0
0
0
0
0
1
0
1
5
0
0
4
2
0
0
6
0
1
0
Norway
Pakistan
Panama
Paraguay
Peru
Philippines
Poland
Portugal
Romania
Russia
Rwanda
Senegal
Slovakia
Slovenia
South Africa
South Korea
Spain
Sri Lanka
Swaziland
Sweden
Switzerland
Tajikistan
Thailand
Tunisia
Turkey
Ukraine
United Kingdom
United States of America
Uruguay
Venezuela
Vietnam
Yemen
Zambia
Zimbabwe
W&F
0
0
0
1
6
0
0
0
0
27
0
0
0
0
4
0
3
2
0
0
0
0
5
2
9
0
26
3
0
0
10
0
0
0
W&S
0
0
0
6
7
0
0
0
1
14
0
2
0
0
0
0
7
0
0
0
0
0
5
0
10
0
32
2
1
0
13
0
0
0
Table A-5: Exogenous access to Credit and War Participation: This table lists the # Years at War while Credit Flows
(W&F), and # Years at War while Credit Stops (W&S). N = 107
Figure A-2: The Geography of Military Conflict in the Long-Nineteenth Century.
Colors indicate the total number of years at war. Source: Wimmer and Min (2009).
(20,47]
(10,20]
(6,10]
(3,6]
[1,3]
no war
xii
Figure A-3: Frequency of War Participation in the Long-Nineteenth Century.
Colors indicate the total number of years at war. Source: Wimmer and Min (2009).
(19,62]
(10,19]
(7,10]
(6,7]
(4,6]
(2,4]
[1,2]
no war
xiii
C. Sensibility Tests involving the Main Dependent Variable
Appendix A explains that the IMF GFS Data on Personal Income Tax is augmented with
various sources. Column 1 shows that when the sample is limited to countries for which there
is GFS data, results hold despite that the N decreases by over 20%.
Columns 2 and 3 replace average Personal Income Tax as % of GDP between 1995
and 2005 for its equivalent in the period 1990-2000, and 2000-2010 (both of which are also
augmented when possible). These columns show that results are the same as in the main
text, but the sample size marginally decreases. All in all, results do not hinge on small
variations in the dependent variable.
Table A-6: Personal Income Tax Today (as % of GDP) as a Function of War
and Endogenous Credit Access in the Long-Nineteenth Century, with Special
Attention to Measurement Decisions regarding the Dependent Variable
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
Great Power
Modern Census by 1820
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
(1)
Non-Augmented
Dep Variable,
1995-2005
(2)
Dep Variable
dated as of
1990-2000
(3)
Dep Variable
dated as of
2000-10
0.264***
(0.076)
-0.251***
(0.071)
0.737
(1.627)
0.012
(0.535)
0.029***
(0.008)
-0.060
(0.059)
2.657**
(1.250)
0.565
(1.345)
1.495
(0.925)
0.184**
(0.081)
-0.181**
(0.075)
2.267
(1.572)
0.180
(0.709)
0.033***
(0.010)
-0.043
(0.058)
2.090
(1.329)
0.265
(1.451)
0.465
(1.168)
0.196***
(0.059)
-0.220***
(0.071)
1.031
(1.474)
0.156
(0.420)
0.026***
(0.007)
0.021
(0.039)
3.016**
(1.152)
1.325
(1.347)
1.436*
(0.734)
Yes
Yes
87
0.658
Yes
Yes
83
0.602
Yes
Yes
104
0.634
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xiv
D. Influence of Outliers
Based on the partial correlation Figures 2 and 4, I identify potential outliers in each
relationship: Peru, Argentina, Mexico and Prussia, in the case of endogenous access to
credit, and Russia, Georgia, and France, in the case of exogenous access to credit. Table A-7
reports models without these outliers, and based on the new estimates, I rerun the partial
correlation plots, shown in Figures A-4 and A-5. Both sets of results suggest that outliers
downward bias the main coefficient of interest β̂1 , especially in the case of endogenous access
to credit. By contrast, β̂2 turns positive in the endogenous model (although it is barely
significant and the magnitude of this coefficient is one order of magnitude smaller than β̂1 )
and again not significant in the exogenous access to credit sample, consistent with other
results in that section. Results are similar if outliers are identified by standard measures of
influence: e.g. Cook’s distance.
Altogether, this exercise confirms the positive effect of war fought in the absence of
external credit on long-term fiscal capacity, as well as the null (at best mixed) effect of war
fought while having access to external credit.
xv
Table A-7: Dropping Influential Outliers. PIT as % of GDP Today as a Function of War
and Exogenous/Exogenous Access to Credit in the Long Nineteenth Century once Outliers
are excluded.
Dependent Variable: PIT as % of GDP
Endogenous Access
Exogenous Access
To Credita
To Creditb
(1)
(2)
# Years at War while in Default
0.517**
(0.230)
0.041*
(0.023)
# Years at War with Access to Credit
# Years at War while Credit Stops
0.248***
(0.090)
-0.109
(0.150)
# Years at War while Credit Flows
# Years in Default
Population Density in 1820
Oil Producer
Sea Access
Desert
Great Power
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
a
-0.015
(0.014)
2.583
(1.761)
-0.861
(0.670)
0.020**
(0.008)
-0.090
(0.061)
1.047
(1.438)
3.402**
(1.427)
0.712
(1.391)
-0.053
(0.450)
0.029***
(0.007)
0.010
(0.046)
3.148***
(1.183)
1.037
(0.829)
Yes
Yes
59
0.788
Yes
Yes
103
0.610
Models with Endogenous Credit Access exclude Argentina, Mexico, Peru and Russia.
Models of Exogenous Credit Access exclude France, Georgia, and Russia. Robust
standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
b
xvi
Figure A-4: Partial Correlations of Personal Income Tax and Endogenous War-Financing
once Outliers are dropped: Argentina, Mexico, Peru, Russia. These estimates are drawn
from column 1 in Appendix Table A-7, and should be compared to those in Figure 2.
