Portuguese demography and
economic growth, 1500-18501
Nuno Palma, LSE and Queen Mary University of London
Jaime Reis, Universidade de Lisboa (ICS)
Prepared for the “Accounting for the Great Divergence” conference,
The University of Warwick in Venice, 22-24 May 2014
Abstract
We draw on a new database for prices, wages, and rents to construct an annual series
for Portugal’s real wages, and both agricultural and manufacturing productivities, for
both the early modern period and the first half of the nineteenth century. We further
aggregate dispersed demographic information to construct the first annual series for
the Portuguese population during this period. This in turn permits a demand-side
calculation of yearly real per capita GDP. There is a highly persistent upward trend of
per capita income, starting around the middle of the sixteenth century, accelerating
after 1700 and peaking around 1755, by which time per capita incomes were more than
50% above “subsistence”. Around the mid-eighteenth century, Portuguese per capita
GDP was about as high as that of Britain and the Netherlands, and higher than that of
France, Spain, Germany and Sweden. In fact, in Portugal during most of the early
modern period population did not catch up with intensive economic growth resulting
from increasing colonial opportunities and the introduction of highly productive crops
from the Columbian exchange, in particular, maize. Moreover, the colonial empire
allowed migration opportunities which raised land-labor ratios and alleviated
Malthusian population pressure at home. As the second half of the eighteenth century
advanced, however, these sources of intensive growth were exhausted and the decline
of the Brazil gold production and remittances initiated a phase of economic decline,
which may also have been accompanied by growth-discouraging institutional change.
By the late eighteenth century almost all per capita GDP and real wage gains had been
lost, and these variables were considerably lower than in Britain, though they were not
yet at a comparatively low level by continental standards. In the absence of new and
fortuitous influences, however, convergence towards a stagnant steady-state resumed,
eventually leaving Portugal as one of the most backward economies of Europe
precisely at the dawn of the era of modern economic growth.
Keywords: Early modern Portugal, Standards of Living Debate, The Early Modern
Little Divergence, Malthusian Model
JEL codes: N13, O52
We are grateful to Steve Broadberry, António C. Henriques and Joan R. Rosés for discussions and
advice, and to Hermínia Barbosa, Cristina Giesteira and Carlota Santos for providing us access to their
parish registers data. We also thank Paulo Paixão for dedicated research assistance in collecting much
of the demographic data, the colleagues of the Prices, Wages and Rents in Portugal 1500-1910 project,
and FCT for financial support.
1
1
1. Introduction
The effort to understand the historical origins of modern economic growth necessarily
involves paying attention to the European periphery. Understanding what “went
wrong” in the periphery is a crucial part in the effort to make sense out of what “went
right” in Northern Europe. Knowing how prices, rents, income and population evolved
in the laggard countries is a critical piece of the puzzle, as it provides a source of
variation in the data which permits comparison with the modernizing economies. The
behavior of such major macroeconomic variables is well known for England (Allen
2001, O’Rourke and Williamson 2005, Broadberry et at 2011), Holland (van Zanden
and Leeuwen 2012), Germany (Pfister 2011, Pfister, Riedel and Uebele 2012), Sweden
(Edvinsson 2009, 2013a, b), Italy (2011, 2013), and Spain (Rosés, et al 2007, ÁlvarezNogal and Escosura 2007, 2013). In this study, we consider the case of Portugal, a
case-study which has been absent from the literature.
While much empirical research in economic growth has leaned on Angus Maddison’s
per capita GDP estimates (Maddison, 2001, 2003), a good deal of recent research has
challenged his methods and conclusions, especially for the early modern period (Allen
2001).2 While Maddison claims that European per capita incomes showed a steady
upward trend during this period, much modern research suggests otherwise, with the
exceptional cases of Holland and England. The real wage evidence we put forward in
this paper for the case of Portugal lends support to the modern view: to the extent that
real wages are representative of overall income, we find strong evidence against
Maddison’s claim that Portugal’s per capita income consistently rose between 1500
and 1820 (Maddison 2006, p.642). Evidence based on real wages suggests long run
decline. However, real wages based on the day wage may not be the end of the story.
In the English case, for instance, while real wages evidence present a picture of
stagnation for the entire early modern period (Allen 2001, Clark 2005, 2010), but this
view is not confirmed by output-side GDP estimates, which show substantial intensive
growth (Broadberry et al 2011).
In this paper we present the first annual estimates of Portugal’s early modern GDP
per capita, real wages and population. In figure 1, we show indices for these variables
for 1500-1850. Portugal’s early growth was characterized by several distinctive
phases. The first was decline from high levels of standard of living at the beginning of
the sixteenth century, which lasted until the mid-sixteenth century. This was followed
by extensive and intensive growth from 1530 to about 1645. From then until the early
eighteenth century there is slight decline. During this period both per capita GDP and
real wages did not fall despite fast population growth, and in fact rose during
prolonged periods, in what does not appear to be a Malthusian situation. Then during
the half a century which ensued after 1700 there was strong intensive growth, and as a
result Portugal’s 1750 per capita GDP came to be as high as that of England or the
Netherlands. In fact for the 20-year period between 1703 and 1723, Portugal’s per
capita income grew an average of 1.4% per year – a remarkable rate for a pre-modern
economy. From the mid-century income undergoes slow but persistent decline which
is intensified after 1790, when Portugal initiates a fast decline, which continued well
into the nineteenth century. This decline was initiated several decades before two of
the main causes for Portugal’s decline which have been proposed before – the
Napoleonic invasions and the loss of the Brazilian empire, though it may have been
See the Maddision project website for a recent effort to publish updated panel estimates in the tradition
of Maddision.
2
2
accelerated by them. The picture of the long-term evolution of the Portuguese
economy we have just presented is based on GDP per capita as a measure of income.
This evidence is not always consistent with the simpler picture which would emerge if
one looked at real wages only (figure 1)3.
FIGURE 1
Over the very long term the evidence is broadly consistent with, or observationally
equivalent to, the predictions of the Malthusian model4. Portugal’s income, whether
measured by the real wage or GDP per capita, did tend to converge to a long run
steady state5. However, as figure 1 makes clear, Portugal did not adhere to a canonical
version of the Malthusian model in which population responds to growth
opportunities fast enough that incomes never deviate for long from a Malthusian
subsistence level; in Portugal, Ricardo’s “iron law of wages” breaks down for
prolonged periods. The persistent upward deviation in the form of a slow upward
trend of income which starts around 1550 and, despite ups and downs, lasts until 1755,
means that by that time income per person was well above “subsistence”. During a
period lasting more than two centuries and a half, population did not catch up with the
intensive economic growth resulting from increasing colonial opportunities, the
introduction of highly productive crops from the Columbian exchange, the opening of
England as a new market for Portuguese wine and, possibly, growth-promoting
institutional change. The colonial empire also allowed migration opportunities which
alleviated Malthusian population pressure at home. Early modern Portugal hence
emerges as an instance of pre-modern intensive growth.6
After the mid-eighteenth century, however, the above-mentioned sources of growth
were exhausted and the decline of the Brazil gold rush and gold remittances initiated a
phase of decline. By the late eighteenth century all per capita GDP and real wage gains
had been lost, and the decline was intensified. In the absence of any new and fortuitous
influences, population continued growing and convergence towards a stagnant steadystate resumed, eventually leaving Portugal as one of the most backward economies of
Europe precisely at the dawn of the era of modern economic growth.7 In this paper, we
offer both a thorough growth accounting exercise regarding Portugal’s early modern
economy and an explanation of the proximate causes underlying both the observed
process of economic growth and decline.
The choice of 1530 as the baseline year is due to the fact that this is the first year for which we have
annual variation in population, and hence the year our econometrics analysis below starts. It is also
convenient for comparison with the long term evolution of the Spanish economy, since Álvarez-Nogal
and Prados de la Escosura (2007) also use 1530 as the base for the index.
4 We favor an observational equivalence interpretation as it is neutral over whether the equilibrating
forces are primarily Malthusian or institutional in nature.
5 If we interpret the median of the distribution as the Malthusian “subsistence” level, it corresponds to a
level in which one “skilled” (alternatively, unskilled) family of two adults and two children (3.15 adultequivalent individuals) is able to afford, depending on the exact year, between 3.6 (1705) and 4.5 (1671)
“respectability” baskets (alternatively, 1.3 (1794 or 1818) to 1.4 (1629) for the unskilled. (This last one
was, of course, instead forced to choose a “barebones” basket in order to sustain a family; that basket
suggests he would be able to purchase between 2.4 (1794) and 3.3 (1671).
6 Jones (1988), Goldstone (2002). See Mokyr and Voth (2008) for the interpretation of the Malthusian
model as an “equilibrating” force, in which the persistent deviations from the stagnation equilibrium can
occur in the presence of sequential sources of growth.
7 Bairoch (1976) believed that Portugal was one of Europe’s five richest countries as late as 1800. See
Reis (2000) for a comparative study of Europe’s periphery which demonstrates that as late as 1850,
Portugal’s income was comparable to that of the Scandinavian countries.
3
3
2. Data and analytic tools
2.1 Data
We use long-term annual series for the following variables: population, land, wages,
agricultural and manufacturing prices, and land rents.8 For wage and price sources, we
restrict ourselves to Lisbon and its rural surroundings. We thus adopt the principle of
the “national representativity” of the data from the country’s principal city, following
Allen (2001).9 In common with other projects, the data for this one come chiefly from
the accounts of religious foundations, charitable institutions, royal palaces and
municipalities. The data refers to market transactions. In order to homogenize results,
all monetary values have been converted into grams of silver and prices have been
normalized to correspond to metric units. Unfortunately, due to the lack of more
detailed sources, we have had to interpolate part of the data. In the following table, we
show which percentage of the yearly variation of the data is covered by our sources.
