Origins of EMP06Kyle Harper, Le Wang, and Kirsten de Beurs
WORKING DRAFT NOT FOR CITATION PLEASE DO NOT CIRCULATE
Abstract
This paper explores the important but neglected role of infectious diseases in shaping
demographic and economic outcomes in historical times. It is widely appreciated in the
development literature that the infectious disease burden has (1) a significant correlation
with patterns of development and (2) a strong geographic structure, especially in the
case of vector-borne diseases such as malaria. Yet, measuring the role of the infectious
disease environment in the past is challenging due to the lack of reliable evidence. In
consequence, an important variable in economic history remains underexplored. To
address this challenge, we have compiled unique historical datasets that allow us to
proxy for the malaria-specific mortality burden in pre-modern Italy and England at
regional, sub-regional, and micro-scales. Further, we have been able to assemble
historical data on female age at first marriage (FAFM) for these same regions. FAFM is
a critically important demographic parameter; late FAFM is the central feature of the
“European Marriage Pattern” (EMP), a fundamental contrast between the demographic
patterns of northwestern European societies and the rest of the world in preindustrial
times. We hypothesize that the disease environment was a significant determinant of
marriage patterns, specifically that higher disease burdens required earlier FAFM. Our
data show a strong correlation between malaria burden and FAFM and offer a novel
theory for the origins of the EMP. To further isolate the causal relationship, we employ
an exogenous topography-derived measure of the disease environment, as well as a
model based on bordering parishes in Kent, England. These empirical analyses all show
strong support for our hypothesis. Given the potential (though debated) implications of
the EMP for economic growth, our results also demonstrate a mechanism through which
infectious disease environment might help to account for patterns of economic
development and divergence.
Keywords: Infectious Disease, European Marriage Pattern, Historical Demography,
Geography, Malaria
I. Introduction
The “European Marriage Pattern” (EMP) is one of the truly enduring hypotheses in
comparative social science. In 1965, John Hajnal published a classic study positing a fundamental
contrast between the marriage patterns of northwestern European societies and the rest of the
world. The EMP was characterized by relatively late female age at first marriage (FAFM) and a
high proportion of persons never marrying. The kernel of this idea was present already in Malthus,
who associated the “preventative check” of late marriage with western European societies. Unlike
many grand theories, the Hajnal thesis has stood the test of time. Five decades of research have
inevitably added nuance to the argument and underscored the existence of variation within both
Europe and Asia (Smith 1980; Hanley and Wolf 1985; Reher 1998; Engelen and Wolf 2005;
Lundh, Kuros et al. 2014; Campbell and Kuroso 2018). But the central claim of the paper has been
repeatedly validated and remains one of the most notable patterns in demographic history.
In recent years the EMP has been implicated in debates over economic development and
divergence.1 In brief, the EMP prevailed in the parts of Europe that were the first to experience
modern economic growth. It has been hypothesized that the EMP was a causal factor contributing
to early development. De Moor and van Zanden (2010) argued that the EMP fostered what they
called “Girlpower.” Later female age at first marriage supported the expansion of markets and the
formation of human capital; such a link is also supported when examining a particular country
(e.g., England in Foreman-Peck and Zhou, 2018). A debate has ensued, with proponents of the
theory contending that late marriage ought to be considered a major factor in early economic
development and critics questioning the link (Dennison and Ogilvie 2014; Carmichael et al. 2016;
1 For another way in which marriage structures can have powerful effects on other social institutions like democracy, see Schulz 2017.
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Edwards and Ogilvie 2018). The issue remains highly contested, but there is no doubting the
historic importance of the EMP itself.
Despite its manifest importance, the origins of the EMP remain obscure. Hajnal’s original
paper (1965), and his major sequel (1982), are descriptive rather than explanatory. For a time, the
prevailing theory held that late marriage was associated with nuclear family forms and neo-
locality; by contrast, early marriage was more common in societies with complex, joint-family
systems. But this theory did not withstand rigorous investigation (see Saller 1994 for ancient
Rome; Reher 1998 for Spain). More recently, Voigtländer and Voth (2013) have argued that a shift
to pastoralism in the wake of the Black Death, which pulled women into the labor force and
delayed marriage, could account for the rise of the EMP.2 Late marriage has also been attributed
to lower mortality rates (Foreman-Peck 2011; Foreman-Peck and Zhou 2018). Foreman-Peck
showed how an exogenous decrease in the mortality rate would increase the age at first marriage.
In brief, higher mortality rates forced women to marry earlier to ensure that the desired number of
surviving children was reached. In a cross-Europe panel, based on 19th-century data, societies with
early death were shown to practice early marriage. But it remains to be established whether
development caused death rates to fall and thus caused later FAFM, or whether exogenous
environmental conditions created lower death rates and thus later FAFM.
Our paper proposes that the answer lies in infectious disease ecology, a crucial yet
neglected deep factor that could account for the EMP. In pre-modern societies, infectious diseases
were the prime determinant of morbidity and mortality (Fogel 2010; Mercer 2014; Deaton 2015).
Infectious diseases are environmentally sensitive, so it is highly plausible that mortality rates are
2 In their model, women have a comparative advantage in livestock rearing over arable farming. The mortality shock from plague increased the amount of land given over to extensive pastoral production. Using county-level data from England, they indeed showed that pastoral agriculture pulled women into the labor force and delayed marriage.
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responsive to environmental conditions. We seek to establish that FAFM was influenced by such
environmental factors via the disease burden, and that the EMP emerged in regions with a lower
infectious disease burden. While we hasten to add that a phenomenon as complex as FAFM likely
has numerous determinants, the prevailing disease regime was an important and heretofore
neglected cause.
There is an obvious reason why such a powerful factor has been neglected: lack of data.
Today, datasets like the WHO’s Global Burden of Disease (GBD) provide a remarkable source of
cause-of-death statistics based on global surveillance and modern disease categories. To be useful,
such data ought to be based on two foundations: (1) reliable surveillance, such that all deaths are
captured and categorized, and (2) valid biomedical classification, such that the causes of mortality
are legitimate. The GBD has existed for one generation. The further back in time we go, the worse
both the surveillance and the validity of the classification become. Decent national statistics in the
US go back about a hundred years (Preston 1976). In Britain and much of Europe, national cause-
of-death statistics were gathered from the late-19th century onward. Disease categories were
progressively improved via international standardization and the spread of germ theory (Moriyama
et al. 2011). Further back, there are city records (such as the London Bills of Mortality), a tradition
that seems to have started in northern Italy in the wake of the Black Death, but the disease
classifications are primitive and the geographic coverage is narrow (Alter and Carmichael 1999).