4
Residuals from Regressing
PIT as % of GDP on Controls
-2
0
2
Zimbabwe
Chile
Italy
Austria
Indonesia
Brazil ThailandFinland
Spain
Guatemala
Turkey
Brazil
Thailand
Venezuela
Finland
Colombia
Indonesia
Ecuador
New States
Zealand
United
of America
Tunisia
Kenya
Chile
Ireland
Bolivia
Iceland
Honduras
Paraguay
Portugal
Malaysia
Uruguay
Zambia
Myanmar
Netherlands
Philippines Australia
Spain
Canada
Greece
India
El Salvador
Guatemala
Nicaragua
Japan
Dominican
Republic
Poland
Norway
Morocco
PanamaCosta
Rica
Sri Lanka
Sweden
South Korea
Egypt
Switzerland
Ivory Coast
Nigeria
Romania
Italy
Austria
Hungary
Germany
United Kingdom
China
France
-4
New Zealand
Venezuela
Colombia
United States of America
Bolivia
Ireland
Honduras
Ecuador
Zambia
Malaysia
Tunisia Kenya
Uruguay
United
Kingdom
Philippines
Iceland
Paraguay
Hungary
Portugal
Myanmar
Nicaragua
Canada
Netherlands
Australia
India
China
Japan
Greece
Panama
Morocco
Dominican
Republic
El Salvador
Poland
Norway
France
Costa RicaSwedenSri Lanka
South Korea
Ivory Coast
Switzerland
Egypt
Germany
Nigeria
Romania
Denmark
Belgium
South Africa
Turkey
-4
Residuals from Regressing
PIT as % of GDP on Controls
-2
0
2
4
Denmark Belgium
South Africa
Zimbabwe
-1
0
1
2
Residuals from Regressing
# Years at War while in Default on Controls
3
-20
coef = .516, (robust) se = .230, t = 2.25
0
20
Residuals from Regressing
# Years at War while Having Access to Credit on Controls
40
coef = .041, (robust) se = .023, t = 1.75
(a) War in Default
(b) War while having Credit Access
10
0
Lesotho
Hungary
Indonesia
Bhutan
Yemen
Spain
Swaziland
Thailand
Iceland
Nepal
New
Zealand
Iran
United
States
of America Paraguay
Uruguay
Netherlands
Peru
Zambia
Mongolia
Ireland
ChileVenezuela
Portugal
Poland
Colombia
Morocco
Lithuania
Tunisia
Kenya
Macedonia
Philippines
Bolivia
Norway
Germany
Honduras
Nicaragua
South
Korea
JapanEcuador
Guatemala
Cyprus
Greece
Chad
Ivory
Coast
Slovenia
Switzerland
Estonia
Mexico
Cambodia
Slovakia
Mali
Sweden
Senegal
Congo
Canada
Armenia
Ethiopia
Australia
Malaysia
Costa
Rica
India
China
Romania
Czech
Republic
Burundi
Latvia
Azerbaijan
Democratic
Republic
of
the
Congo
Panama
Croatia
Pakistan Republic
El Salvador
Rwanda
Guinea
Myanmar
Dominican
Bulgaria
Lebanon
Nigeria
Kazakhstan
Tajikistan
Sri Lanka
Vietnam
Bosnia and Herzegovina
Belarus
Moldova
Ukraine
Madagascar
Bangladesh
Egypt
Albania
-5
0
Brazil
Argentina
5
NamibiaBelgium
5
Israel Denmark
Austria
Zimbabwe
Italy Finland
Brazil
Argentina
South Africa
Turkey
Lesotho
Hungary
Indonesia
Bhutan
Yemen
Nepal
Thailand
Swaziland
New
Zealand
Chile
United
States
of America
Colombia
Venezuela
Iceland
Uruguay
Portugal
Iran
Mongolia
Poland
Peru
Ireland
SpainZambia
Lithuania
Ecuador
Bolivia
Tunisia
Philippines
Japan
Macedonia
Honduras
Nicaragua
Paraguay
Kenya
Guatemala
Cambodia
Korea
Norway
Netherlands
Morocco South
Switzerland
Germany
Ivory
Coast
Slovakia
Slovenia
Estonia
Sweden
Greece
Mexico
Cyprus
Congo
Chad
Armenia
Czech
Republic
Costa
Rica Ethiopia
Azerbaijan
Panama
Burundi
Democratic
Republic
of
the
Congo
Canada
Croatia
India
Australia
Latvia
Mali
Romania
El
Salvador
Malaysia
Senegal
Rwanda
Pakistan
Myanmar
Guinea
Dominican
Republic
China
Tajikistan
Kazakhstan
Nigeria
Bulgaria
Lebanon
Sri Lanka
Bosnia
and Herzegovina
Belarus
Moldova
Ukraine
Madagascar
Vietnam
Bangladesh
Egypt
Albania
-5
South Africa
Israel
Denmark
AustriaZimbabweItaly
Finland
Turkey
0
Residuals from Regressing
PIT as % of GDP on Controls
5
Belgium
Namibia
-5
Residuals from Regressing
PIT as % of GDP on Controls
10
Figure A-5: Partial Correlations of Personal Income Tax and Exogenous War-Financing
once Outliers are dropped: Russia, Georgia, and Mexico. These estimates are drawn from
column 2 in Appendix Table A-7, and should be compared to those in Figure 4.
10
-4
Residuals from Regressing
# Years at War while Credit Stops on Controls
-2
0
2
Residuals from Regressing
# Years at War while Credit Flows on Controls
coef = .27888029, (robust) se = .09852436, t = 2.83
coef = -.10292007, (robust) se = .15588756, t = -.66
(a) War while Credit Stops
(b) War while Credit Flows
xvii
4
E. Influence of Fixed Effects
Region and colonial origins fixed effects (6 and 4 categories, respectively) are intended to
minimize unobserved heterogeneity across countries. However, if covariates are highly correlated within region/colonial origins, adding fixed effects might induce high multicollinearity
and outliers. Based on the simplest specification of the exogenous access to credit model, I
stepwise drop region fixed effects and colonial origins fixed effects.