TABLE 1
We have chosen to use the daily unskilled wage for male adult workers as the measure
of the labor factor. In this way we capture only the value of raw labor of a well
identified unit of services and thereby avoid distortions which might be caused by
variation in the unidentified presence of human capital in the labor stock. These wages
refer always to employment in either agriculture or the building industry and to
situations in which non-monetary complementary remunerations were entirely
absent.10
As far as prices are concerned, for the agricultural sector we have selected prices
corresponding to the principal articles of consumption and production. Consumables
include wheat and maize bread, meat, olive oil, wine, eggs and hens, all of which form
part of the widely used standard consumption basket of the early modern period
literature. On the production side we take the prices of charcoal, linen cloth, soap and
candles. We add prices for nails and lime, as well as paper and ink, in order to endow
the set with a greater sensitivity to, respectively, the prices of capital and luxury
goods.
Land rents have attracted little attention from the economic historians of this period in
Portugal. At this time most land in use was not directly cultivated by its lords. Rents
Quantitative macro-history in Portugal has a long and respectable tradition going back to the 1950s,
which has yielded a considerable and mostly published stock of price and wage statistics, though not
rents. However, the heterogeneity of procedures and periods covered followed by different scholars
make it difficult to convert into a consistent series liable to be used for long run macroeconomic
analysis. Hence the development of a major project constructing a database of prices, wages and rents
for the period 1300-1910, which supports the present study: http://pwr-portugal.ics.ul.pt/
9 This is further justified by the following circumstances. Portugal is a small country (89,000 square km)
and Lisbon is located centrally. It had reasonable communications by sea and river with most of the
country’s regions and their markets. By European standards the integration of markets for basic food
products at least in the 18th century was already fairly high (Federico 2010, Justino 1988, Santos 1998).
In the case of labour, restrictions on its mobility were non-existent and qualitative evidence regarding
internal labour migration during this period is plentiful. This too suggests a well-integrated market
(Silbert 1986, Dias 1998, Reis 2005b.) Finally, while Lisbon was likely to have transportation cost
premium over other areas, there were only limited improvements of transportation during this period,
so such premium is likely to have remained approximately constant.
10 The skill premium, as measured by the difference between the skilled and the unskilled wage, shows a
roughly constant evolution during this period. See Costa, Palma and Reis (2014) for details.
8
4
are important for the present narrative as indicators of the value of the services
produced by land, as well as its relative scarcity. Possibly a little less than half of all
agricultural land was under the regime of commercial tenancy, with leases typically
running from three to ten years (Monteiro 2005). The remainder was held under long
term or perpetual enfitheutic contracts, whereby the lord received a fixed fee and the
tenant enjoyed an assignable right to the exclusive enjoyment of all the fruits of the
land (Costa, Lains and Munch 2011; Fonseca and Reis 2011).11 We assume that the
rent of the first category of contracts provides a reliable indication of the market value
of all agricultural land. The argument in support of this is that tenant turnover in the
second, more rigid category, although lower was not insignificant given the high
probability of death of tenants and their successors. Enfiteutic rents were thus more
responsive to market forces than might be expected and closer to commercial ones.
Data for land rents are not abundant. In this paper, we rely on the aggregate rent of a
stable set of thirty two estates owned and regularly leased by a charitable institution
in the region of Alentejo from 1595 to 1850 (Santos 2003). We retropolate this index
back to 1565 using a similar set of properties of a charitable hospital in Lisbon and for
the remainder of the century we use the land rent index for Spain for the same period
published by Alvarez-Nogal and Prados de la Escosura (2013).12
As a means to test some of the implications of the Malthusian model, we have also
constructed the first annual series for Portugal’s population during this period.13 The
evidence underlying the estimation of our annual population data presents several
advantages over the analogous exercise for England by Wrigley and Schofield
(1989)14. We present these as well as all the details of the exercise in the appendix. The
main goal is to arrive at an annual aggregate figure, but in a companion paper, we
further analyze the underlying regional patterns (Palma and Reis 2014).
Finally, measuring agricultural land for any given economy during the Early Modern
period is a considerable challenge, so it is hardly surprising that a common solution
employed in the economic history literature is to assume that despite changes over the
few last hundreds of years, the number of hectares in some sort of use has not changed
much up to the present day (despite the fact that with varying degrees of investment,
quality may have changed substantially). In this perspective, what matters is the
potential resource base, upgrades or changes of usage being assigned in the model to
changes in “technology”. In the case of Britain, this procedure has been defended on
11While
the first of these arrangements was employed mostly for larger units of production, the latter
corresponded to small or minuscule farms.
12 The main Portuguese series, after 1595, is quite consistent, in grams of silver, with the Spanish one as
well as the other available Portuguese series (Salvado 2009).
13 Simply interpolating between the few population stock numbers which we have would produce
insufficient variation in the data for the purpose of subsequent econometric analysis.
14 While Wrigley and Schofield only considered Anglican parishes– hence the gradual growth of
nonconformity presenting a threat to the randomness of their sample – Portugal’s political and religious
uniformity means that this type of sample selection is not an issue. Second, again in contrast to the case
of England several census-type point estimates for the stock (numbers at risk) are available before the
nineteenth century, hence eliminating the need for back projection methods altogether. Third, our
sample’s coverage is such that are also able to present regional estimates (for details, see Palma and Reis
2014). Fourth, in the English case, the lack of population numbers for the first half of the sixteenth
century has given rise to controversy (Hatcher 2003). Our figures do represent observed annual
variation from 1530 onwards (no parish records are available before this date, to our knowledge),
although it is unavoidable that the earlier periods are based on a smaller set of parishes for which data
happens to be available as compared with latter periods which have higher empirical support.
5
the grounds that for practical purposes all land had been put to use by 1066
(Williamson and O’Rourke 2005). The same rule of thumb has been adopted for
Europe as a whole, from 1300 to 1800 by Allen (2003). We follow this approach for
Portugal and assume that by 1500 all usable land was under some form of husbandry,
even if not necessarily the most intensive. This would include rough grazing and
prolonged fallows. We therefore assume the stock of land was equal to the area of
“agricultural land” measured by the UN-FAO in the 1950s, namely 4.13 million
hectares.15
2.2. Construction of variables
We now briefly describe the construction of the variables which describe the long-run
evolution of the Portuguese economy. They are shown in figure 2.
FIGURE 2
The real wage. The early modern economic history literature has embraced the real
wage as a useful measure for international and intertemporal assessments of living
standards (Allen 2003; Pfister et al. 2012). For the conversion of nominal into real
wages, we employ the procedure outlined by Allen (2001). This involves deflating the
nominal silver wage by a CPI defined by the silver price of a basket with a fixed
composition of goods and assumed to be reasonable in terms of the consumption needs
of a representative family. For want of a better solution, we have used a modified
version of the Strasbourg “pre-modern” working class basket of 1745-5416 (Allen
2001).
GDP per capita. GDP per capita could be a better measure of overall well-being than
the real wage, though it has the disadvantage of not reflecting as well the welfare of
the majority of the population if the wealth distribution is very skewed.17 Here we use
a modified version of Portugal’s GDP per capita which is estimated from the demand
side in Reis (2013). The modification which we make to this estimate corresponds to
an upwards adjustment concerning labor supply. The reason this adjustment is
advisable is that it is likely that over this period days and market participation
increased, as they did elsewhere in Europe. If an industrious revolution occurred –
possibly in the countryside as a response to the more-labor intensive agriculture
“Agricultural land” is defined as the sum of cropped land, meadow land, pasture and rough grazing. In
pre-industrial times this would have included a sizeable portion of fallow land in crop rotations. We
realize clearings can occur as an endogenous response to land scarcity, but for practical purposes what is
important is that all else constant they should lead to reduced agricultural productivity. The only
available roughly contemporary evidence comes in a calculation made in 1875 by the geographer
Gerardo Pery, who assessed the country’s total “productive area” – also excluding forests, a concept
close to “agricultural land” – as being 4.34 million hectares (Fonseca 2005).
16 In the case of foodstuffs, we have had to make the necessary adaptations required by geographic
differences, taking care that caloric and protein standards are not significantly altered. Beer has been
replaced by wine, butter by olive oil, cheese by hens. Over the course of the three centuries of the study,
there was a gradual shift from wheat to maize bread until the latter eventually became dominant.
During the sixteenth century, maize was practically absent from the national diet but by the early 1800s
probably represented more than 50% of grain eaten by humans (Reis 2000, Lains and Sousa 1998). This
has been taken into account by altering the annual grain content of the CPI in accordance to the
information on production shares based on tithes (Oliveira1990, 2002), To complete the basket, we have
added non-edible items, namely candles, linen cloth, charcoal and lamp oil, replaced here by olive oil
which was used for purposes of lightning.
17 For the reasons which can justify divergence between the real wage and GDP per capita, see Angeles
(2008).
15
6
required by the gradual introduction of maize (Ribeiro 1986), and in the urban areas in
response to the opportunities offered by the Empire (Costa, Palma and Reis 2014) –
then per capita GDP will be higher than that calculated under the assumption of fixed
labor supply.
There are two moments for which supply-side GDP estimates are available, and they
are conveniently located near or at the two extremities of our period: 1515 (Godinho
1968) and 1850 (Reis 2000). For each of these moments, we convert total output into
tons of silver. This quantity can then be compared with the analogous quantity
calculated from the demand side. Using an initial number of 168 days worked (as in
Álvarez-Nogal and Prados de la Escosura 2013), we conclude that the ratio of GDP
calculated from the demand side to that calculated from the output side is 1.09. The
same exercise for 1850, however, leads to a ratio of 1.47. We conclude that over this
period average days worked must have increased to 248, which is the number needed
to conciliate GDP estimated from the demand and supply sides.18
Finally, in table 2 we report a comparative study of the per capita GDP of Portugal, in
the spirit of that offered in Álvarez-Nogal and Prados de la Escosura (2013). One
result of this exercise may be surprising to some: in 1750, Portugal’s per capita GDP
was higher than that of France, Spain, Germany and Sweden and at around the same
level as Britain and the Netherlands. However, as the 1700 and earlier benchmarks
show, this was mainly the result of remarkable – and idiosyncratic – growth in the
previous half a century.