The economic or demographic historian interested in cause-of-death statistics before modern
growth is seriously hampered by the lack of good data.
This general situation may represent more of a conundrum than has been fully realized.
Significant variation in the infectious disease burden existed before modern development;
infectious diseases influence developmental pathways and in turn are influenced by development;
5
yet, direct measures of the disease burden are all far downstream of economic development,
obscuring the role of pre-existing variation in the infectious disease environment.
Given the importance of health and disease, it is worth trying to overcome these obstacles
to historical investigation. In our effort to do so, we have chosen to focus on the influence of
malaria at regional and sub-regional scales. We believe that this approach provides a significant
improvement on the literature on infectious disease for several reasons. First, malaria has been a
major burden on human health, and historically its spatial reach was far greater than it is today,
including much of Europe. Second, before modern eradication efforts, the prevalence of malaria
was persistent throughout history, determined in large part by physical geography (unlike other
causes of death and the overall mortality rates used in the literature). In other words, malaria
prevalence, as a variable, is less likely to be affected by other factors and more likely to be
exogenous. Third, we have been able to locate unique samples where we expect variation in the
disease environment and have a consistent index or proxy for the malaria burden. We also borrow
insights from recent advances in geography by constructing an exogenous measure of the malaria
burden in an area. This measure is called topographic wetness index (TWI), which is based only
on the exogenous geographic landscape in the area.
Finally and most importantly, we have been able to construct indices that provide valid
measures of the varying malaria burden across regional and sub-regional scales. Unlike typical
cross-country analysis, where there are major concerns with unobserved country-specific
heterogeneity that could account for the relationship, we are able to compare culturally,
technologically, and institutionally homogenous groups by focusing on such regional and even
sub-regional data. In one of our cases, we are able to employ a model based only on individuals in
neighboring parishes that share a geographic border. Most of the potential confounders are thus
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reasonably controlled for. We also minimize the bias that may result from systematic differences
in the national reporting systems when conducting cross-country analysis. 3 As a result, the
documented influence of the exogenous disease environment is the most probable explanation for
observed variation in demographic patterns.
Using the unique historical datasets (both sub-regional and micro-level) that we compile
(with information on malaria-specific mortality rates and related characteristics in pre-modern
Italy and England), we show a strong correlation between malaria burden and female age at first
marriage, the central characteristic of the EMP. Our preferred model based on bordering parishes
and an exogenous measure of disease environment lends further support for our hypothesis.
Our paper connects with and contributes to the literature on the significant role of
health/disease in economic history in three different ways: substantively, empirically, and
methodologically. First, we add to the growing literature substantively linking the disease
environment to important demographic and economic outcomes, e.g., fertility, saving and
investment, as well as human capital and worker productivity (Sachs and Malaney 2002; Almond
2006; Lorentzen et al. 2008; Eppig, Fincher, and Thornhill 2010; Zhang n.d.).4 The disease
environment has a strong and direct influence on human capital formation via its effect on
physiological development (Bloom, Canning, and Fink 2014; Baird et al. 2016).
Most significantly, there is robust debate about whether the geography of disease has
determined the geography of development, or vice versa. The relationship between economic
3 For example, using a similar methodology but different datasets, McCarthy and others found smaller growth effects of malaria than Gallup and Sachs (2001). The differences may indeed be due to the systematic differences across countries in the WHO data because the data “because the data “permit only very limited comparison between countries or even between various periods for the same country”. 4 Acemoglu and Johnson (2007) also find a strong relationship between disease and life expectancy, although the impact of life expectancy on growth in their framework is not significant.
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development and the decline of infectious disease is significant and complex.5 For instance,
Acemoglu, Johnson, and Robison (2001) argued, “Tropical diseases obviously cause much
suffering and high rates of infant mortality in Africa, but they are not the reason that Africa is poor.
Disease is largely a consequence of poverty and of governments being unable or unwilling to
undertake the public health measures necessary to eradicate them.”6 This view has been criticized
by Jeffrey Sachs, among others (e.g., Austin 2008). In his words, the prevalence of infectious
disease in Africa (particularly malaria) “is not just a consequence of poverty but also a cause of
poverty” (Sachs 2012). In a series of papers, he and coauthors showed that, controlling for the
quality of institutions, geography still exerted an effect through the influence of the infectious
disease burden (Gallup et al. 1999; Sachs 2003). This view is also confirmed in more extensive
investigations of the issue, e.g., Carstensen and Gundlach (2006). Our findings of a large impact
of disease burden on an important demographic outcome again underscore the important role of
the disease environment in economic history. To the extent that the EMP is linked to economic
growth, our paper also provides a potential mechanism through which infectious disease
environment might help to account for patterns of economic development and global divergence.
Second, our paper extends the line of research on disease environment deeper into the past
with our pre-modern data. The Sachs argument has been shown to work in shallow historical time.
In his hypothesis, the effect of a heavy disease burden has been to “cause poverty” by impeding
5 There is broad agreement that some combination of better nutrition, advances in public health, and biomedical interventions led to the decline of infectious disease risk (Schofield, Reher, and Bideau 1991; Riley 2001; Cutler, Deaton, and Lleras-Muney 2006). The immune system is metabolically expensive, and poor nutrition and infectious disease morbidity and mortality go hand in hand; rising living standards have thus depressed the effect of infectious diseases (Harris 2004). Public health measures, including better sanitation, hygiene, and clean water, as well as landscape manipulation and interventions like quarantine, helped to control the influence of infectious disease (Szreter 2005). While a few early biomedical advances were important (“Jesuit’s bark” as a treatment for malaria, smallpox inoculation and then vaccination), it was not until the 20th century that antibiotic pharmaceuticals and widespread vaccination exerted a meaningful influence (Davenport, Boulton, and Schwarz 2016). 6 Acemoglu and Robinson (2013) 51.
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economic development, particularly catch-up growth. We argue that geographic patterns of the
infectious disease burden worked in deeper time too, and not only in the contrast between
equatorial Africa and the rest of the world.7 As we will show, even within Europe, geographically
structured variations in the infectious disease burden were consequential.