Table A-8: Personal Income Tax Today (as % of GDP) as a Function of War
and Exogenous Credit Access in the Long-Nineteenth Century, with Special
Attention to the Influence of Fixed Effects
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
(1)
(2)
(3)
0.227***
(0.056)
-0.181***
(0.060)
1.335
(1.386)
0.214
(0.508)
0.031***
(0.007)
0.012
(0.046)
2.290***
(0.781)
0.283***
(0.068)
-0.265***
(0.077)
0.511
(1.545)
0.851
(0.521)
0.020**
(0.009)
0.018
(0.055)
1.101*
(0.615)
0.157*
(0.092)
-0.185**
(0.082)
1.466
(1.539)
0.784
(0.615)
0.020**
(0.010)
0.056
(0.057)
1.310**
(0.605)
Yes
No
106
0.533
No
Yes
106
0.298
No
No
106
0.118
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xviii
F. Sensibility Tests addressing Measurement and Sample Decisions
Table A-9 investigates the extent to which results hinge on particular cases, regions,
assumption about error correlation, matching decisions, or territorial configuration of the
state.
The bellicist hypothesis has been tested in Europe with positive result. More importantly,
European countries are overrepresented in inter-state warfare. Column 1 in Table A-9 drops
all European countries from the sample to assess their influence.
Great Powers (Austria, France, Germany, Hungary, Italy, Russia and the United Kingdom) follow distinct trajectories from the rest of the world: they developed domestic financial
markets early enough and also had high military capacity. For these reasons too, they are
more prone to participate in war. Column 2 drops them from the sample.
Arguably, wars happening in country x might affect the likelihood of war-making among
its neighbors. To account for contemporaneous error, Column 3 fits clustered standard errors
to address residual correlation at the regional level. I consider 6 regions in the world. Thus,
cluster standard errors create smaller standard errors than the Huber-White standard error
counterparts, which are used in the main text.
Appendix Table A-3 lists past polities that cannot be matched with current state-borders
without making various assumptions. The analyses in the main text do not consider these
polities, but column 4 in Table A-9 does in order to minimize any potential attrition bias.
Results hold.
A federal structure might condition central government tax yields as well as correlate with
past warfare if a non-unitary state results from ethnical or ideological civil war. Column 5
includes this variable as a control. Data for federal structure is drawn from Treisman (2014).
xix
Table A-9: Personal Income Tax Today (as % of GDP) as a Function of War
and Endogenous Credit Access in the Long-Nineteenth Century, with Special
Attention to Measurement and Sample Decisions
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
(1)
Europe
Dropped
(2)
Great Powers
Dropped
(3)
Cluster
Std. Error
(4)
Tentative
Matches
(5)
Federal
Structure
0.190**
(0.075)
-0.140**
(0.054)
-0.952
(0.742)
-0.005
(0.422)
0.016**
(0.006)
0.011
(0.049)
0.260***
(0.082)
-0.146
(0.116)
0.800
(1.433)
-0.053
(0.441)
0.026***
(0.007)
0.009
(0.046)
0.108
(1.820)
1.146
(1.306)
0.233***
(0.041)
-0.247***
(0.058)
0.770
(1.850)
0.146
(0.387)
0.027**
(0.008)
0.012
(0.027)
2.814***
(0.252)
0.801
(0.524)
0.233***
(0.062)
-0.247***
(0.068)
0.770
(1.425)
0.146
(0.463)
0.027***
(0.007)
0.012
(0.046)
2.814**
(1.195)
0.801
(1.239)
1.380*
(0.763)
1.105
(0.838)
1.339
(0.669)
1.339
(0.832)
0.237***
(0.063)
-0.252***
(0.066)
0.802
(1.423)
0.183
(0.475)
0.026***
(0.007)
0.012
(0.046)
2.898**
(1.283)
0.761
(1.232)
-0.315
(0.793)
1.371
(0.862)
Yes
Yes
88
0.560
Yes
Yes
100
0.597
Yes
Yes
106
0.613
Yes
Yes
106
0.613
Yes
Yes
106
0.614
Great Power
Modern Census by 1820
Federal Structure
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
In column 1, Great Power is dropped because all of them were European. Robust standard errors
in parentheses. *** p<0.01, ** p<0.05, * p<0.1
xx
G. Sensibility Test involving Crises Timing and Length of SuddenStops
The 1910 crisis is a stock-market crash, not a banking crash. Based on Figure 3, the
stock-market crash might not cause comparable capital dry shocks. Accordingly, column 1
in Table A-10 treats the 1910 stock-market crisis as a non-crisis, and investigates whether
this has any impact.
Reinhart and Rogoff (2009) do not list the 1873 banking crisis for Great Britain, despite it
being a major crisis in the nineteenth century (Kindleberger and Aliber, 2005). Technically,
the 1873 crisis originated in Austria and Germany. But, it was only a matter of months
that the crisis reached London, causing a sudden-stop of credit (Bordo 1986), as Figure 3
reflects. Based on the relevance of this crisis, I include it in the main analysis. For the
sake of robustness, column 2 in Table A-10 excludes the 1873 banking crisis as a cause of
sudden-stop.
Columns 3 and 4 allow for longer spells of sudden-stops. Specifically, columns 3 and 4
replace the four-year rule of credit stop based on Catao (2006) for five and six years spells,
respectively. The effect of fighting war during these longer periods is still positive, although,
consistently with the actual length of sudden-stops, the effect becomes weaker.
xxi
Table A-10: Personal Income Tax Today (as % of GDP) as a Function of War
and Endogenous Credit Access in the Long-Nineteenth Century, with Special
Attention to Timing Issues
# Years at War while Credit Stops
# Years at War while Credit Flows
Population density in 1820
Oil Producer
Sea Access
Desert Territory
Great Power
Modern Census by 1820
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
(1)
1910 Crisis
Dropped
(2)
1873 Crisis
Dropped
(3)
5-year Sudden-Stop
Windows
(4)
6-year Sudden-Stop
Windows
0.189**
(0.077)
-0.175*
(0.102)
0.853
(1.419)
0.178
(0.464)
0.028***
(0.008)
0.007
(0.046)
2.702**
(1.305)
0.782
(1.255)
1.339
(0.844)
0.243***
(0.073)
-0.213***
(0.081)
1.319
(1.450)
0.149
(0.469)
0.028***
(0.007)
0.016
(0.047)
2.076
(1.370)
1.296
(1.236)
1.368
(0.837)
0.170***
(0.056)
-0.248***
(0.080)
0.780
(1.437)
0.168
(0.462)
0.026***
(0.007)
0.014
(0.046)
2.774**
(1.169)
0.809
(1.273)
1.330
(0.840)
0.159***
(0.054)
-0.305***
(0.087)
0.765
(1.423)
0.185
(0.461)
0.026***
(0.007)
0.012
(0.046)
2.681**
(1.116)
0.801
(1.277)
1.333
(0.834)
Yes
Yes
106
0.596
Yes
Yes
106
0.606
Yes
Yes
106
0.607
Yes
Yes
106
0.612
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xxii
H. Alternative Sources of War Financing
There are three additional ways to finance war: domestic borrowing, printing money, and
financial repression.