TABLE 2
Population. We combine information provided by several sources, in particular census
and census-type information about stocks and a wealth of parish-level data about flows
to form a detailed estimate of the annual evolution of the early modern period and
nineteenth century Portuguese population up to the first modern census, taken in
1864. See section A1 of the appendix for details.
The land-population ratio. The land-labor ratio is a critical element on which any
evaluation of the Malthusian model must rest since it brings out the shift over time in
the relation, in physical terms, between these two fundamental factors of production.
However, since data for the economy-wide labor supply is usually not available, the
land-labor ratio is often instead presented as a ratio between agricultural land and
population (Galor and Weil 2000, O’Rourke and Williamson 2005)19.
The corresponding increase, assuming proportionality, for each labor type is as follows: 120 to 177 for
agricultural workers, and 180 to 265 days for semi-skilled laborers. For urban skilled laborers we have
truncated the increase at 320 days (from an initial level of 250). Part of the increase may be due to a
combination of an increase in labor supply at both the intensive level (the same cohorts of people work
more days) and at the extensive level (more people from the same cohorts substitute leisure for
agricultural production or enter the rural skilled or urban labor markets). In the appendix we report the
unadjusted estimates, which also show intensive growth, though weaker.
19 Despite the land-population ratio being implicitly identified with the land-labor ratio as a standard
practice in the literature, however, this is less than ideal since as far as agricultural productivity and
labor market tightness are concerned, it is labor, not population, which matter. For instance, wages
should respond with a lag to a strong burst of population growth, since regardless of the wage level it
must take a few years until the new-born reach the age at which they start working the fields or enter
the labor market.
18
7
The wage-rent ratio. This measures relative factor scarcity and according to some
interpretations its trend provides a signal of the presence of Malthusian forces in the
economy (O’Rourke and Williamson 2005, Rosés et al 2007). It is obtained by dividing
the nominal unskilled wage by the nominal value of the rent index. An alternative,
suggested by Williamson (2002, p. 255), is to take the ratio of the nominal (day) wage
to nominal GDP, but this is only valid when divergences are due to changes in the
wealth distribution, as opposed to changes in labor supply or relative prices (Angeles
2008).
Ratio of agricultural to manufacturing prices (PAPM). The inter-sectorial terms of trade
in a simple economy such as this are measured by the ratio between the prices of
agricultural and manufactured goods. In a Malthusian world, such an index could
highlight another aspect of the economic response to population trends. If the latter
rose, this would cause a relative scarcity of products derived from the fixed factor of
production, i.e. food, and this would be revealed by a rise in this ratio. The index is
calculated on the basis of price indices for these two categories of commodities. In this
calculation, for manufactures, we resort an index comprising linen cloth, candles,
charcoal, soap, lime, nails, writing ink and paper. In the absence of their shares in total
manufacturing output, our index is obtained by means of the geometric mean of these
prices. In the case of foodstuffs, we take the prices which reflect the four main
subgroups of commodities in this sector, namely grain (represented by maize and
wheat), wine, animal products (represented by meat) and olive oil. These are
aggregated using the mean weights of two estimates of the composition of agricultural
output, one for ca. 1515 (Godinho 1967), and the other for ca. 1850 (Reis 2000).
Total factor productivity in manufacturing (TFPMAN). The lack of suitable data
precludes the possibility of obtaining an index which covers a wide range of products.
It also obliges us to use the “dual” method for estimating manufacturing TFP (Antràs
and Voth 2003). In a similar situation, Allen (2003) estimated the TFP of one
important subsector alone – woollens – and considered this as representative of
manufacturing as a whole. The index was obtained by calculating the geometric mean
of the price of the raw material and the skilled wage rate and then dividing this by a
cloth price series. Likewise, we have adopted a cheap (and therefore not imported)
variety of linen cloth, one of the principal textiles manufactured during the period in
Portugal. And for the input side we have calculated the geometric mean of the skilled
wage and of the price of flax. This is divided by the market value of linen cloth to get
the TFP index.
Total factor productivity in agriculture (TFPAG). Analogous problems are met with
agricultural TFP but the solution is slightly different. We continue to make use of the
“dual” approach. The price of aggregate of output is the same as was used to estimate
PAPM. Input prices are given by the weighted geometric mean of wages and rents,
using the weights, respectively 65 and 35%, employed by Rosés et al. (2007), for Early
Modern Spain.20
We did not follow Allen’s (2003) approach to the calculation of agricultural TFP because this would
have required annual data for agricultural population and output, which we are unable to estimate with
any degree of confidence.
20
8
3. Portugal’s early modern economy
The Portuguese early modern economy was prevalently based on agriculture, with
productive specialization patterns similar to those of the rest of Southern Europe. The
scant available evidence suggests that at the start of the fifteen hundreds the largest
agricultural sub-sector was animal husbandry, followed closely by grain and, a long
way behind, by wine and olive oil. This structure did not change much thereafter. The
change which did occur consisted of the gradual replacement of pastoral by arable
production, the rise of wine and oil production and in grain, and an important shift
from wheat, rye and millets to American corn (maize).21 Industry consisted of textiles
(woollens and linen), leather, construction and all the other necessities of ordinary
daily life. Luxuries and manufactured exports occupied secondary positions, though
they began to enjoy an increasing role during the eighteenth century. In the tertiary
sector, apart from the normal contribution of transport, trade, administration and
shipping in such economies, it is worth noting the significant element of colonially
oriented activity.22
The central role of the agricultural sector in Portugal’s pre-modern economy justifies
some detailed analysis. Several circumstances shaped its evolution. The first was land
clearance with the view to expanding arable production, which occurred with varying
degrees of intensity over time. This accompanied the gradual shift from animal to
arable husbandry, as happened elsewhere throughout most of Europe (though England
and Holland were exceptions in this regard). An important force was the so-called
“maize revolution”, which spread throughout the northern half of Portugal and
significantly displaced other grains. Finally, there was the development and
enlargement of the commercial wine sector, from the late 17th century but mainly in
the 18th century. All of these processes were gradual, and only one – the introduction
and spread of maize, which might be described as a technological shock – was a total
novelty (Costa, Lains and Munch 2011). Unfortunately, too little is known about them.
In particular, their respective chronologies, impacts and rates of diffusion remain
obscure and tracking these movements with precision is hard. All of them nevertheless
contributed to raising agricultural TFP. We now consider each in turn.
Land clearances had long been the classic way to intensify the use of this production
factor. They were certainly important before 1500 (Barata and Henriques 2011) and
may have continued to be so during the fifteen hundreds (Gil 1965). Throughout the
next two centuries, however, there was an unavoidable decline in the amount of underutilized land available for putting under the plough. Data from the accounts of several
monasteries suggest that new leases for clearing land were becoming less common,
with a clear drop from early 17th century onward.23 Further, in all likelihood they
Costa, Lains and Munch (2011), Serrão (2005 and 2010), Magalhães (2010). We note in passing that
the shift from animal to grain production corresponds to a shift from capital intensive to labor intensive
mode of production which is suggestive of increased population pressure on the land.
22 For a revision of the contribution of the colonial empire to the domestic economy, see Costa, Palma
and Reis (2014).
23 Our view is that by the 16th century, there was little cultivable land which had never had some
economic use or another, no matter how tenuous. Given the pastoral vocation of much of the national
territory, it seems likely that a lot of the clearance which occurred was in order to convert rough
pastures into arable. It therefore represented an intensification of land use, but not an increase in
“agricultural land”. Note in this article we refer to land as a “raw” land input – this is what to a firstorder approximation remained fixed throughout this period, while land quality varied endogenously
through additional investments such as the construction of dykes. These would have, all else constant,
raised agricultural TFP.
21
9
comprised land which was of increasingly poor quality (Oliveira 1979; Maia 1991;
Neto 1997; Campos 1989; Silva 1994; and Amorim 1994).
The introduction to Portugal, in the early 16th century, of American corn (zea maïs)
and its subsequent wide diffusion has been glorified by historians as a “revolution”
though in fact it took until the first half of the 19th century for its diffusion to be
completed24. Once a garden plant, the advantages of converting it into a field plant
were manifold. Its yields were considerably higher than for other breadstuffs (wheat
and rye), its caloric content per kilogram was 30% greater, and its unit costs of
production were lower. In addition, it required more labor per hectare, was less
sensitive to climate fluctuation and could be used for many different purposes.
Adopting maize involved a learning curve for Portuguese peasants, but not a steep one
since they were already familiar with millet. It also called for a reorganization of the
field system, the creation of micro irrigation facilities and therefore a certain amount of
investment, both physical and in terms of coordination mechanisms.
Unfortunately, the timing of the spread of this crop is not well known. It is clear,
nevertheless, that around 1600 its share in total grain output was small and that by
around 1800 it had reached its long term ceiling, attaining 60% of national grain
production towards the middle of the nineteenth century (Lains and Sousa 1998). In
between these benchmarks, evidence from local histories points to a fairly rapid spread
all through the 1600s and the first half of the 1700s (Oliveira 1990, 2002). Not
surprisingly, a national survey of parishes in 1756, the memórias paroquiais, shows
qualitative but convincing evidence that maize had by then come to play a leading role
in a considerable part of the country, in particular where conditions were best suited
for it (Entre-Douro-e Minho, Beira, and parts of Estremadura). Although this spread
continued thereafter, it is likely to have been slowing down, as good land with a
potential for irrigation became scarce. The beneficial effects of maize on agricultural
production were probably losing steam.