Finally, we also apply a novel method to exploit data from neighboring areas to isolate the
main potential confounders that may impede us from drawing a causal conclusion regarding the
impacts of the disease burden. This approach may be useful for many similar situations since
historical data are oftentimes too limited to provide sufficient information to control for potentially
confounding variables in empirical analyses, let alone test for plausible exogenous variations in
the disease environment to identify the causal parameter.
II. The Ecology of Infectious Diseases
The “demographic transition” of the last century and a half has radically altered the basic
conditions of life and death for humanity. Life expectancies are now so high in the developed
world that levels of pre-transition mortality seem uniformly abysmal. Even the healthiest pre-
transition societies would rank far below the least healthy societies on the planet today. But these
drastic improvements in health can introduce a distorting effect, for pre-transition societies were
far from equally abysmal. The diversity of mortality conditions in the past was not immaterial.
Some societies were significantly healthier than others, long before the modern interventions that
7 This is actually consistent with Acemoglu et al. (2001). In that paper, the authors argued the disease environment in the past determined what kind of institutions European imperial powers set up in colonial territories. In environments with a heavy endemic disease burden, Europeans did not settle but imposed extractive institutions; in healthier environments, Europeans settled and established cooperative institutions. These institutional differences have been persistent, they argue, and account for the wealth and poverty of nations today.
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controlled infectious disease. We contend that economic historians have not paid sufficient
attention to either the underlying causes or the downstream effects of the varying health regimes
of pre-transition societies. Consider a small sample meant only to illustrate the existence of such
variation, including a set of regional averages calculated by the historical demographer James
Riley.
Historical Life Expectancies (e0) Hunter-Gatherer Societies8 Hadza 34 Ache (forest period) 37 Hiwi 27 !Kung 36 Agta 21 Forager-Horticulturalist Societies Yanomamo Mucaj 39 Yanomamo 21 Tsimane 42 Gainj 30 Agricultural Societies Roman Egypt9 ~22-25 Late medieval France10 ~25 Early Modern Germany11 ~35-7 India, 1891, males12 24.59 India, 1891, females13 25.54 China, early 20C14 24 England, 160015 37.5 Life Expectancy Before Health Transition, By World Region16 Africa 26.4 Americas 34.8 Asia 27.5
8 Gurven and Kaplan 2007. 9 Bagnall and Frier 1994, 109. 10 Biraben 1988. 11 Knodel 1988, 59. 12 Narain 1929, 322. 13 Narain 1929, 322. 14 Barclay 1976 15 Wrigley et al. 1997, 295. 16 Riley 2005, 538.
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Europe 34.3 Former Soviet Union 29.0 Oceania – indigenous 22.5 Oceania – white 45.6
Because infectious diseases were overwhelmingly the leading cause of death in pre-
transition societies, it is reasonable to presume that differences in mortality were related to
variations in the prevalence of infectious diseases. In this paper we focus on malaria, a major
infectious disease. It is therefore important to describe why malaria is of particular importance and
why variation in the prevalence of malaria has a pronounced geographic structure.
It would be hard to overstate the importance of malaria for human history (Gallup and
Sachs 2001; Webb 2009; Packard 2011).17 Significant progress has been made to reduce malaria.
Malaria was eradicated from Europe and North America in the 19th and 20th centuries (although
some drainage projects may have reduced its impact already in the 18th century), and it has now
been eradicated in large parts of Asia and Africa (Bruce-Chwatt and Zulueta 1980; Riley 1986).
Even in the last generation, significant (though far from complete) progress has been made in
Africa thanks to intensive global health interventions (Murray et al. 2012). Despite the progress,
however, malaria is still “one of the world’s greatest public health challenges, with an estimated
3.2 billion people at risk of infection” (Yamana, 2015). In 2013, there were nearly 600,000 deaths
among 198 million people infected (World Health Organization, 2014).
Malaria is caused by protozoan parasites, two of which pose significant burdens on human
health, Plasmodium vivax and Plasmodium falciparum. Although these species are related, they
emerged as human pathogens from two separate evolutionary events (Liu 2013; Loy 2017; Otto et
17 The term “malaria” comes from the Italian for “bad air;” thus, long before the true etiology of the disease was understood, it was recognized certain places were naturally unhealthy.
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al. 2018). The diseases they cause in humans follow similar physiological processes. 18 To
distinguish the closely related clinical diseases caused respectively by Plasmodium vivax and
Plasmodium falciparum, we will refer throughout to vivax malaria and falciparum malaria. While
vivax malaria can cause high morbidity and mortality, falciparum malaria causes an even more
severe clinical disease with higher case fatality rates (Carter and Mendis 2002; Baird 2013).
Malaria parasites are transmitted exclusively by mosquitos in the genus Anopheles.19 There are
about seventy different species of Anopheles mosquitos that are competent vectors of malaria, and
the different species of the malaria parasite are adapted only to certain species of Anopheles
mosquito (Sinka 2010; Molina-Cruz et al. 2016).
The relative incidence of malaria is determined by unchanging features of physical
geography, since the distribution of malarial diseases is strongly influenced by the distribution of
mosquito vectors, which itself is highly dependent on geographic environment.20 While human
alteration of the landscape and changes in population density influenced the prevalence of malarial
diseases, permanent climatological characteristics such as temperature, precipitation, humidity,
and seasonality were primary in shaping the ecology of malaria. As noted in Yamana (2015),
“malaria transmission occurs primarily in the wet season when rain-fed water pools form and serve
as mosquito breeding sites. Temperatures affect the mosquito’s ability to transmit the disease.”
18 Malaria is an acute disease characterized by intermittent febrile illness; intense fevers of different periodicities are determined by the replication cycle of the parasite in the blood. During the blood stage of the disease, malaria parasites invade erythrocytes; infection causes severe anemia and a violent immune response. 19 Mosquitos are flying insects whose females specialize in a form of parasitism based on the consumption of blood from a host; they have evolved the ability to pierce the epidermis and extract blood with a needle-like tube called a proboscis (Spielman and D’Antonio 2001). From the perspective of a microscopic pathogen, mosquitos are attractive vectors – biologically abundant, highly mobile, and able to help the pathogen bypass some of our immune system’s most formidable barriers and innate mechanisms. Mosquitos have played and continue to play an outsized role in human health. 20 Other factors, such as host genetics (many sub-Saharan Africans lack the Duffy antigen and are refractory to infection by vivax malaria) also play a role (Carter and Mendis 2002). The vivax parasite evolved in the Pleistocene and migrated with humans out of Africa; the falciparum parasite is younger but reached its modern diffusion across the Old World thousands of years ago, so that we can consider it a permanent feature for our purposes. See Packard 2011 and Otto et al. 2018 for the genetic evidence.