The former strategy should not be of major concern: Domestic borrowing requires a
developed financial market, something that, in the period under consideration, was only
guaranteed in a few European countries (Reinhart and Rogoff, 2009, ch.7). The pool of
domestic investors in the periphery tended to be small, and loans to government represented
a large share of their portfolio. This implied expensive credit relative to other options
overseas (della Paolera and Taylor, 2013; Flandreau and Flores, 2012).54 Not surprisingly,
countries in the periphery resorted to international markets for financing.
Columns 1-3 in Table A-11 address the possibility of fighting wars while having access to
either domestic or external credit and the lack thereof. The first row shows the coefficient
of not having access to the domestic or international markets (i.e. domestic and external
default), while the fourth row shows the effect of having access to either to the domestic or
international markets. In the former case, I expect the incentives to invest in fiscal institutions to be maximum. Consistent with this expectation, the magnitude of the coefficients
grows with respect to those reported in Table 1. Column 2 adds a Great Power indicator
to control for differences in domestic credit markets, and column 3 controls for the War
Location, as it could influence the capacity to mobilize resources domestically. The point
estimate of the two coefficients of interest, β̂1 and β̂2 , remain fairly stable.
A second means to financing war is expanding the money supply (also known as printing money). Except as an extreme measure of last resort, printing money has came to
occupy a “subordinate position” in pre-1913 war finance (Sprague, 1917). The reason is
that expanding the money supply has inflationary consequences. A sudden expansion of the
money supply gives the government a temporary relief with which to purchase additional
54
An example might be illustrative: domestic lenders in Mexico would apply rates in the range of 300%500% (Centeno, 2002).
xxiii
xxiv
Yes
Yes
63
0.761
3.464**
(1.403)
0.032
(0.026)
3.421**
(1.655)
-0.917
(0.671)
0.022**
(0.008)
-0.060
(0.060)
-0.009
(0.013)
0.171**
(0.073)
Yes
Yes
63
0.761
3.423**
(1.434)
0.028
(0.027)
3.377*
(1.723)
-0.926
(0.685)
0.022**
(0.008)
-0.059
(0.060)
-0.008
(0.014)
0.332
(1.551)
0.172**
(0.075)
b
Yes
Yes
63
0.767
(3)
Yes
Yes
63
0.760
3.497**
(1.433)
0.171*
(0.092)
0.154***
(0.055)
0.032
(0.026)
3.422**
(1.673)
-0.919
(0.680)
0.022**
(0.008)
-0.061
(0.060)
-0.009
(0.013)
Yes
Yes
63
0.761
3.459**
(1.464)
0.172*
(0.094)
0.157***
(0.056)
0.028
(0.028)
3.380*
(1.741)
-0.927
(0.695)
0.022**
(0.008)
-0.059
(0.061)
-0.008
(0.014)
0.314
(1.567)
Yes
Yes
63
0.767
0.053
(0.060)
3.574**
(1.403)
0.203**
(0.097)
0.142**
(0.057)
-0.006
(0.053)
3.389**
(1.610)
-1.016
(0.722)
0.020**
(0.010)
-0.063
(0.066)
-0.017
(0.014)
Accounting for
Money Printing
(4)
(5)
(6)
Years in default refer to external default.
0.051
(0.058)
3.517**
(1.373)
-0.005
(0.052)
3.383**
(1.591)
-1.007
(0.709)
0.020**
(0.009)
-0.062
(0.065)
-0.017
(0.014)
0.187**
(0.076)
In columns 1-3, access to credit refers to either domestic or international markets, or both.
Robust Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
a
Colonial Origins FE
Region FE
Observations
R-squared
Intercept
War Location 1816-1913
Great Power
# Years in defaultb
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
# Years at war with access to credita
# Years at war while in external default and money printing
# Years at war while in external default but no money printing
# Years at war while in external and domestic default
Accounting for
Domestic Default
(1)
(2)
(3)
Table A-11: PIT as % of GDP Today as a Function of War and Endogenous Credit Access in the Long Nineteenth
Century, with Special Attention to Domestic Default Episodes and Money Printing
weapons or pay bills, but this gain is rapidly dissipated by the costs of inflation (Rockoff
1998, Schumpeter 1938). Nevertheless, it is worth checking what is the effect of printing
money on long-term fiscal capacity.
In the absence of direct data of instances of money printing, I use episodes of inflationary crises, as coded by Reinhart and Rogoff (2009), to explore how inflation weakens the
incentives to invest in fiscal capacity while being at war despite not having access to external
credit. In other words, this test assumes that inflationary crises are related to episodes of
money supply expansions. Inflation does not dissipate soon. To account for these lags, I add
four leads to the onset of an inflationary crisis. Suppose, for instance, that the onset of an
inflation crisis dates as of 1900 in country x. Then, I assume that inflation stays around until
1904, five years in total. Based on that, I estimate the effect of being at war and in default
in the presence and absence of an inflationary crisis. Again, I expect inflationary crisis (i.e.
the proxy of money printing) to dissipate the incentives to invest in fiscal capacity despite
being in default.
The results in columns 4-6 in Table A-11 reinforce and qualify earlier findings. First,
they confirm that waging war while being in default is related to higher fiscal capacity in the
long-run regardless of money printing: both coefficients are positive. However, based on the
coefficients’ magnitude, if inflation is kept under control (i.e. the ruler does not print money),
fiscal capacity might be even higher in the long-run. This result implies that incumbents
that are not tempted to print money while being at war and in default are those investing
more decisively in the fiscal capacity of the state, holding everything else constant.