Finally, the growing of vine and the production of wine, although hardly a novelty of
the early modern era, developed remarkably during this time and became the second
pillar of the sustained improvement in agricultural production.25 The timing of its
expansion is well known and goes from the late 17th century to its heyday in the 1750s
and subsequent deceleration down to circa 1800. Total output doubled between 1700
and 1800 (Martins 1998). There were two drivers behind this change. One was
sustained market expansion, both on the national and international fronts. The latter
was stimulated by a change in military and diplomatic relations which gave
Portuguese wine a signal advantage in the valuable English market throughout the
1700s (Cardoso et al. 2003). The other was technical progress on the ground, which
occurred both in grape-growing and in the processing of wine. As in the case of maize,
the conjunction of circumstances meant socio-economic deep adjustments in the
regions where the process made itself felt. It required profound changes in the field
system, new equipment and production techniques, and substantial investment. It led
See Almeida (1992) and Ribeiro (1986). For a broader view on this plant, see Langer (1975) and
Messer (2002). There is an obvious parallel with the potato but two contrasts are important. The potato
revolution came later and was shorter and maize never had to face a natural catastrophe. It remained
one of Portugal’s principal breadstuff until the present. Nunn and Qian (2011) give reasons why in
general people resisted the widespread adoptions of the potato until late. In any case historical evidence
suggests it did not play a major role in Portugal until the nineteenth century.
25 Despite the growth export wines during the eighteenth century, most production was for internal
consumption.
24
10
to pronounced productive cash-crop specialization – some districts in north Portugal
became practically mono-cultural – and required the achievement of higher levels of
marketing expertise (Serrão 2009).
4. Econometric analysis
A quick glance at GDP per capita or the real wage (figures 1) shows that over the very
long run, evidence appears to be consistent with, or observationally equivalent to, the
basic prediction of the Malthusian model: income tends to converge to a stagnant
“subsistence” level. Two issues prevent an immediate acceptance of the Malthusian
picture, however. First, the simple observation of stagnation in itself would not prove
that the mechanism pulling the wage back to a steady state following a short run
deviation is the equilibrium agricultural and labor market responses to series of
positive and preventive checks on mortality and fertility of the sort Malthus identified.
It is a priori well possible, for instance, that the forces at work are of a separate
political economy type. Second, as observed in the introduction, there was indeed at
least a long period of significant deviation from the predictions of the standard
Malthusian model.
In order to dig deeper into the determinants of the long run evolution of the income,
we need to take a look at co-variation with a host of other factors, in addition to
population alone. In some models, the land-labor ratio and the productivity of the
agricultural sector prices are used as exogenous variables in modelling income, the
wage-rent ratio, and the ratio of agricultural to manufacturing prices, interpreted as
dependent variables (O’Rourke and Williamson 2005, Rosés et al 2007). Even when
explicitly recognizing that the endogenous fertility decisions of families mean there is
a (lagged) negative feedback between income and the land-labor ratio, no single model
or identification strategy is accepted in the literature as the best way to test the
adequacy of the Malthusian model to any given period or region. Crafts and Mills
(2009), Nicolini (2007), Kelly (2007) and Klemp (2011) have all used different models
and identification assumptions. Our approach here is to initially estimate parameters
which represent reduced form statistical associations which are useful to learn from,
but without attributing a causal interpretation to the results. Then, we consider
sources of exogenous variation to the otherwise endogenous variables (instruments),
namely climatic and disaster-related variables, in order to reach causal estimates.
We start with the model of O’Rourke and Williamson (2005) and Rosés et al (2007),
but we make the following changes. First, we consider GDP per capita as the
dependent variable (table 3)26. Second, we allow for the possibility of autocorrelation
by using the Newey-West estimator27. Finally, given the likely possibility that during
All data (in natural logs) have been tested for stationarity and structural breaks, which can be rejected
at the usual levels of significance. (This is true for per capita GDP even if the time period is restricted to
1530 to 1755.) The exception is, as visual inspection of the population curve might suggest, the landpopulation ratio, for which a unit root with drift cannot be rejected at 5% and a unit root with trend
cannot be rejected at 1% significance. The clear downward trend of the data suggests the latter is the
correct choice, and for this reason we include a deterministic trend as a control in the regression. See the
appendix for details.
27 Standard errors are Newey-West corrected. This estimator requires choosing a truncation parameter
which corresponds to the number of lags up to which the error terms are allowed to be correlated in the
variance matrix. Several rules for the choice of these lags have been proposed in the econometrics
literature. We use five lags in the baseline estimates, which corresponds to the most conservative choice.
(The choice of a smaller number does not change our conclusions in any significant manner). See section
A3 of the appendix for details on the choice of the truncation parameter.
26
11
the eighteenth century real per capita gold imports significantly affected incomes, we
include them as a control28.
TABLE 3 HERE
At first sight, the sign of the marginal effect of the land-population ratio seems odd: for
the full sample, a 10% increase in the land-population ratio appears as associated with
an approximate 2% decline in the per capita GDP, all else constant, an effect which is
statistically significant. We can see, however, in columns (3) and (4) that this effect is a
result of the forces of the 1530-1755 period.29
The estimates discussed so far represent statistical associations, but they do not reflect
causal relationships. Further, the Malthus model suggests that the land-population
ratio is endogenous.30 In general one solution to the problem of endogeneity is to use
instrumental variables, separately for an income and population equations. However,
because we are interested in the joint evolution of income and population, if we use
2SLS equation by equation the error terms will implicitly be assumed orthogonal to
each other. Instead, we engage in three-stage estimation for systems of simultaneous
equations (3SLS). This method explicitly takes into account that income and the
population are endogenous to each other, and allows for correlation in the disturbances
of the system’s equations. We use two groups of instruments. First, we use several
climatic variables, which include both temperatures and rainfall data.31 Second, we
build two “calamity” indexes, which include natural disasters such as earthquakes,
fires, and wars.32 Our source for the construction of this variable is the compilation of
such events published in the “PWR-Portugal site” (Prices, Wages and Rents 2013).33
The source is Morineau (1986). An alternative series, which we do not use because it only starts in
1720, is provided by Costa, Rocha and Sousa (2013), who also provide a detailed comparison between
both series after 1720. For our purposes it is sufficient to notice that while quantities in several
individual years differ, the general trend is the same. Since in many years in the sample the quantity of
gold imports equals zero, we have simply substituted all zeroes by the smallest positive quantity
observed divided by 100. This seems to us a better solution than the more common solution of adding 1
to all the data, because that solution is unit-dependent; doing so, however, does not change our basic
results. Finally, it should be said that only the Brazil gold is considered here, though earlier Portugal
imported (much smaller) quantities of gold from the Guinea coast. Further, notice that including
contemporary gold imports only accounts for impact effects but since these imports were highly
persistent, it can also give a sense of the relative global magnitude for each period.
29 In fact, after the mid-eighteenth century the expected conditional sign does turn up – but even for this
period, it is only significant as long as the year trend is not included: compare columns (5) and (6). This
could be due to the smaller sample size for this period (95 observations only), to the actual absence of a
conditional statistical relationship for this period, or to the identification problems which the next
paragraphs will address.
30 There are two pillars to the canonical Malthusian model. The first says diminishing marginal returns
from land lead to lower incomes when the population is higher, for all else constant. The second says
that lower levels of income leading to lower population levels both now and in the future via more
deaths (positive check) an endogenous fertility channel (preventive check). If population falls or less
people are born, agricultural production and the labor market will eventually also be affected. This twopillar structure of the Malthus model means that, as in estimation of supply and demand curves for
which the observed price is an equilibrium variable, the estimation method to be used needs to take this
simultaneity into account.
31 Guiot and Corona (2010) have reconstructed gridded annual air temperature for a panel of European
countries in the last 1400 years using paleoclimatology proxies: tree-rings, pollen assemblages and ice
cores. We also use independent reconstructions of precipitation (Pauling et al 2006).
32 All wars fought in the continental territory were caused by exogenous outside political events (such
as the war of the Spanish succession or the Napoleonic invasions and led to external invasions by or via
Spain)
33 The full list of instruments for the baseline estimates is: rainfall in the winter, spring and summer, the
average temperature, and second calamity variable. We alternatively consider the first calamity variable,
28
12
TABLE 4 HERE
TABLE 5 HERE
Here we show the results for the income equation in table 4 and for the population
equation in table 5. Unlike the estimates of table 3, the estimated parameters in these
tables have a “treatment effect”-type causal interpretation34. In table 4, we can see that
an exogenous increase in the land-population ratio (ie. an exogenous decrease in the
population) has a positive effect on per capita GDP after 14 years and an even stronger
effect after 17 years, but no effect at 4 years; this is what makes sense from an
agricultural and labor market perspective.35
Table 5, in turn, shows the causal results for the population equation (which is part of
the estimated 3SLS system of the income equation in table 4). The results in this table
confirm that (with a one-period lag) population did indeed respond to income – in the
baseline estimate (column 1), an exogenous 1% increase in per capita income causes a
1.4% decrease in the land-population ratio one year later. This outcome is likely to be
due to a combination of both the positive (less people die) and preventive (as families
adjust their fertility levels to the new income, more people are born) checks.
5. Discussion
The evolution of the Portuguese economy during 1500-1850 is not a story of decline,
but viewed over the very long run it is a story of stagnation. However, this is not
monotonic stagnation, and several distinct periods can be identified.
During an initial period from 1500 to 1530 there is a tendency for incomes to decline, a
trend shared with other European economies at the same time and which corresponds
to continued population-catch up from the post-bubonic plague population levels
(figure 4). The consequent decrease in land-labor ratio was accompanied by a decline of
the wage-rent ratio and agricultural TFP, and in the absence of new external sources
of growth incomes fell36. Agriculture, which in the fifteenth and early sixteenth
centuries had been essentially pastoral (Medeiros 1993) gradually switches to arable, a
change which given individuals’ taste for variety in food intake they were only willing
to incur since it was a way to feed more people without increases in technology.
From about 1530 onward, incomes grew both intensively and extensively until around
1645. In our view this was caused by a combination of several factors, prominently the
and interactions between all these variables. The basic results are unchanged. It should also be noted
that not all IV’s are individually statistically significant, but in the table we also report the F-statistics
for each of the first stage regressions, which show that jointly instruments are not weak. Comparing
these F-statistics with the cut-offs suggested by Stock and Yogo (2005) we conclude that a weak
instrument problem does not exist. Further it is reassuring that both the magnitudes and individual
significance of the first stage results make sense (for example the summer temperatures have stronger
effects than those of the other periods of the year; for more systematic evidence that higher
temperatures tend to reduce economic growth, see Dell, Jones and Olken 2012).