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Permanent geography is indeed the reason why malaria is so persistent and “intensive efforts to
eliminate malaria in the most severely affected tropical countries have been largely ineffective”
(Gallup and Sachs, 2001).
Latitude is the most fundamental fact of physical geography behind these permanent
features. The importance of latitude for disease ecology in general is recognized in the familiar
terminology of “tropical diseases.” Lower latitudes receive more solar energy and have higher
temperatures as well as weaker seasonal temperature variation; in general, the tropics also have
high humidity and high levels of precipitation. These conditions foster what is known as the
“latitudinal species gradient,” the most prominent pattern of global biodiversity: the greater density
and greater variety of life near the equator. The prevalence of pathogenic organisms at lower
latitudes is simply a special case of the latitudinal species gradient (Guernier, Hochberg, and
Guégan 2004; Dunn et al. 2010; Stephens et al. 2016). It should be noted that elevation plays a
similar role as latitude. High-elevation regions tend to be cooler and drier, low-elevation regions
warmer and more humid. Many pre-transition societies experienced sharp “altitudinal” gradients
in morbidity and mortality: healthy highlands, lethal lowlands.
Prior to eradication, malarial diseases were present across major parts of Europe, and their
prevalence was highly geographically variable even within Europe or a particular country. Most
fundamentally, Southern Europe sits along the transitional zone between the subtropics and the
temperate latitudes (Bruce-Chwatt and Zulueta 1980). In the pre-eradication era, parts of southern
Europe were afflicted by falciparum malaria, whereas vivax malaria prevailed to the north.
Furthermore, regions with ecologies that supported large populations of competent mosquito
vectors were zones of endemic malaria. Marshes, swamps, and estuaries were often notoriously
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unhealthy. In England, for example, low-lying fens and marshy regions in the east and south were
rife with “ague,” intermittent fevers attributable to the vivax parasite (Dobson 1997).
In light of its important role in human history and significant geographic component, we
focus malaria as a primary measure of disease environment. Because malaria was a major influence
on morbidity and mortality in pre-modern times, variations in malaria prevalence are likely to
capture an important piece of the overall variability in patterns of health, disease, and mortality.
For instance, even in England, which knew only vivax malaria (the less virulent of the two major
forms of the disease), the influence of the malaria burden was profound. Mary Dobson has shown
that in the 17th century, the annual death rate in the most malarial parishes exceeded 50 per 1000;
in the least malarial parishes, by contrast, located in healthy inland and upland environments, the
death rate was in the 20s per 1000 (Dobson 1980; 1997).
Since malarial diseases are primarily determined by the permanent features of physical
geography, their prevalence is highly persistent in the pre-eradication period. This fact also allows
us to overcome the stark empirical challenges in studying the role of infectious diseases as a factor
in historical demography. Regional variations in malaria prevalence in more recent times can
plausibly act as a proxy of malaria and disease burden in past times. We can also exploit the
exogenous features of local geography to measure environments particularly conducive to malaria.
Reconstructing past disease environments in any quantitative fashion is enormously difficult, and
this barrier probably accounts for the relative neglect of infectious diseases in economic and
demographic history. The focus on malaria – a major infectious disease that is persistent and
largely (exogenously) determined by permanent geographic features in the pre-modern period –
permits us to overcome this obstacle.
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III. Descriptive Analysis
Our first set of empirical analysis is descriptive and uses regional data from two European
countries, Italy and England, where we are able to construct information on malaria-burden at such
levels, as opposed to macro data used in typical cross-country analysis. Any reported “average”
age at first marriage is necessarily an aggregate, and we maintain that it is important to consider
the possibility of spatial variation at small scales. Studies that analyze composite national averages
might obscure significant regional variation, and more importantly, fail to control for significant
confounders at the country level. Malarial diseases are highly fixed by local ecological conditions,
and even regional averages – county or province-level measures for instance – might only be
aggregations of very different microregional patterns. Evidence from Italy and England suggests
as much. In what follows, for each country, we will provide some background in this context,
explain the sources of our data, and present the corresponding results.
A. Italian Regions
Italy is a significant test case because (1) its historical documentation is reasonably rich
and has been amply studied and (2) malarial diseases were prevalent but with strong regional
variation (see generally Snowden 2008; Bruce-Chwatt and Zulueta 1980). In Italy, three main
species of competent Anopheline vectors transmitted malarial diseases: A. sacharovi, A.
labranchiae, and A. superpictus. The latter two were of greatest importance in central and southern
Italy. A. labranchiae was probably the most important vector of malaria. It mostly breeds in fresh
water and was common in coastal plains; it is not found north of ~42-3° N; it is generally a good
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vector up to about 300 masl. It prefers regions with a minimum temperature of 14.3° C and an
average diurnal temperature range of 12.7 °C. It feeds on a range of species but prefers human
blood. It will bite aggressively and eat indoors. Feeding behavior is most intense around sunset.
For Italy, we have constructed regional malaria-burden indices from late 19th-century
national cause-of-death statistics. In Italy, national statistics become increasingly available after
unification (1871). Large-scale malaria eradication efforts did not pick up pace until the very end
of the 19th and beginning of the 20th century. There is thus a window of a few decades, before
malaria was controlled, during which reasonably good official statistics exist. Before germ theory
and modern diagnostics, nosological categories did not align perfectly with the actual infectious
causes of various diseases. For the malaria-burden index, we have used a six-year average of deaths
from “malarial fevers and swamp wasting” during the years 1887-1892. Using the regional
populations from the 1881 census, we take the average annual malaria deaths per 100,000
population to construct the index. The measurements span from Liguria, with a negligible 3.03
deaths per 100,000 from malaria, to Sardinia, at 308.60.