A third way to finance war is financial repression. Calomiris and Haber (2014) and
Reinhart (2012) show that, if anything, financial repression is a substitute of fiscal capacity
building.55 I lack systematic data about instances of financial repression, and cannot test this
proposition here. However, if fiscal repression is negatively correlated to efforts of building
tax capacity, the omission of financial repression, if any, biases downwards the main coefficient
55
See Menaldo (2016) for recent evidence about it.
xxv
of interest, β1 . That is, if rulers prioritize fiscal repression when they lack access to external
finance, we should not expect a positive coefficient for the # Years at War while in Default,
precisely because fiscal repression is implemented as to avoid fiscal capacity building. The
same applies to alternative ways to finance war, such as office selling (Hoffman, 1994).
xxvi
I. Value-Added Tax as Outcome Variable
Value-Added Tax (VAT) is arguably easier to implement than the income tax (Bird and
Gendron, 2007), and it may not capture accumulated investment in fiscal capacity as precisely
as income tax ratios do. Still, Table A-12 fits models of current VAT (as % of GDP) as a
function of war and credit access in the long-nineteenth century. VAT data is drawn from IMF
Government Financial Statistics. The sample size is limited by data availability. Column 1
regresses average VAT revenue between 1995 and 2005 on the benchmark regressors. We can
extend the dependent variable by replacing missing values for those reported in USAID Fiscal
Reform and Economic Governance Project, 2004-10, as I did with PIT data (Appendix F
shows that this decision is inconsequential). Results with the extended dependent variable
are reported in column 2. Columns 3 and 4 add two controls for initial state capacity, one
at a time. Results hold.
xxvii
Table A-12: Value-Added Tax (VAT) as % of GDP Today as a Function of Years at
War and Exogenous Access to External Credit in the Long Nineteenth Century
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Dessert Territory
Great Power
(1)
(2)
(3)
(4)
0.229*
(0.124)
0.065
(0.104)
0.326
(1.098)
-1.165
(0.761)
0.005
(0.013)
0.097*
(0.051)
-3.416**
(1.355)
0.097
(0.059)
0.047
(0.079)
-0.260
(0.784)
-1.018
(0.684)
0.008
(0.008)
0.029
(0.054)
-0.420
(1.375)
0.126**
(0.060)
0.040
(0.081)
-0.237
(0.778)
-1.042
(0.697)
0.011
(0.009)
0.034
(0.054)
-0.574
(1.417)
-1.223
(0.896)
0.097*
(0.057)
0.037
(0.077)
-0.371
(0.839)
-1.188
(0.733)
0.008
(0.008)
0.022
(0.058)
-0.309
(1.364)
Modern Census by 1820
State Antiquity
Intercept
Extended Dep Var
Region FE
Colonial Origins FE
Observations
R-squared
1.285
(1.182)
2.207**
(0.861)
2.112**
(0.845)
0.000
(0.002)
2.202**
(0.958)
No
Yes
Yes
65
0.439
Yes
Yes
Yes
105
0.388
Yes
Yes
Yes
105
0.394
Yes
Yes
Yes
102
0.381
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xxviii
J. Military Alliances, British Colonies, and British Wars
Appendix Table A-13 examines the effect of (1) military alliances in the international
system, (2) the effect of being a British colony, and (3) British active participation at war.
Do results change when we account for these potential confounders?
1. Accounting for Military Alliances. Military alliances might change the incentives
to go to war and also facilitate access to external credit. To account for this, I control for
military alliance with the four credit capitals in the long-nineteenth century: the British, the
French, the German, and the USA. Despite having uneven weight in global finances (refer to
Table 2), one might argue that any of these four countries had both the capacity to finance
third countries and coordinate military interventions with them. To code military alliances,
I rely on Gibler (2009). This dataset offers dyads of military alliances between independent
countries since 1816. Some of these alliances were short-lived while others were enduring. To
account for this heterogeneity, I compute the share of years between 1816-1913 under which
a given country had any form of military alliance (defense, neutrality, non-aggression, and
entente) with any of these credit capitals. For instance, Portugal had at least one military
alliance with Britain for the whole period. Accordingly, it takes the maximum value: 100%.
Other countries (e.g. Belgium) stroke no military alliance with Britain during the longnineteenth century. Accordingly, the value for Belgium is zero. Since the total number of
years in sample are 98, these shares may be interpreted as the total number of years under
which a given country had a military alliance with any of the four financial capitals.
2. Excluding British Colonies. It is proved that British colonies had access to external
credit in more favorable conditions than other colonies (Accominotti, Flandreau and Rezzik,
2011). Since Britain is the credit capital and the military superpower of the long-nineteenth
century, one might suspect that the decision to go to war for British colonies is different
from other countries’. The British colonial origins fixed effect might not address this source
xxix
of heterogeneity well enough. To address this issue, columns 3 and 4 re-run Expression 1
excluding all British colonies and using the exogenous variation of credit access.
3. Excluding Wars Fought by Britain. Having already addressed strategic considerations with respect to British colonies, we might wonder whether wars involving British direct
participation are comparable to other wars. To address this issue, columns 5 and 6 report
models excluding all wars in which the British explicitly participated.