34 We report unconditional results as well due to the possibility that including controls leads to the
“controlling for intermediate outcomes” fallacy.
35 The latter case can be seen as a placebo test, and these results are not sensitive to an approximate
alternative choice of thresholds.
36 The returns from the empire were growing but still in absolute terms too small at this point to matter
a great deal (Costa, Palma and Reis 2014).
13
expansion of new crops which resulted from the Columbian exchange, notably maize37.
A series of legal reforms (Ordenações Manuelinas) were progressively issued from 1512
to 1605 and, for instance by encouraging the standardization of weights and measures,
may have promoted specialization gains from additional market integration38. Even
more important may have been the land clearances we discussed before, which lead to
more intensive usage of the land, hence contributing to higher levels of production.
After 1620 GDP per capita and the real wage start diverging and after 1643, the
economy enters a period of twenty years of stagnation followed by slow but persistent
decline until 1703. Maize continued to expand at a steady pace (figure 5), but the areas
for least costly expansion may have been used by then and we must not forget the
permanent state of war which lasted until 1668. Meanwhile, the empire continued to
provide growing returns, but these would nevertheless remain under 10% of income
until significantly rising in the eighteenth century (Costa, Palma and Reis 2014).
FIGURE 5
The eighteenth century opens with a persistent boom which lasts up to about 1755, at
which time income was up to more than 170% that of the turn into the eighteenth
century – a remarkable rate of intensive growth for a premodern economy.39 What
caused the extraordinary fast rise in incomes during the first half of the eighteenth
century? First, the economy was subject to the large exogenous shock of the discovery
and subsequent exploitation of rich gold and diamond mines in Brazil. Due to this
discovery and subsequent exploitation, incomes were rising and population was falling
(due to emmigration, especially from the north of the country, in association with the
gold rush).
Many irrigation projects needed to allow further expansion of maize after the midseventeenth century, as well as the expansion of vine, especially in the Douro region,
after the turn of the century, were of the “lumpy investment” type. As it is well known
in the development economics literature, people may find it hard to save significant
amounts over a long period of time due to dynamic inconsistency and lack of any
formal commitment mechanism for accumulating savings. The combination of these
facts meant that the gold windfall – which affected people’s income both directly
through remittances and indirectly through higher land-labor ratios resultant from the
emigration of people to Brazil in the context of the gold rush – may have helped
finance these irrigation projects, especially in the north of Portugal. This is confirmed
by both the rise in agricultural TFP during the first decades of the eighteenth century
(figure 2b), the evidence of the dízimos (figure 5) and the regional patters of population
growth which can be seen in figure 6.
FIGURE 6 HERE
Notice that because agricultural TFP is endogenous, the full impact and timing of the introduction of
maize cannot be fully observed. In other words, under constant population this variable would have
presumably risen much more.
38 More research in needed on this topic, however, and it is far from clear how much were these legal
changes were enforced, since these laws were periodically reissued, a sign of previous limited
enforcement, and in fact well into the nineteenth century a diversity of weights and measures persisted.
39 This is a rate of intensive growth of about 1% a year, comparable to modern economic growth rates.
Of course, given the contemporaneous population growth, extensive growth was even higher.
37
14
Incidentally, there had been an earlier period when agricultural TFP had risen, which
was the first decades of the seventeenth century, when maize was first introduced in a
systematic manner. Our hypothesis is that by the mid seventeenth century, further
introduction required increasingly costly investments in infrastructure. In addition,
wine cultivation expanded considerably in the north of the country following the 1703
Methuen treaty with England.
The boom up to 1755 was followed by slow but persistent decline which intensified
after 1790 and continued well into the nineteenth century (with a short-lived rebound
during 1820 to 1834). What was the cause of the decline? What is certain is that after
1790, several of the previous sources of growth successively become exhausted: gold
remittances became negligible, further expansion of maize was not possible, the
privileged relation with England in relation to wine exports ended, and eventually,
following the invasion by Napoleon’s troops in 1807 the court escaped to Brazil, and as
a delayed but direct consequence that empire was lost in 1822. The loss of Brazil may
have mattered a good deal: a recent estimate suggests that severing the colonial trade
around 1800 would lead to a real wage between one fifth and one fourth lower than
that observed (Costa, Palma and Reis 2014).
Further, while in other parts of the European periphery the increased usage of iron
agricultural improvements were a source of growth since the eighteenth century
(Edvinsson 2013b), the evidence which exists for some parts of Portugal suggests their
usage was still limited well into the nineteenth century (Fonseca and Reis 2011). The
second half of the eighteenth century may have also experienced fast institutional
change.40 The possible role of political economy factors in arresting the development
of the economy after the mid-eighteenth century has been subject to different, and
sometimes conflicting, interpretations in the Portuguese literature.41 Around 1850 the
economy was below the median income for 1500-1850.
In the very long run, the economy conformed to the predictions of the Malthusian
model: despite variation in response to shocks of various natures, incomes eventually
showed highly persistent long term behavior, converting back to what could be a long
term stagnation steady state. However, while forces of convergence to such a steady
state did include endogenous fertility and mortality responses in the spirit of Malthus,
it is also true that negative effects on a political economy or institutional nature may
have been present. More research is needed on this topic, but what does seem clear at
this point is that the middle of the eighteenth century was a turning point.42
Tengarrinha (1994), for instance, has suggested the second half of the eighteenth century may have
experienced a “segneurial” revolution: a series of institutional changes which lead to redistribution from
the peasants to the lords. While the wage rent-ratio was in fact falling during much of the eighteenth
century (figure 2a) it is not clear how original to the eighteenth century were the changes which
Tengarrinha notes, and hence their causal importance is open to debate.
41 Pereira (2009) claims the 1755 earthquake lead to benevolent political changes, while Macedo (1963)
believed the windfall of Brazilian gold lead to a Dutch disease and institutional resource curse. For the
literature on Dutch disease and institutional resource curse in Spain, see Hamilton (1934), Forsyth and
Nicholas (1983), Drelichman (2005a, b, 2007) and Grafe (2012).
42 If the data is used in levels, it is non-stationary and per capita GDP and the population level were
cointegrated up to around 1755, but not after this date (due to the super-consistency property associated
with the existence of a cointegration vector, no controls were used in these regressions). While in the
econometrics reported, we used the natural logarithm of all variables in order to be able to interpret all
estimated coefficients as elasticities, it is well known that logs can help attenuate non-stationarity. As
the appendix reports, if the ADF tests are repeated for our main variables, per capita GDP and
population levels, a unit root with drift (or alternatively, a trend) cannot be rejected at the standard
levels of significance. We tested for a cointegration relationship for these variables and have additionally
40
15
6. Conclusion
Thomas and McCloskey (1981, p.102) have described Portugal, along with Spain, as
one of the “giants” of the sixteenth century, especially in comparison with Britain, the
“inconsiderable little island of the sixteenth century, a mere dwarf”. In turn, Bairoch
(1976) considered Portugal one of Europe’s five richest countries as late as 1800, and
Lisbon one of Europe’s four most populous cities (after Naples, Paris and London).
How do these statements stand in comparison with the evidence we have gathered
here? The discussion so far allows us to draw three main conclusions:
1. Unexpectedly “late”, Portugal was comparatively rich. There were ups and
downs, but while a loss of dynamic is already noticeable since the second half of
the eighteenth century, Portugal’s definite divergence towards relative poverty
by European standards is a result of failure to industrialize and hence a
nineteenth century phenomenon. As late as 1750, Portugal was, in per capita
terms, as rich as Britain and Holland, and richer than France, Spain, Germany
and Sweden.
2. At least until the mid-eighteenth century, Portugal was not Malthusian, in the
sense that per capita income did not have a tendency of convergence towards a
stagnation steady-state. This is confirmed by contemporary rise in both income
and population. While much growth was in fact of an extensive nature, these
forces were not sufficient to cancel the Smithian intensive growth opportunities
generated by the introduction and diffusion cultures such as maize and wine,
and the increasing returns from the empire.
3. After the mid-eighteenth century Portugal entered a period of persistent
decline which had as proximate causes the increase in population combined
with the exhaustion of the previously available engines of economic growth
without their substitution by new sources. Whether there was an institutional
element for this decline and if so how it may be related to the previous resource
boom and episode of “extractive growth” (Acemoglu and Robinson 2012)
remains unclear at the moment.
In the spirit of Broadberry et al (2011) or van Zanden and Leeuwen (2012), who focus
on the proximate rather than ultimate or fundamental causes of growth, our goal in
this paper has been to provide a careful factual description of Portugal’s
macroeconomic history during this period. In light of the new evidence presented here,
it would be helpful for future research to consider in more detail the Malthusian versus
political economy interpretations of Portugal’s long term decline. In particular,
following the boom up to 1755 it would be important to understand the persistent
decline after that time, which was intensified after about 1790. What does seem clear
at this point is that the middle of the eighteenth century was a turning point. It was
only after this date that the sources of intensive growth were not sufficient to cancel
the Malthusian forces which pushed the economy towards extensive growth only.43
estimated an error correction-model which confirms the relationship. It does not seem to bring
additional insights to the story, however, and we do not report it.
43 What is certain at this point is that the Malthusian forces pulled economies in one direction, but this
force could, and indeed often was, often cancelled in net terms if contrary external sources of growth
were available at a given time (Mokyr and Voth 2008).
16
We have offered a description of both the factors of growth and some of the proximate
factors for the ultimate decline of Portugal’s economy from the early sixteenth century
to the mid ninetieth century. We have hence added Portugal to the pool of existing
evidence on GDP as well as factor and commodity prices for early modern Europe.
The Portuguese database allows us to round off the habitual picture with the inclusion
of a non-core economy the likes of which are still little known in this context. The
Portuguese case provides partial support for the notion of an early modern “little
divergence” within Europe. Portugal’s volatile growth record suggests that as far as
this country is concerned a little divergence did happen, but it was only definite after
1750.