We have identified three sources of data for historical female age at first marriage in the
existing historical demography literature, providing a reasonably representative sample of
variation across Italy. We call attention to an important dataset from southern Italy constructed by
the historian Gérard Delille. Delille used a sample of parish records from the Kingdom of Naples
between the 16th and 18th centuries to recover data about the social and demographic history of a
number of villages. We also use syntheses by Rettaroli and by Dal Panta and Livi-Bacci which
report 42 regional measures from the 17th and 18th centuries.
16
Figure 1. Annual deaths from malaria per 100,000 population (Late 19th century.
Map source: the authors. Data source: see text).
17
Figure 2. Distribution of Age at First Marriage (Southern vs. Northern Italy, 17-18th
century. Graph source: authors)
Figure (1) depicts the geographic distribution of malaria burden (annual deaths from
malaria per 100,000 population) in Italy. Northern regions are generally associated with lower
malaria burden than southern regions. Figure (2) plots the density of age at first marriage by
regions. In general, northern regions with low malaria incidence exhibit the EMP, while in
southern regions with higher malaria burden, women married significantly earlier.
To quantify the actual impacts of malaria burden on marriage age, we perform regression
analysis using our data. The results are presented in Table (1). The first column shows the bivariate
association between malaria burden and age at first marriage. We confirm a strong negative
relationship indicated in Figure (2). When the malaria burden increases, age at first marriage
indeed decreases. In other words, women marry early in less healthy areas, but delay their
marriages in more healthy environments. The effect is both statistically and economically
significant. Every death per 100,000 could lead to a reduction of 0.051 in age at first marriage in
the area. The average malaria burden is 102.07 and 14.27 per 100,000 in the Southern and Northern
Italy, respectively. Using our estimated effect, such stark difference is translated into a difference
of 4.47 years in age at first marriage between regions.
To further control for variation in possible underlying institutions and cultures, we also
include region fixed effects (North vs. South) in the Column (2). Our result continues to hold. The
estimated effect is relatively stable, and the difference between the results in Columns (1) and (2)
is statistically insignificant. One may be concerned about the systematic differences in
measurements due to the sources of the data. We further control for such differences by adding the
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source fixed effects in Column (3), and the results remain unchanged. Finally, our results are also
robust to inclusion of period fixed effects accounting for variation in age at first marriage over
time (Column 4).
In Columns (5) and (6), we repeat our analysis with the full set of controls using only the
Delille (1985) data. The data from Delille (1985) are primarily from Southern Italy and also have
more refined year information from which age at first marriage is collected. This allows us to
further control for any unobservable region-specific differences that cross-country analyses
cannot. In Column (5), we continue to find a significant, negative relationship between malaria
burden and age at first marriage. In Column (6), we also experiment with a linear time trend and
find the same result.
B. Southern Italy, 16th-18th Centuries
In the previous analysis, we focus on province-level measures of the malaria burden. With
the Delille data, we can further examine the relationship between malaria burden and marriage age
at the parish level. While not providing direct information on malaria burden, his data have
information on the type of the dominant mode of agricultural production, which is a useful proxy
for the ecology of malaria. As discussed earlier, the ecology of malaria is relatively fixed by the
unchanging features of physical geography. The lowland villages faced greater exposure to malaria
due to the ecological preferences of the Anopheline mosquito vector; conversely, the inhabitants
of the upland villages were buffered from malaria exposure. On the other hand, the villages that
practiced extensive agriculture were lower in elevation and dependent upon arable wheat farming.
The villages that practiced arboriculture relied more heavily on the production of grapes and olives
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and tended to be higher elevation. Together, it suggests that extensive agriculture is more likely
than arboriculture to be associated with a higher level of malaria burden. We verify this hypothesis
by estimating the relationship between the provincial level measure of malaria burden and the type
of agricultural mode. The result is presented in Column (1) of Table (2). While imperfect, the
finding confirms this hypothesis.
Figure (3) displays the density of age at first marriage by type of agricultural mode using
the Delille’s sample. The average age at first marriage for females in the villages that practiced
extensive agriculture was 19, and in the villages where arboriculture dominated it was 24.5.
Columns (2) of Table (2) quantifies the statistical significance of this observed difference. We also
further control for time trend, but our result continue to hold (Column (3) of Table (2)). It is
remarkable that even villages in relatively close geographical proximity experienced radically
different demographic regimes.
Figure 3. Distribution of Age at First Marriage (Extensive vs Arboriculture, 16-18th
century. Graph source: the authors).
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C. England: Counties
As the heartland of the Industrial Revolution, the economic history of England has always
been of particular importance. Moreover, England’s demographic history is better researched and
better understood than any other nation’s, due to the rich historical record and the long tradition of
scholarship, highlighted by the monumental efforts of the Cambridge Population Group (CPG).
But with a few notable exceptions like Mary Dobson’s Contours of Death and Disease in Early
Modern England, infectious disease has not received its due attention. Certainly, economic
historians have given little consideration to the geography of infectious disease in shaping
demographic patterns within England. This is not necessarily surprising. Compared to the rest of
Europe, with the exception of Scandinavia, England was naturally healthy. Long before it
experienced development, England enjoyed a relatively gentle disease regime – except in its
malarial regions. As we will show below, the general impression of a healthy environment in
England may mask significant regional variation, leading to an underestimated role of the ecology
of infectious disease in explaining the country’s demographic patterns. More broadly, the very
absence of a deleterious disease ecology in most of England deserves greater attention.
In pre-modern times, vivax malaria, transmitted by Anopheles atroparvus, was a significant
burden on human health in the marshlands and fens. To measure the malaria burden, we have
borrowed an index created by Kuhn et al. in 2003. They tabulated deaths attributed to “ague” by
county between 1840 and 1910 in the national records of the Registrar General and constructed an
ague death rate per 100,000 population. Ague was a common term for an acute, intermittent fever,
21
and it is a reasonable proxy for malaria burden. While the dataset covers a short period of time
overlapping with the later 19th century, a period in which the prevalence of malaria had started to
decline, the ague death rate by county captures regional variation, and this variation appears to
have been of long standing.