xxx
xxxi
Yes
Yes
106
0.635
Colonial Origins FE
Region FE
Observations
R-squared
Yes
Yes
103
0.668
0.001
(0.001)
0.468
(0.975)
0.298***
(0.061)
-0.290***
(0.087)
0.648
(1.415)
0.025
(0.454)
0.029***
(0.007)
-0.010
(0.034)
-0.000
(0.008)
0.138**
(0.068)
-0.007
(0.021)
0.811
(0.625)
0.806
(1.155)
Yes
Yes
86
0.557
0.471
(0.820)
2.503**
(1.246)
0.699
(1.302)
0.207***
(0.051)
-0.236***
(0.049)
2.395
(1.823)
0.228
(0.464)
0.025***
(0.008)
0.081
(0.050)
Yes
Yes
83
0.656
0.004***
(0.001)
-1.624*
(0.835)
2.525**
(1.115)
0.197***
(0.037)
-0.238***
(0.042)
1.928
(1.619)
-0.009
(0.419)
0.029***
(0.007)
0.042
(0.027)
British Colonies Excluded
(3)
(4)
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
1.201
(0.835)
0.289***
(0.076)
-0.298***
(0.089)
0.753
(1.462)
0.007
(0.477)
0.025***
(0.007)
0.017
(0.049)
0.001
(0.007)
0.158**
(0.068)
-0.011
(0.020)
0.630
(0.878)
0.823
(1.209)
0.895
(1.558)
Intercept
State Antiquity
Modern Census by 1820
Great Power
Alliance with USA
Alliance with Germany
Alliance with France
Alliance with Britain
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
# Years at War while Credit Flows
# Years at War while Credit Stops
All Countries Included
(1)
(2)
Yes
Yes
106
0.625
1.307
(0.840)
2.789**
(1.161)
0.752
(1.213)
0.332***
(0.080)
-0.320***
(0.055)
0.741
(1.408)
0.084
(0.454)
0.027***
(0.007)
0.015
(0.046)
Yes
Yes
103
0.658
0.001
(0.001)
0.644
(0.986)
2.665**
(1.115)
0.339***
(0.072)
-0.313***
(0.053)
0.699
(1.374)
0.118
(0.433)
0.030***
(0.007)
-0.013
(0.032)
British Wars Excluded
(5)
(6)
Table A-13: PIT as % of GDP Today as a Function of War and Exogenous Access to Credit in the Long Nineteenth
Century, with Special Attention to Military Alliances, Favorable Access to Credit by British Colonies, and Wars
Involving British Participation.
K. Instrumenting for War-Making
This section addresses the endogeneity of war in a reduced-form framework. In analyzing
the effect of war in Europe, Gennaioli and Voth (2015) instrument war frequency of country
i based on war participation by adjacent countries against third countries. The logic behind
this instrument is that contextual circumstances that lead neighboring countries to war might
increase the likelihood of country i going to war against a third country. The exclusion
restriction is that there is no effect of war in neighboring countries on fiscal capacity that is
not the result of the risk of war (ibid.).
Here I follow a similar strategy. However, instead of running a pure IV model, I stick
to a reduced-form set up, in which I replace inter-state war fought by country i while credit
stops (flows) for inter-state wars fought by immediately adjacent neighbors while credit stops
(flows). For reference, wars of i against adjacent countries are excluded to maximize exogeneity.
I fit the reduced-form instead of a fully-fledged IV model with two endogenous variables
(# of years at war while credit dries, and # of years at war while credit flows), as that requires
too many untestable assumptions. A ratio of the two endogenous variables would simplify
everything, except that there are zeros in both variables, leading to indeterminate form (0/0)
or infinite values (n/0) in the key explanatory variable. In light of both limitations, I opt
for the reduced-form model, in which I replace the two potentially endogenous variables for
their two instruments. Accordingly, Expression 1 becomes
P ITi,1995−2005 =
α + β1 (#years at war by i’s-adjacent neighbors between 1816-1913 | external lending stops)
+β2 (#years at war by i’s-adjacent neighbors between 1816-1913 | external lending flows)
+X i δ + γ + ρ + i
(2)
where controls and fixed effect batteries remain the same, and common episodes of suddensstops are used to exogenize access to external credit.
In Gennaioli and Voth (2015) all countries have adjacent neighbors. However, some
xxxii
islands in my sample have no adjacent neighbor whatsoever: Australia, Iceland, Madagascar,
Philippines, and New Zealand. Column 1 shows the result for every country except these
cases. The exclusion restriction requires the instrument not to be directly related with the
outcome or unobservables affecting the outcome. The latter assumption can be addressed
to the best extent by controlling for further covariates. Accordingly, column 2 includes all
controls for which I have full data. Columns 3 and 4 rerun the same tests including islands.
In every model, the coefficients of interest, β̂1 and β̂2 , hold the expected sign: that is, the
instrumented-version of waging war while having access to external credit does not increase
(nor decrease) fiscal capacity today, whereas the instrumented-version of waging war while
not having access to external loans is associated with higher fiscal capacity today. The main
difference with Table 7 results is the size of the effects: these are now attenuated as a result
of the imperfect match between war-making by country i and that of its adjacent countries.
xxxiii
xxxiv
No
Yes
Yes
101
0.445
Islands Included
Region FE
Colonial Origins FE
Observations
R-squared
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
1.841**
(0.877)
0.112*
(0.062)
-0.069
(0.044)
1.201
(1.129)
0.232
(0.526)
0.030***
(0.009)
-0.015
(0.047)
Intercept
# Years at Civil War 1816-1913
Ethnic Fractionalization
State Antiquity
Modern Census by 1820
Great Power
War Location
Desert Territory
Sea Access
Oil Producer
Population Density in 1820
years at war by i’s-adjacent neighbors between 1816-1913 while external lending flows
years at war by i’s-adjacent neighbors between 1816-1913 while external lending stops
(1)
No
Yes
Yes
98
0.564
0.119*
(0.067)
-0.079*
(0.045)
0.310
(1.375)
-0.142
(0.511)
0.029***
(0.009)
-0.047
(0.033)
0.052
(0.052)
1.446
(1.519)
1.050
(1.478)
0.000
(0.002)
-0.827
(1.226)
0.073
(0.055)
1.530
(1.380)
(2)
Yes
Yes
Yes
106
0.556
1.523*
(0.867)
0.111*
(0.062)
-0.071
(0.044)
1.129
(1.128)
0.194
(0.493)
0.029***
(0.008)
-0.004
(0.045)
(3)
Yes
Yes
Yes
102
0.652
0.112*
(0.067)
-0.074
(0.045)
0.473
(1.379)
-0.081
(0.492)
0.029***
(0.008)
-0.043
(0.034)
0.056
(0.052)
1.435
(1.530)
1.041
(1.496)
0.000
(0.001)
-0.464
(1.181)
0.070
(0.053)
1.075
(1.384)
(4)
Table A-14: Reduced-Form Models. Personal Income Tax as % of GDP Today as a Function of War and Exogenous Access
to Credit in the Long Nineteenth Century, with War Participation of Country i Instrumented by War Participation by Adjacent
Countries
L. Including Bad Controls
Covariates that result from treatment are known as bad controls. Their inclusion in
empirical models bias the estimate of interest, in this case β1 and β2 . This is also known
as post-treatment bias. Here I consider four potential bad controls: democracy, preferences
for redistribution, GDP per capita, and trade openness. Bates and Lien (1985) claim that
democratic institutions result from tax-financed war participation. Scheve and Stasavage
(2010) suggest that preferences for the size of government is endogenous to war participation.