Despite a seemingly promising episode of intensive growth which lasted over 200
years, it is undeniable that nineteenth century Portugal failed to take off. For a while,
Portugal sailed away from Malthus successfully, but in the end the empire was no
silver bullet – Malthusian and institutional forces pulled back. In the absence of new
and fortuitous influences, convergence towards a stagnant steady-state resumed,
eventually leaving the country as one of the most backward economies of Europe.
Portugal would not be able to enter a process of modern economic growth until well
into the twentieth century.
References
Acemoglu, D. and J. Robinson (2012). Why Nations Fail. Profile Books.
Allen, Robert C. (2001). The Great Divergence in European Wages and Prices from
the Middle Ages to the First World War. Explorations in Economic History, 38, pp.411447.
Allen, Robert C. (2003). Progress and Poverty in Early Modern Europe. Economic
History Review, 3, pp. 403-443.
Almeida, Luís Ferrand de (1992). A Propósito do Milho ‘Marroco’ em Portugal nos
Séculos XVII e XVIII. Revista Portuguesa de História, 27, pp.103-43.
Almeida, Luís Ferrand de (1995). Sobre a introdução e difusão do milho maís em
Portugal. In Luís Ferrand de Almeida (ed.) Páginas Dispersas. Estudos de História
Moderna de Portugal (Coimbra), pp. 229-259.
Álvarez-Nogal, Carlos and Leandro Prados de la Escosura (2007). The decline of Spain
(1500-1850): Conjectural estimates. European Review of Economic History 11, 319-366.
Álvarez-Nogal, Carlos and Leandro Prados de la Escosura (2013). The rise and fall of
Spain (1270–1850). The Economic History Review 66 (1): 1–37
Amorim, Inês (1994).Descrição da Comarca da Feira. 1801. Revista da Faculdade de
Letras. Historia 11, 227-286.
Amorim, Inês (1996). Aveiro e a sua provedoria no séc. XVIII : 1690-1814: Estudo
económico de um espaço histórico. Ph.D. dissertation, Universidade do Porto.
Angeles, L. (2008). GDP per capita or real wages? Making sense of conflicting views
on pre-industrial Europe. Explorations in Economic History, 45(2), 147-163.
17
Antras, Pol and Voth, Joachim (2003). Factor prices and productivity growth during
the British industrial revolution. Explorations in Economic History, 40, 52-77.
Bairoch, P. (1976). Europe’s Gross National Product: 1800-1975. Journal of European
Economic History 5, 273-340.
Barata, Filipe Themudo and Henriques, António Castro (2011). Economic and fiscal
history. In José Mattoso (ed.). The historiography of medieval Portugal (c.1850-2010).
(Lisboa: Instituto de Estudos Medievais, UNL).
Broadberry, Campbell, Klein, Overton and Leeuwen (2011). British Economic Growth
1270-1870: An output based approach. Unpublished manuscript.
Cardoso, Jose Luis et al. (2003). O Tratado de Methuen (1703). Diplomacia, Guerra,
Politica e Economia (Lisboa: Livros Horizonte).
Costa, L. F. Lains, P. and S. Munch (2011). Historia Economica de Portugal, 1143-2010
(Lisboa: A Esfera dos Livros).
Costa, Leonor F., Nuno Palma, and Jaime Reis (2014). The Great Escape? The
Contribution of the Empire to Portugal’s Economic Growth, 1500-1800. European
Review of Economic History, forthcoming
Costa, Sousa and Rocha (2013). O Ouro do Brasil. Imprensa Nacional Casa da Moeda.
Clark, Gregory (2008). A Farewell to Alms. A Brief Economic History of the World
(Princeton and Oxford, Princeton University Press).
Clark, Gregory, Cummins, Joseph and Brock Smith (2012). Malthus, Wages and
preindustrial Growth”, Journal of Economic History 72, pp.364-92.
Crafts, Nicholas and Mills, Terence C. (2009). From Malthus to Solow: how did the
Malthusian economy really evolve? Journal of Macroeconomics 31(1): pp. 68-93.
Dell, Melissa, Benjamin F. Jones and Benjamin Olken (2012). Temperature Shocks and
Economic Growth: Evidence from the Last Half Century. American Economic Journal:
Macroeconomics 4(3): pp. 66–95
Drelichman, Mauricio (2005a). The curse of Moctezuma: American silver and the
Dutch disease Explorations in Economic History 42 (3), 349-380
Drelichman, Mauricio (2005b). All that glitters: Precious metals, rent seeking and the
decline of Spain European Review of Economic History 9 (3), 313-336
Drelichman, Mauricio (2007). Sons of Something: Taxes, Lawsuits, and Local Political
Control in Sixteenth-Century Castile The Journal of Economic History 67 (03), 608-642
Edvinsson, Rodney B. (2009). Swedish Harvests, 1665-1820: Early Modern Growth in
the Periphery of European Economy, Scandinavian Economic History Review, 57(1) pp
2-25.
18
Edvinsson, Rodney B. (2013a). New annual estimates of Swedish GDP, 1800–2010,
Economic History Review, 66(4), pages 1101-1126.
Edvinsson, Rodney B. (2013b). Swedish GDP 1620-1800: stagnation or growth?,
Cliometrica, 7(1), pages 37-60.
Fonseca, Helder Adegar (1996). O Alentejo no Século XIX: Economia e atitudes económicas.
Lisboa: Imprensa Nacional – Casa da Moeda.
Fonseca, Helder Adegar and Jaime Reis (2011). The Limits of Agricultural Growth in
a Fragile Eco-System: Total Factor Productivity in Alentejo, 1750-1850. In Olsson,
M. & P. Svensson (eds.) Growth and stagnation in European historical agriculture.
Turnhout: Brepols publishers, pp. 37-66
Forsyth, P. and S. Nicholas (1983). The Decline of Spanish Industry and the Price
Revolution: A Neoclassical Analysis Journal of European Economic History, pp. 601-610.
Galor, Oded and Weil, David N. (2000), “Population, Technology and Growth: From
Malthusian Stagnation to the Demographic Transition and Beyond”, American
Economic Review, 90, pp. 806-28.
Gil, Maria Olímpia da Rocha (1965). Arroteias no Vale do Mondego durante o século XVI:
Ensaio de história agrária. Lisboa: Instituto de Alta Cultura.
Godinho, Vitorino Magalhães (1968), Ensaios. Lisboa: Sá da Costa, 4 vols.
Goldstone, J. A. (2002). Efflorescences and economic growth in world history:
rethinking the" Rise of the West" and the Industrial Revolution. Journal of World
History, 13(2), 323-389.
Grafe, R. (2012). Distant Tyranny. Markets, Power and Backwardness in Spain, 1650-1800.
Princeton University Press
Guiot, Joel and Christophe Corona (2010). Growing season temperatures in Europe
and climate forcings over the past 1400 years. PLoS One 5(4). Data avaliable at the
World Climate Centre for Paleoclimatology of the U.S. National Climatic Data Center:
www.ncdc.noaa.gov/paleo/recons.html
Hamilton, Earl J. (1934). American Treasure and the Price Revolution in Spain, 15011650. Harvard University Press
Hatcher, J. (2003). Understanding the population history of England, 1450-1750. Past
and and Present, 180, 83- 130.
Hoffman, Philip (1996), Growth in a Traditional Society. The French Countryside, 14501815 (Princeton: Princton University Press).
Jones, E. (1988). Growth Recurring: Economic Change in World History. Ann Harbor: The
University of Michigan Press
Klemp, Marcus (2011). Prices, wages and fertility in pre-industrial England Cliometrica
6 (1): pp. 63-77.
19
Langer, William (1975). American foods and Europe’s population growth 1750-1850.
Journal of Social History, 8, 51- 66.
Lains, Pedro and Sousa, Paulo (1998). Estatística e produção agrícola em Portugal,
1848-1914. Análise Social, 33, 935-968.
Macedo, J. Borges (1963). Problemas da história da Indústria Portuguesa no século
XVIII. Lisboa.
Malanima, Paolo (2011). The long decline of a leading economy: GDP in central and
northern Italy, 1300-1913. European Review of Economic History 15, 169-219.
Malanima, Paolo (2013). When did England overtake Italy? Medieval and early
modern divergence in prices and wages. European Review of Economic History 17(1): 4570
Martins, Conceição Andrade (1998). Vinha, vinho e política vinícola em Portugal.: do
Pombalismo à Regeneração. Ph.D. dissertation. Universidade de Évora.
Maia, Fernanda Paula Sousa (1991). Do mosteiro do Bustelo: Propriedade e produção
agrícola no Antigo Regime (1638-70) e (1710-1821). Ph.D dissertation, Universidade
Portucalense.
Medeiros, Carlos Alberto (1993). Environnement, agriculture et élevage au Portugal a
l’époque des découbertes maritimes. In Durand, Robert. (ed.) L'homme, l'animal
domestique et l'environnement: du Moyen âge au XVIIIe siècle. Nantes: Ouest Editions),
307-313.
Messer, Ellen (2000), Maize. In The Cambridge History of World Food (Cambridge:
Cambridge University Press), vol. 1, 97-112.
Miguel, E., S. Satyanath and E. Sergenti (2004). Economic Shocks and Civil Conflict:
An Instrumental Variables Approach. Journal of Political Economy, 112(4), 725-753
Monteiro, Nuno (2005). A ocupação da terra. In Lains, Pedro e Silva, Alvaro Ferreira
da Silva (eds). História Económica de Portugal (Lisboa: ICS), vol. I, 67-92 .
Mota, Salvador Magalhães (2006). Cistercienses, camponeses e economia rural no Minho na
era do Antigo Regime: O mosteiro de Santa Maria de Bouce e o seu dominio (Lisboa:
Imprensa Nacional- Casa da Moeda).
Mokyr, Joel and Hans-Joachim Voth (2008). Understanding Growth in Europe, 17001870: Theory and Evidence. The Cambridge Economic History of Modern Europe Volume
1, 1700–1870.
Neto, M. (1997) Terra e conflito : região de Coimbra (1700-1834) (Viseu : Palimage).