Indeed, there is strong confirmation from England for the historical validity of the ague
death rate as an index of malaria burden. The bioarchaeologists R. Gowland and A. G. Western
(2012) conducted a geospatial study of the incidence of osteological lesions from skeletons of the
Anglo-Saxon period. They attribute the non-specific indications of stress known as cribra orbitalia
as a sign of vivax malaria. The incidence of these skeletal markers correlates strongly with a
mosquito-prevalence index they construct based on modern observations. Most importantly, the
patterns they found suggest deep continuity in the geographic structure of malaria prevalence. Not
only does their work validate the ague death index, it confirms that studying malaria is a useful
way to gain significant insights into the historical infectious disease burden.
For historical data on female age at first marriage, we use the well-known parish
reconstitution study of Wrigley et al. (1997). These figures thus come from what is probably the
most carefully validated dataset in historical demography. Using family reconstitution methods,
the study is based on a complete reconstruction of the demographic history of 26 parishes across
England from 1580-1837. This same dataset is also the basis of Voigtländer and Voth’s (2006)
study on the origins of the EMP.
Using these two datasets, we estimate the relationship between malaria burden and female
age at first marriage. The results are presented in Table (2). In Columns (1)-(3), we present results
for the entire period covered in Wrigley et al. (1997)—five periods between 1600 and 1837. In
Column (1), we continue to find a statistically significant, negative impact of malaria burden on
22
age at first marriage. Specifically, every additional death per 100,000 decreased age at first
marriage by 0.2. This again implies that a healthy environment is associated with the EMP.
Comparing the parish with the maximum ague rate (194.28) and the one with the minimum (11.68),
the average difference in age at first marriage can be as large as 7.87 years. In Column (2), we
control for period fixed effects, the size of and significance of the coefficient is nearly the same.
In Column (3), we further control for region fixed effects in our model. The inclusion of the region
fixed effects is most significant since malaria burden is highly determined by geography, as
discussed above. It is thus not surprising that the magnitude of the coefficient decreases (from -.02
to .-009). What is surprising, is that even after controlling for the regional fixed effects and thus
any differences in geographic environment (including malaria burden) across regions, there is still
significant within-region variation in health environment, leading to a highly significant estimate.
This also indicates the robustness of our findings. The coefficients are similar and even
strengthened when we focus on the early period 1600-1749. It is worth observing that in the later
18th century, while the EMP is still persistent, there was some convergence in the age at first
marriage, reducing the variation in the outcome and possibly accounting for the weaker effects
after 1750 in our data. We also note that large-scale drainage efforts from the mid-18th century
may have reduced the malaria burden in England (Riley 1987). Specifically, the standard
deviations of age at first marriage before and after 1750 are 1.37 and 1.06, respectively.
A Naïve Investigation of Potential Mechanisms.
Our empirical analysis above shows a strong reduced-form relationship between the
malaria burden and female age at first marriage. There are many potential mechanisms through
23
which the malaria burden can affect age at first marriage. For example, as alluded above, the
malaria burden may increase mortality rates in a region, which, combined with people’s desired
number of children, may in turn “push” people to marry early. Similarly, changes in the disease
environment can also change the type of agricultural production, which may impact female
employment and hence marriage behavior. While these mechanisms are generally difficult to test
generally due to lack of historical data, we are able to examine the implications of the production
channel here.
In Voigtlander and Voth (2013), the authors consider that the EMP can be partially
attributed to a shift to pastoral sector (in favor of female employment) due to the land abundance
that resulted from the Black Death. The authors indeed find strong empirical evidence supporting
their argument. A more infectious environment with high mortality rates can similarly lead to
relative land abundance, which would also favor the development of the pastoral sector. Similarly,
the impact of the Black Death could have been more severe in a less healthy environment. As a
result, if pastoralism is partly attributed to disease environment, but disease environment also
affects other determinants of female age at first marriage, then inclusion of the malaria burden can
weaken the results in Voigtlander and Voth (2013). Here, we investigate this possibility.
Specifically, we repeat the empirical analysis presented in Table 4 of Voigtlander and Voth (2013)
by further including our “ague” variable. The results are presented in Table (3). All the odd
columns correspond to the original results from Voigtlander and Voth (2013), and all the even
number columns to the right of each odd-numbered column correspond to our results. As is
evident, the coefficient on the malaria burden is always negative. More importantly, while the
results in VV continue to hold after the malaria burden is included, the magnitudes of their
coefficients decrease. When we focus on the early period (1600-1749), their OLS estimates
24
(Columns (7)-(10)) decrease both in terms of magnitude and statistical significance. In contrast to
the insignificant results of their measures of pastoralism and DMV, the coefficient on ague
continue to be economically and statistically significant. While this is not a definitive test of the
mechanism, it does underscore the possibility. We leave this type of exercise to future research.
IV. Further (Preferred) Analysis
We have thus far provided empirical evidence supporting the relationship between the
disease burden (proxied by malaria specific death rates or related characteristics) with female age
at first marriage for regional and even micro-level data. Even though the malaria burden is fixed
and our analysis focuses only on within-country variations, thereby controlling for many
institutional and cultural differences at the country level, one may still be reasonably concerned
that our measures above may be an (endogenous) outcome of development, or that the relationship
may simply be driven by reverse causality.
We address these issues further using two approaches. First, we adopt an exogenous
measure called the topographic wetness index, which is an index designed to identify the effect of
local topography on hydrologic processes such as run-off. Second, we employ a model that further
takes into account any heterogeneity common to bordering parishes. This is one of the first
applications of such method to address the endogeneity issue in a historical context, to the best of
our knowledge.
Note that our goal is to identify the reduced-form causal impact of malaria environment on
female age at first marriage, instead of the mechanisms through which malaria environment affects
marriage outcomes. Existence of such intermediate channels does not invalidate our approach.
25
Rather, it simply implies that the estimated effect should be interpreted as the total impact of
malaria environment on female age at first marriage.
A. Data and Exogenous Measure of Malaria Environment (Wetness Index)
Ecclesiastical records from preindustrial England have proven one of the richest sources
for the study of historical demography. Here, we have exploited a novel source of individual-level
data on female age at first marriage from the county of Kent (approximately 3700 km2, with a
contemporary population of 1.8 million). Kent, in the southeast corner of England, is of particular
interest because it was one of the most notoriously malarial regions of the country. And yet, even
within the county, the disease burden varied due to physical features of the environment – the
marshy lowland parishes being reputedly unhealthier than the well-drained upland parishes.