Dincecco and Prado (2012) show that long-term GDP is a function of participation in war in
the past. Queralt (2015) suggest that trade openness follows fiscal capacity building, which
results from war participation. Table A-15 corroborates that the inclusion of bad controls
impact the size of the coefficients of interest. Still, both β̂1 and β̂2 hold the expected sign
and achieve statistical significance within conventional levels.
xxxv
Table A-15: Models of PIT as % of GDP Today as a Function of Exogenous
Credit Access and War-Making in the Long Nineteenth Century including Bad
Controls.
# Years at War while Credit Stops
# Years at War while Credit Flows
Population Density in 1820
Oil Producer
Sea Access
Desert Territory
Modern Census by 1820
Great Power
Polity IV Score 1995-2005
(1)
(2)
(3)
(4)
0.238***
(0.059)
-0.230***
(0.069)
0.419
(1.444)
0.280
(0.494)
0.022***
(0.007)
0.009
(0.049)
0.337
(1.347)
2.133*
(1.215)
0.134***
(0.051)
0.225***
(0.061)
-0.238***
(0.068)
0.676
(1.419)
0.008
(0.505)
0.024***
(0.007)
0.026
(0.051)
0.828
(1.228)
2.685**
(1.190)
0.147***
(0.054)
-0.139*
(0.075)
0.711
(1.090)
-0.386
(0.405)
0.010
(0.007)
-0.013
(0.036)
-0.060
(1.042)
1.331
(1.222)
0.232***
(0.063)
-0.243***
(0.070)
0.830
(1.482)
0.171
(0.471)
0.027***
(0.007)
0.014
(0.047)
0.838
(1.251)
2.809**
(1.208)
Government Size 1995-2005
-4.540*
(2.427)
ln(Per Capita GDP) 1995-2005
1.136***
(0.217)
Trade Openness 1995-2005
Intercept
Region FE
Colonial Origins FE
Observations
R-squared
1.631*
(0.826)
2.091**
(1.023)
-5.095***
(1.402)
0.003
(0.007)
1.133
(1.019)
Yes
Yes
104
0.641
Yes
Yes
104
0.623
Yes
Yes
106
0.732
Yes
Yes
106
0.613
Sources of bad controls: Democracy: Marshall and Jaggers (2000); Per Capita GDP
and Trade Openness: World Bank Indicators; Government Size: Feenstra et al. (2013).
Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1
xxxvi
M. Effect of War and Credit Access Over Time
Table A-16 regresses decennial non-trade tax revenue as a percentage of GDP starting in
1946 and ending in 1995, the first year considered in the main analyses. For each decade, I
compute the average value of the dependent variable. Given the small N, fewer controls are
considered. Colonial Past is a dummy variable that equals 1 for any type colonial origins.
Table A-16: Intermediate Effects: Non-Trade Tax Revenue as a Percentage of
Total Tax Revenue from 1946 to 1995 as a Function of War and Credit Access
between 1816 and 1913
# Years at War in 1815-1916 while Credit Stops
# Years at War in 1815-1916 while Credit Flows
Census by 1820
Oil Producer
Colonial Past
Constant
Observations
R-squared
(1)
1946-1955
(2)
1956-1965
(3)
1966-1975
(4)
1976-1985
(5)
1986-1995
1.082**
(0.462)
-1.503*
(0.773)
11.695***
(3.314)
-8.772*
(4.354)
-7.284*
(3.937)
93.694***
(5.142)
0.327
(0.488)
-0.488
(0.738)
12.126***
(3.053)
-6.692***
(2.120)
-0.139
(3.739)
88.166***
(3.884)
0.745*
(0.407)
-0.926
(0.617)
13.907***
(2.957)
11.149*
(6.194)
-2.195
(3.479)
70.702***
(5.531)
0.920**
(0.379)
-1.004*
(0.568)
11.505***
(2.567)
16.950***
(4.581)
-4.960*
(2.794)
69.473***
(4.102)
0.898**
(0.405)
-0.426
(0.642)
12.022***
(3.710)
11.933***
(3.513)
0.181
(5.570)
70.707***
(5.465)
55
0.264
71
0.380
85
0.208
34
37
0.366
0.242
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xxxvii
Table A-17 reruns the same analysis controlling for contemporaneous democratic score.
I select the first year of each decade to minimize endogeneity. Results, despite the smaller
N, hold. This test shows that the persistence of fiscal capacity holds regardless of regime
type, as discussed in the main text.
Table A-17: Decennial Non-Trade Tax Revenue as a Percentage of Total Tax
Revenue from 1946 to 1995 as a Function of War and Credit Access from 1816
to 1913, and controlling for contemporaneous Polity IV score
# Years at War in 1815-1916 while Credit Stops
# Years at War in 1815-1916 while Credit Flows
Census by 1820
Oil Producer
Colonial Past
Polity IV in 1946
(1)
1946-1955
(2)
1956-1965
(3)
1966-1975
(4)
1976-1985
(5)
1986-1995
1.454***
(0.405)
-2.029***
(0.704)
9.394**
(4.289)
-4.827
(3.787)
-3.172
(3.952)
0.220
(0.350)
0.353
(0.485)
-0.444
(0.763)
9.333**
(3.963)
-3.427
(3.067)
0.829
(3.776)
0.986**
(0.484)
-1.012
(0.684)
9.860***
(3.209)
2.229
(3.757)
0.352
(3.343)
0.810*
(0.442)
-0.588
(0.647)
5.294
(3.178)
13.535***
(4.650)
-1.871
(2.515)
0.666**
(0.332)
-0.492
(0.492)
3.947*
(2.326)
12.155***
(3.024)
-2.894
(2.158)
Polity IV in 1956
0.458
(0.276)
Polity IV in 1966
0.722***
(0.213)
Polity IV in 1976
0.762***
(0.226)
Polity IV in 1986
Constant
Observations
R-squared
88.296***
(5.542)
83.058***
(4.614)
30
36
0.366
0.286
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
xxxviii
75.953***
(3.851)
70.082***
(3.978)
0.623***
(0.169)
75.084***
(2.968)
52
0.338
69
0.443
77
0.489
N. Further Evidence of Exogeneity of Sudden-stops
Table 4 suggests that the frequency and length of war in and outside sudden-stop periods
are virtually identical (or balanced). Figure A-6 shows this differently. In particular, I plot
the Total Number of Wars per Year in the sample, and identify the onset of sudden-stops.