Neto, Margarida Sobral (1997). Terra e conflito : região de Coimbra (17001834) (Viseu : Palimage).
20
Neto, Margarida Sobral (2012). A Crise da Agricultura Portuguesa no Século XVII, in
Álvaro Garrico, Leonor Freire Costa e Luís Miguel Duarte (eds), Estudos em
Homenagem a Joaquim Romero de Magalhães. Economic, Instituições e Império (Coimbra:
Almedina), pp. 263-76.
Neto, Margarida Sobral (2010), O Universo da Comunidade Rural. Época Moderna
(Coimbra: Palimage).
Nicolini, E. (2007). Was Malthus right? A VAR analysis of economic and demographic
interactions in pre-industrial England. European Review of Economic History 1, pp 99121
Nunn and Qian (2011). “The potato’s contribution to population and urbanization:
evidence from a historical experiment”, Quarterly Journal of Economics, 126: 593-650.
O’Rourke, Kevin H. and Williamson, Jeffrey G. (2005), “From Malthus to Ohlin:
Trade, Industrialisation and Distribution since 1500”, Journal of Economic Growth, 10,
pp. 5-34.
Oliveira, Aurelio de (1979). A abadia de Tibães: 1630/80-1813, Ph. D. Dissertation,
Universidade do Porto,2 vols.
Oliveira, António de (1972). A vida económica e social de Coimbra de 1537 a 1640
(Coimbra: Instituto de Alta Cultura).
Oliveira, A. (2002). Movimentos sociais e poder em Portugal no século XVII (Coimbra:
IHES da FLUC).
Oliveira, César (1996) História dos municípios e do poder local : dos finais da Idade
Média à União Europeia (Lisboa: Círculo de Leitores).
Oliveira, João Nunes de (1990), A Produção Agrícola de Viseu entre 1550 e 1700 (Viseu:
Câmara Municipal de Viseu).
Oliveira, Nicolau de (1620). Livro das Grandezas de Lisboa (Lisboa: Jorge Rodriguez).
Palma, N. and J. Reis (2014). Portugal’s population, 1527-1864: a regional analyis.
Unpublished manuscript
Pauling, Andreas, Jürg Luterbacher, Carlo Casty, and Heinz Wanner (2006). Five
hundred years of gridded high-resolution precipitation reconstructions over Europe
and the connection to large-scale circulation. Climate Dynamics 26(4): pp. 387-405.
Pereira (2009). The Opportunity of a Disaster: The Economic Impact of the 1755
Lisbon Earthquake. Journal of Economic History, vol. 69, no. 2, June, pp. 466-499.
Pfister, Ulrich (2011). Economic growth in Germany, 1500–1850. Unpublished
manuscript. Presented at the “Quantifying long run economic development”
conference, University of Warwick in Venice.
Prices, Wages and Rents (2013). Chronology of Calamities. This source is avaliable at
http://pwr-portugal.ics.ul.pt/?page_id=20
21
Ravn, Morten and Uhlig, Harald (2002). On adjusting the Hodrick–Prescott filter for
the frequency of observations. The Review of Economics and Statistics 84 (2): 371–375.
Reis, Jaime (2000). How poor was the European periphery before 1850? The
Mediterranean vs. Scandinavia. In: The Mediterranean response to Globalization
before 1950, ed. S. Pamuk and J. Williamson. Routledge.
Reis, Jaime (2013). The economic growth in Portugal during the imperial era, 15001850: Is there an Iberian model? The Laureano Figuerola Lecture for 2013, Fundacion
Areces, Madrid
Ribeiro, Orlando (1986). Portugal, o Mediterrâneo e o Atlântico: Esboço de Relações
Geográficas. (Lisboa: Sá da Costa. 4th edition).
Rodrigues, Lisbeth de Oliveira (2013). Os hospitais portugueses no Renascimento (14801580): O caso de Nossa Senhora do Pópulo das Caldas da Rainha. Ph.D. dissertation,
Universidade do Minho.
Rosés, Joan R., Kevin H. O’Rourke and Jeffrey G. Williamson (2007). Globalization,
Growth and Distribution in Spain 1500-1913. Working Papers in Economic History,
Departamento de Historia Económica e Instituciones, Universidad Carlos III de
Madrid, WP-08.
Salvado, João Paulo (2009). Nobreza, monarquia e império. A casa senhorial dos almotacésmores do reino (séculos XVI-XVIII). Ph.D. dissertation, Universidade Nova de Lisboa.
Santos, Rui (2003). Sociogénese do latifundismo moderno: Mercados, crises e mudança social
na região de Évora, séculos XVII a XIX. 2003 (Lisboa: Banco de Portugal).
Serrão, José Vicente (2009). Land management responses to market changes. Portugal,
seventeenth-nineteenth centuries. In Pinilla, Vicente (ed.). Markets and Agricultural
Change in Europe from the Thirteenth to the Twentieth Centuries. Turnhout: Brepols, 4773.
Sharp, P., Strulik, H. And Jacob Weisdorf (2012). The determinants of income in a
Malthusian equilibrium. Journal of Development Economics, 97:1, 112–117
Stock, James H. and Yogo, Motohiro (2005). Testing for weak instruments in linear IV
regression. In J.H. Stock and D.W.K. Andrews (eds), Identification and Inference for
Econometric Models: Essays in Honour of Thomas J. Rothenberg. Cambridge: Cambridge
University Press, chapter 5.
Tengarrinha, José (1994). Movimentos populares Agrários em Portugal (1750-1825)
(Lisboa, Europa-America).
Thomas, Richard P. and McCloskey, Deirdre N (1981). Overseas trade and empire
1700-1800. In Roderick Floud and Deirdre N. McCloskey (eds), The Economic History
of Britain since 1700. Cambridge: Cambridge University Press, 2 vols, vol. 1, 87-102.
Trindade, Maria José Lagos (1981). Estudos de História Medieval e Outros. Lisboa
22
Van Zanden, Jan Luiten (2005). What Happened to the Standard of Living Before the
Industrial Revolution? New Evidence from the Western Part of The Netherlands. In
Living Standards in the Past. New Perspectives on Well-Being in Asia and Europe., eds.
Robert C. Allen, Tommy Bengtsson, and Martin Dribe. Oxford: Oxford University
Press.
Van Zanden, Jan Luiten. 2005. “What Happened to the Standard of Living Before the
Industrial Revolution? New Evidence from the Western Part of The Netherlands.” In
Living Standards in the Past. New Perspectives on Well-Being in Asia and Europe., eds.
Robert C. Allen, Tommy Bengtsson, and Martin Dribe. Oxford: Oxford University
Press.
van Zanden, Jan Luiten and van Leeuwen, Bas (2012). Persistent but not consistent:
The growth of national income in Holland 1347‐1807. Explorations in Economic History
49, 119‐130
Voigtlander, Nico, and Hans-Joachim Voth. (2009). Malthusian Dynamism and the
Rise of Europe: Make War, Not Love. American Economic Review, 99(2): 248-54.
Voigtländer, Nico and Hans-Joachim Voth (2013). The Three Horsemen of Riches:
Plague, War, and Urbanization in Early Modern Europe. The Review of Economic
Studies
Weil, D. (2008). Economic Growth. Pearson
Williamson, Jeffrey G. (1998). Growth, distribution, and demography: Some lessons
from history. Explorations in Economic History 35(3): 241-271.
Wrigley, E. A. and R. S. Schofield [1989 (1981)]. The Population History of England
1541-1871. Cambridge University Press.
23
Tables and figures
Wheat
bread
62
Maize
bread
n.a.
Meat
Eggs
Hens
Wine
16th c.
Unskilled
wages
38
Coal
Linen
Population*
55
Olive
oil
39
30
33
35
52
32
70
17th c.
40
90
84
98
100
100
82
100
98
60
100
18th c.
65
96
94
99
100
100
100
100
80
83
100
Table 1. Data coverage for each series or underlying variable (%). *Population: refers to
periods for which we have coverage of at least some part of the country (see the appendix)
Britain
The
Germany
Netherlands
France
Italy
Spain
Sweden
Portugal
1500
39
37
49
50
68
50
-
58
1550
39
37
-
-
64
54
35
30
1600
37
68
34
50
60
53
36
44
1650
34
69
-
-
62
41
-
51
1700
55
54
40
54
65
48
53
45
1750
61
60
45
55
68
46
41
59
1800
75
67
42
56
60
54
40
50
1850
100
79
61
78
66
64
52
46
Table 2. Output per capita in Europe (UK 1850=1).
24
Dependent variable: Ln
of pcGDP
(1)
(2)
(3)
(4)
(5)
(6)
Estimator
OLS
OLS
OLS
OLS
OLS
OLS
Period
Full sample
Full sample
1530-1755
1530-1755
1756-1850
1756-1850
2.168***
(.6667)
-.1917***
(.0684)
.0656***
(.0189)
-.0306***
(.0108)
-.0002
(.0002)
1.284***
(.281)
-.1029***
(.0250)
-.033***
(.0102)
2.531***
(.3483)
-.2899***
(.0545)
.1100***
(.0251)
-.0143
(.0147)
1.161***
(.3055)
.1531***
(.0576)
.1027***
(.0256)
-.0252*
(.0149)
-
-
1.099***
(1.015)
-.1697*
(.0963)
1166***
(.0234)
-.010
(.0139)
.0005
.0004
1.054
(3.711)
.1608
(.2688)
.1030***
(.0307)
-.0254*
(.0138)
.00004
(.0013)
Constant
Ln of land-population
ratio
Ln of Agricultural
TFP
Ln of Manufacturing
TFP
Linear time trend
-
-
Ln of gold imports
.0021**
.0028**
-.0023
-.004
-.0005
-.0005
from Brazil (at
(.0008)
(.0011)
(.0018)
(.0023)
(.0022)
(.0025)
constant prices)
Ln of per capita GDP
.7909***
.6452***
.6293***
.5983***
.5996***
(t-1)
(.0412)
(.0629)
(.065)
(.0781)
(.0887)
2
2
R or pseudo R
0.8888
0.8882
0.8939
0.8951
0.8388
0.8388
Number of
320
320
225
225
95
95
observations
Table 3. Explaining the Portuguese per capita GDP 1530-1850. In columns (1)-(4) the NeweyWest HAC standard errors allow autocorrelation up to 5 lags. ***=individually statistically
significant at the 1% level, **= at 5% level, *= at 10% level.