Prior to civil marriage in 1836, legal marriages in England were administered by the church,
and the vast majority of marriages were conducted by the Church of England. In part to prevent
consanguineous or otherwise invalid marriages, a couple wishing to be married had to acquire
permission via a license from the bishop or via the “bann” (a waiting period of three weeks during
which the impending nuptials were publically announced). The ecclesiastical authorities in
Canterbury (the episcopal seat of Kent) kept records of the marriage licenses issued from the 16th
century onward.
Marriage licenses are a major source of genealogical reconstruction, preserved in church
archives across England. By a stroke of good fortune, in 1892, an antiquarian named Joseph
Meadows Cowper published in five volumes the marriage licenses preserved in the Archives of
Canterbury because of their genealogical interest. Each marriage recorded follows a standard
format, providing necessary information for our analysis, albeit limited, such as the name of the
bride and groom, their home parish, and date of marriage.
26
Our measure of malaria environment comes from the recent literature on both malaria and
geography. As discussed earlier, the distribution of malaria is strongly influenced by the
distribution of mosquito vectors, which itself is highly dependent on geographic environment.
Wetter areas typically serve as mosquito breeding sites and thus tend to bear higher malaria burden.
The medical literature on malaria has proposed the topographic wetness index (TWI) as an
approximate measure of predicted water accumulation and shown the strong association between
malaria and the TWI (e.g., Cohen et al. 2008, 2010). The TWI is “calculated as the ratio of the
area upslope from any given point on the landscape to the local slope at that points, and thus
represents the amount of water that should enter a given spatial unit divided by the rate at which
the water should flow out of that unit.” The TWI is an appealing measure for two reasons. First, it
provides a “simple, biologically meaningful description” of the malaria environment. Second, it
is only determined by the local shape of the land and plausibly exogenous to other variables that
may confound our analysis.
By merging data from marriage licenses with the TWI within a parish, we have drawn
4,843 observations of a female age at first marriage in which the woman is identified as a virgin
and her home parish is named and has valid information on the TWI. We have covered the years
1615 to 1681 with more than 98 percent of the data from 1619 to 1645, and 261 parishes, 222 of
which share borders with another parish.
B. Formal Identification Strategy
To formalize our identification strategy, consider the potential outcome framework where
D denotes disease environment, and FAFM(d) is the potential female age at first marriage in
27
disease environment D = d. The marginal impact of disease environment on age at first marriage
increasing from d − 1 to d, the parameter of interest, is given by
β = FAFM(d) − FAFM(d − 1)]
Suppose that in the absence of high disease environment, female age at first marriage is
determined by factors such as local culture and economic development, as well as individual
characteristics, given by
FAFM-,/(0) = β1 + x′/α + u-,/
where x/ is a vector of spatially varied determinants of female i’s potential age at first marriage
(FAfM) in parish j, and u-,/ includes all individual characteristics, observable or not.
Together, this implies that the observed female age at first marriage is given by
FAFM-,/ = β1 + D/β + x′/α + u-,/
In this historical context, it is often difficult to have detailed individual-level information,
and even other aggregate-level information. In our case, we are focusing on already micro level
data. Other information at the parish level is hard to come by. As a result, in practice, we first
estimate the following model
FAFM-,/ = β1 + D/β + -,/
where = x′α + u. To consistently estimate β with cross-sectional ordinary least squares (OLS),
we require the following condition
[FAFM(0) D = d-] = [FAFM(0) D = d/] ∀i ≠ j
28
This condition states that the baseline expected potential marriage outcome are comparable
across parishes with different disease environments. In other words, disease environment, D, is
exogenous to (i.e., mean independent of) both x/ and u-,/. Given the construction of our measure
of disease environment, this assumption is likely to hold in our context since the topography within
a parish does not vary with individual characteristics and is also not affected by other parish-level
variables.
In our second approach, we further relax this condition. While our exogenously defined
disease environment may not be affected by other variables, it may still be correlated with
unobservable parish-level variables that capture marriage institutions and local culture. There
could also be cross-parish migration that affects marriage outcomes. We control for these possible
differences by focusing only on neighboring parishes that share geographic borders. For these
neighboring parishes within Kent (already a narrowly defined geographic region), the
geographically heterogeneous determinants of marriage outcomes (such as culture, climate and
economic development, as well as access to both marriage and commodity markets) are
approximately the same on both sides of the border. It is therefore plausible that the effects are
attributed only to the difference lies in the disease environment. Please note that this does not
exclude the possibility that there exist differences in other fundamental causes of female age at
first marriage between neighboring parishes; it simply means that such differences are orthogonal
to the differences in disease environment (determined by exogenous geographic features). This
also does not exclude the possibility that malaria environment may lead to different economic
outcomes, which in turn may be associated with age at first marriage; existence of such
intermediate channels again simplifies the interpretations of our estimates as “total impacts”.
29
the unconditional independence) should hold
[FAFM(0) D = d-, P] = [FAFM(0) D = d/, P] ∀i ≠ j
where P denotes the PBC pair of neighboring parishes. As a result, we further estimate the
following model
FAFM-,/ = β1 + D/β + λE + F-,/
where λE is the fixed effects for each pair of bordering parishes.21 Robust standard errors are
used in all estimations.22
C. Results
Table (5) displays the results using the exogenous wetness index as a measure of disease
environment. In Column (1), we use average wetness index in a parish. We find that disease
environment has a statistically, negative impact on female age at first marriage. Specifically,
increasing the wetness index by one unit can lead to a reduction of female age at first marriage by
approximately two months (. 155 × 12 months). Note that the standard deviation of the wetness
index is 1.06, so this impact is roughly the same as the impact of one-standard-deviation change
in the index on age at first marriage. To put this result further in perspective, note that the minimum
(maximum) wetness value is 10.1678 (17.028) and its corresponding age at first marriage 24.727
(20.5). Using our result, moving from minimum to maximum disease environment (holding
21 Note that in estimation all neighboring parishes are stacked up together to enable the identification of the fixed effects. 22 We do not consider further clustering in our analysis since our data are already focused on a narrowly defined geographic area, and it is too demanding for historical data like ours. However, under the assumption, our estimates are consistently estimated regardless of their precision.
30
everything else constant) can decrease age at first marriage by roughly 1.064 years, accounting
for more than 25 percent of the difference in marriage age between minimum and maximum
disease environments (=1.064/(24.727-20.5)).