Financial crises that begin within four years of the last sudden-stop (the average duration)
are not plotted.
If sudden-stops are anticipated, we should observe a systematic increase in the frequency
of war just preceding the financial collapse onset. However, this is not the case. Wars take
place before and after sudden-stops, almost evenly, as Table 4 descriptive statistics shows.
0
5
# wars
10
15
Figure A-6: Total Number of Wars per Year vs. Sudden-Stops Onset (vertical
line)
1815
1835
1855
xxxix
1875
1895
1915
O. Further Evidence of the Lending Frenzy
Although a full characterization of the lending frenzy goes beyond the possibilities of
this paper, one can easily elucidate the favorable terms of credit faced by countries in the
periphery twofold: by comparing their bond yields with those paid by the European powers
in the nineteenth century, and with those paid by the latter in pre-modern times, when their
state capacity was still developing.
First, between 1850 and 1914, the largest Latin American countries barely paid a 2% premium relative to the European core (Lindert and Morton, 1989), despite their radically different levels of institutional consolidation. Similarly, colonies borrowed at similar prices than
their metropolises, despite having entirely different economic fundamentals (Accominotti et
al. 2011, Ferguson and Schularick 2006).
Second, European powers paid higher interests in pre-modern times than countries in the
periphery in the nineteenth century. The critical period of European state formation goes
from the fifteenth to the seventeenth century Tilly (1990, p.81). This is a period in which
royal power begins to reassert itself, monopolize violence, and settle the first permanent
systems of tax collection at a national-scale, which matches to a great extent the challenges
faced by the rulers of the newly created states in the periphery. The average nominal yield in
the 15th-17th century in Castile, France and, the UK were 8.75, 7.25, and 7.78, respectively
(calculations based on Stasavage 2011). These are actually conservative estimates: Homer
and Sylla (2005, Table 8) show that bond yields could be significantly higher than these,
reaching rates as high as of 100%. In stark constrast, in Latin American, only Honduras and
Paraguay paid higher yields than these in the nineteenth century (Marichal, 1989, Appendix
A and B). Specifically, by the turn of the century, no Latin American economy paid nominal
interests above 6% (ibid.).
Arguably, the underwriters played a crucial role too: in return for low interest rates,
countries in the periphery would grant financial intermediaries monopoly over sovereign bond
trading, which would sell in secondary markets at higher rates. Country-specific examples
xl
of this exchange can be found in: Flandreau and Flores (2012) for Brazil, Suzuki (1994) for
Japan, and Weller (2015) for Porfirian Mexico.
All in all, despite common challenges, countries in the periphery were treated in a more
generous way by international markets than their European counterparts had been centuries
before. This is due to the very different international context in which states were created.
The European countries were built in times in which the financial markets were underdeveloped and oligopolistic, whereas states in the periphery were created in times of financial
boom and cheap credit caused by excess savings in the European core resulting from the
industrial revolution.
xli
P. Appendix-Specific References
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Money Supply: Some International Evidence, 1870-1933.” In Financial Crises and the World
Banking System, ed. Forrest Capie and Geoffrey E. Wood. London: MacMillan.
Cárdenas, Mauricio. 2010. “State Capacity in Latin America.” Economı́a 10(2):1-45.
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer. 2013. “The Next Generation
of the Penn World Table.” URL: www.ggdc.net/pwt
Ferguson, Nial and Moritz Schularick. 2006. “The Empire Effect: The Determinants of
Country Risk in the First Age of Globalization, 1880-1913.” The Journal of Economic History 66(2):283-312.
Gelos, R. Gaston, Ratna Sahay and Guido Sandleris. 2011. “Sovereign borrowing by developing countries: What determines market access?” Journal of International Economics
83(2):243-254.
Gibler, Douglas M. 2009. International Military Alliances, 1648-2008. Washington D.C.:
CQ.
Marshall, Monty G. and Keith Jaggers. 2000. Polity IV Project: Political Regime Characteristics and Transitions, 1800-2010. Center for International Development and Conflict
Management. University of Maryland.
Menaldo, Victor. 2016.“The Fiscal Roots of Financial Underdevelopment.” American Journal of Political Science 60(2):1540-5907.
Porter, Bruce D. 1994. War and the Rise of the State: The Military Foundations of Modern
Politics. New York: Free Press.
Reinhart, Carmen. 2012. “The Return of Financial Repression” Centre for Economic Policy
Research.
Richmond, Christine and Daniel A Dias. 2009. “Duration of Capital Market Exclusion: An
Empirical Investigation.” Available at SSRN: http://ssrn.com/abstract=1027844.
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xlii
Schumpeter, Elizabeth Boody. 1938.“English Prices and Public Finance, 1660-1822.” Review of Economics and Statistics 20(1):21-37.
Suzuki, Toshio. 1994. Japanese Government Loan Issues in the London Capital Market
1870-1913. London: London.
Treisman, Daniel. 2014. “What Does Cross-National Empirical Research Reveal about the
Causes of Corruption?” in Handbook of Political Corruption, ed. Paul Heywood. New York:
Routledge.
Weller, Leonardo. 2015. “Government versus Bankers: Sovereign Debt Negotiations in
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xliii