In Column (2), we further control for the standard deviation of the wetness index within a
parish in our model. We continue to find the same result as above. In Columns (3) and (4), we
repeat our analysis in Columns (1) and (2) using instead the minimum wetness level as a measure
of disease environment. While slightly smaller than using the average index, the impact continues
to be sizeable and statistically significant.
Next, we repeat our analysis using only pairs of neighboring parishes. The results are
presented in Panel A of Table (6). We again find a statistically significant, negative impact of
malaria environment on female age at first marriage. The magnitude of the impact is smaller than
the results above, but such difference is not statistically significant. This result implies that while
there may be some spatially varied determinants of marriage outcomes or migration patterns across
parishes correlated with disease environment, such correlation or heterogeneity (as a whole) may
be too weak to impact our analysis. This is not surprising since our wetness index is considered to
be a rather exogenous variable. In Column (2), we again find that our result is robust to inclusion
of standard deviation of disease environment within a parish. In Columns (3) and (4), we continue
to find our results are robust to alternative measure of disease environment.
In Panel B of Table (6), we also exclude the parishes with fewer than 10 marriage records.
As we can see, while the estimates become smaller, the qualitative conclusions of our results
remain the same. We continue to find support for our hypothesis that malarial disease
environments correspond to lower age at first marriage.
31
V. Conclusions: Disease Environments in Past Times
We have argued that the geography of disease shaped the demography of pre-transition
societies. Perhaps the idea that disease environments had profound social and economic effects
has been not so much radical as difficult to test. Already in 1988 Wrigley suggested that “the
European marriage system is a ‘luxury’ that populations through much of the traditional world
may have been unable to afford. Where endemic diseases were many and fatal, where epidemic
diseases were frequent and devastating, where food supplies were subject to violent and
unpredictable fluctuations, or where some combination of these dangers prevailed, early and
universal marriages may have been mandatory.” But reconstructing disease environments in the
deep past is difficult due to the lack of evidence, particularly at the cross-country level that is of
interest to economic historians. Our paper has found a way around this obstacle, by focusing on a
disease that was important in past times and geographically structured in its prevalence; moreover,
we have been able to capture the effect of geography within small, otherwise homogeneous
regions, where it is unlikely that cultural or institutional factors could account for the observed
effects. The results suggest a powerful role for exogenous geographic factors in the patterns of
pre-industrial demography and the pathways to development.
We have focused on the connection between disease environments and FAFM. Others have
already made a strong case for the link between the European Marriage Pattern and the origins of
economic development, even though these claims remain the subject of ongoing debate. We also
believe that the relationship between geography and growth were more direct, operating via the
influence of disease on human health. The evidence that a heavy burden of infectious disease
impairs cognitive development and human capital formation is overwhelming. But this evidence
has been accumulated in contemporary times and used to show that poor health retards “catch-up”
32
growth. The same mechanisms would have operated in deeper time, as cognitive health, human
capital formation, and thus economic development could have been easier in ecologically
privileged environments such as northwestern Europe.
We do not expect that a healthy environment was a sufficient condition for the emergence
of modern growth, and the example of Scandinavia – with low natural disease burden – points to
the importance of other cultural, institutional, and geographic factors, too, such as proximity to
markets and access to natural resources. Moreover, we would call attention to the need to
investigate whether the advantages of a healthy environment increased over time. As the stock of
knowledge grew, and as printing and literacy made the diffusion of knowledge possible in new
ways, the economic benefits of a lower disease burden may have been amplified. The advantages
of certain geographic variables could have remained latent, until activated by other cultural
changes that increased the value of cognitive health and human capital. But given the established
importance of infectious diseases in more recent times, and the geographic variation in the disease
burden, we suggest that disease ecology should be more prominently considered in the literature
on economic development before the 20th century.
Finally, we close by re-emphasizing that our study is limited to exploring the effects of a
single disease, malaria, in a single continent, Europe. Variation in the prevalence of malaria should
not be conflated with variation in the mortality regime as a whole. At the same time, within Europe,
the geography of malaria probably does capture a great deal of the overall variation in the
infectious disease burden in pre-transition societies, both because malaria itself was such a
powerful influence and because it can proxy for other diseases which are also responsive to
geographic parameters. Prima facie the mechanisms we have explored likely operated on a global
scale; they might have played a part in the diverging trajectories of long-run development between
33
the temperate latitudes and the tropics – and even perhaps between western and eastern Eurasia.
China and South Asia suffered a heavy burden of malaria as well as other enormously important
parasites that were absent in the west for ecological reasons, such as schistosomiasis (snail fever,
which thrived in regions with rice cultivation). In short, geographic variation in the infectious
disease burden can be explored as a neglected but ultimate exogenous factor that might throw light
on the great problem of economic divergence.
34
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1 : M a la ri a B u rd en
(A g u e R a te s)
a n d A g e a t F ir st
M a rr ia g e (I ta li a n R eg io n a l L ev el
D a ta )
2 : M a la ri a B u rd en
, A g ri cu
lt u ra l M o d e a n d A g e a t F ir st
M a rr ia g e (I ta li a n V il la g e L ev el
D a ta )
E x te
3 : M a la ri a B u rd en
(A g u e R a te s)
a n d A g e a t F ir st
M a rr ia g e (E
n g li sh
16 00
-1 83
7 4 9
T a b le
4 : P a st o ra l P ro d u ct io n , A g u e R a te s,
a n d A g e a t F ir st
M a rr ia g e (P
a ri sh -L ev el )
16 00
-1 83
7 4 9
45
Table 5: Disease Environment (Wetness Index) and Age at First Marriage among Parishes in Kent
Average Minimum (1) (2) (3) (4)
Disease Environment -0.155** -0.168** -0.135* -0.140* (Wetness Index) (0.072) (0.076) (0.075) (0.076)
Standard Deviation of Disease Environment 0.129 -0.029 (0.151) (0.145)
1 *** p < 0.01, ** p < 0.05, * p < 0.01. Malaria burden is measured as wetness index discussed in the text.
Table 6: Disease Environment (Wetness Index) and Age at First Marriage among neighboring parishes in Kent
Average Minimum (1) (2) (3) (4)
Panel A: Full Sample Disease Environment -0.107*** -0.193*** -0.182*** -0.168*** (Wetness Index) (0.020) (0.023) (0.022) (0.023)
Standard Deviatio