25
Dependent
variable: Ln of
pcGDP
(1)
(2)
(3)
(4)
Estimator
3SLS
3SLS
3SLS
3SLS
Constant
4.995***
(.1978)
4.719***
(.2009)
4.759***
(.1883)
4.887***
(1.29)
Ln of first
difference of
1.654
land-population
(1.170)
ratio
(t-4)
Ln of first
difference of
1.92***
land-population
(1.352)
ratio
(t-14)
Ln of first
difference of
2.629***
4.887***
land-population
(1.016)
(.0093)
ratio
(t-17)
Additional
YES
YES
YES
NO
controls
First stage F204.05
181.01
163.39
163.39
statistic
R2 or pseudo R2
0.3517
0.3738
0.3965
0.0326
Number of
306
306
303
303
observations
Table 4. Explaining the Portuguese per capita GDP, 1530-1850. ***=individually statistically
significant at the 1% level, **= at 5% level, *= at 10% level.
Dependent variable: Ln of Landpopulation ratio
(1)
(2)
Estimator
3SLS
3SLS
10.76***
9.761***
(.4058)
(.4071)
-1.402***
-1.196***
Ln of per capita GDP (-1)
(.0832939)
(.0835684)
Additional controls
YES
NO
First stage F-statistic
75.27
75.27
R2 or pseudo R2
0.3965
0.0326
Number of observations
303
303
Table 5. Explaining the Portuguese population, 1530-1850. The estimates (1) and (2)
correspond to the 3SLS systems correspondent to columns (3) and (4) of table 4.
***=individually statistically significant at the 1% level, **= at 5% level, *= at 10% level.
Constant
26
Figure 1. Portugal’s GDP per capita, real wage (left scale, unit: 1530=1) and population (right
scale, annual variation since 1530, unit: millions of individuals), 1500-1850. Sources: see text.
27
Figure 2a. The wage-rent ratio, 1500-1850
Figure 2b. Agricultural total factor productivity, 1500-1850
Figure 2c. Manufacturing total factor productivity, 1500-1850
28
Figure 2d. The agricultural-manufactures relative price, 1500-1850
Figure 2. Raw data and trend component of the HP filtered series, 1500-1840 (consists of
panels a - d)
1.20
Netherlands
1.00
Welfare ratio change
France
0.80
0.60
Spain
England
Poland Belgium
Germany
Italy Austria
0.40
0.20
0.00
0
0.5
1
1.5
Population change
2
2.5
3
Figure 3. Welfare ratio and population change 1500-1800, for the ten countries in the Allen
(2001) dataset
29
Figure 4. Behavior of the real wage. Source: PWR project.
Figure 5. Maize and millets as a percentage of total production of cereals, including rye and
wheat. While the source does not differentiate between maize and millets, referring to both as
milhos, the latter are known to have been initially small and not to have experienced significant
growth). Source: Oliveira (1990, 2002).
30
Figure 6. Portugal’s annual population, by regions. Sources: See the appendix.
31
For on-line publication only: Appendix for “Portuguese demography and
economic growth, 1500-1850”, by Nuno Palma and Jaime Reis
This version: May 4th, 2014
In this appendix, we provide information which is complementary to that presented in
the main text. In particular, we describe here in detail how we constructed the annual
population series (section A1), and the climacteric and “calamity” variables (section
A2). We also show the results for the econometrics specification and robustness tests
which we performed (section A3). The dataset used in this paper is available at:
https://sites.google.com/site/npgpalma/
A1. Portugal’s population, 1527-1864: an annual series
We combine information provided by several sources, in particular census and censustype information about stocks and a wealth of parish-level data about flows to form an
detailed estimate of the annual evolution of the early modern and nineteenth century
Portuguese population up to the first modern census, which occurred in 1864. In this
dataset, all stocks, e.g. population levels, refer to beginning-of-period quantities while
flows, e.g. births, correspondingly refer to quantities which apply to the duration of
the year in question. In a few (relatively rare) cases we had to make assumptions about
missing data as explained in detail below, but in calculating the regional estimates
there are no interpolations: all data does correspond to observed annual variation.
The goal heare has been to reach an aggregate annual population number for Portugal,
which we then can use to infer per capita GDP levels, and test a variety of Malthusian
hypothesis. But the underlying data offers of course a detailed picture of regional
demographic patters, which we explore in a companion paper (Palma and Reis 2014).
We have used flow data information from all the available secondary sources that to
our knowledge exist44, and we have further filled in some gaps by collecting addional
primary information ourselves. While our aggregate annual estimates go as far back as
1530, it is important to realize that at different times the weight of the evidence varies.
The extreme case is the first half of the sixteenth century, for which the only evidence
we have is for three parishes (and in one of them deaths are estimated by proxy). The
coverage dates for each province are given in table 1.
While in computing the regional estimates we have used no interpolations at all, when
inflating these numbers into an aggregate esitmate we have used a minimal degree of
interpolations on either the beginning or the end of some of the regional estimates in
order to avoid capturing spurious variation provoked by the sudden entrance or exit of
parishes with different demographic behavior (this is always done for periods for which
other regional estimates with “true” high frequency variation also exist for the period
at stake). The full list of parishes and periods for which this was done is: Viseu
(interpolated 1840 to 1863), Guimarães (1530 to 1579) and Ventosa (1530 to 1558). In
In some cases we were unable to use some of the parishes for which the data is available from
seconday sources either because the author only presents these in a graph without showing the raw data
in tabular form, or because this is done but only three year averages are given. In some cases we decided
not to use the data when the coverage is too small or too far away from the periods for which the stocks
are known; an instance is S. Martinho da Árvore, for which we know 172 individuals existed in 1527-32
and 460 in 1706 but for the intervening period only flows for 1616-1685 are available).
44
32
the case of and Barcarena, the migration residual (explained in detail below) for 15881619 is assumed to be equal as that of the 1620-1706.
A1.1. Introduction
We build an estimate of Portugal’s population from the beginning of the early modern
period to the first modern census of 1864. There are several motivations for doing this.
First, detailed demographic information is of interest to historians in its own right.
Second, while some information about the population stock at a few moments in time
already exists, there are enormous gaps between each data point, and simply linearly
interpolating between such scarce data points would not produce sufficient identifying
variation for the series to be usable in statistical analysis as the actual, but unobserved,
volatility would be artificially smoothed.
We estimate the annual population stock by combining information about stocks with
regional – parish-level -- information about flows, which we aggregate based on
regional representativeness45. As far as stocks are concerned, we are fortunate to have
several usable estimates, which we discuss in detail below. The existence of these
“census-type” sources and the objective of our study, for which we mainly need the
aggregate population stock, mean we do not need to use techniques of the sort known
as back projection. This is fortunate as fact doing so would be likely lead to biases in
the estimates, as both back projection and generalized back projection methods are
well known to present problems when migration rates are no negligible46 – as is surely
the case of Portugal.
The existing studies which discuss regional trends tend to cover some regions more
than others. For this reason, we do not restrict ourselves to secondary sources which
have published the primary data but in fact collect some original data on several
understudied regions, for this study. It remains the case that for cost reasons we
primarily rely on existing secondary sources, but this is not to say that more primary
information of this kind is not available. While we hope our work will encourage
others to pursue this matter further, this is what we have to work with for the
moment. We use a total of X parishes, an unbalanced panel the longest series of which
covers the entire 1570-1864 period47.
The population stock at certain periods can be inferred from early modern counts
(contagens de fogos or numeramentos) and from the 1801 and 1864 censuses. The counts
were, however, reckoned in terms of “hearths” rather than of “souls”. The number of
residents therefore needs to be inferred by applying a factor of conversion of
inhabitants per hearth, a figure which enjoys only mild consensus among Portuguese
historians, ranging between 3.86 and 5.0. Here we use a factor of 4.048. While hearth
counts are certainly useful to infer the population stock at given moments, in general
all we have is a 1527–1532 numeramento49, a partial contagem from the early 18th
The exact location of each parish can be found at http://atlas.fcsh.unl.pt/cartoweb35/atlas.php?lang=pt
See for instance, Wrigley and Schofield (1989, p. xvi-xvii)
47 For the much bigger country of England, the standard study aggregates the contents of 404 parish
registers for a period of 330 years (Wrigley, and Schofield 1989).
48 In those cases for which the actual number of people is known, we use that instead. In the data file we
indicate the source for each stock which we have used.
49 The 1527-32 numeramento reports 282.708 hearths; if we apply a 3.86 to 5.0 conversion factor we
arrive to the following bounds for actual population numbers: 1,091,253 to 1,413,540. A factor of four
leads to our preferred point estimate for this period: 1,130,832. The 6 year interval refers to the fact that
45
46
33
century, a more complete version in 1758 and finally the first censuses which occur in
1801 and 1864. The underlying motivation for undertaking each varies; some were of
fiscal, religious, and even military nature50, and only the last one was what we can
reasonably call a “modern” census. The standard information which we use for most
series is the 1527-32 numeramento, the Corografia of Carvalho da Costa (1706), the
Memórias paroquiais (1758), the information of the 1801 census (sometimes
complemented by the Pina Manique 1798 count), and the first modern census of 1864
census51. For some locations we have reliable information about the stock at other,
additional, moments. When this is the case we use them, being that if we do not
explicitly indicate our source, it is the same as that for the corresponding flows of that
period.
Any concrete demographic information before 1500 is very scarce, in part because
some texts have been lost and in part because the entrance of royal officials in the
lands of the nobles would have been difficult before the early modern period – this may
be why an earlier numeramento would not have been possible (Marques 1997). Still, we
are able to compare a few regions just before the turn of the century – Alenquer in 1495
and Castelo Branco Guarda and Pinhel in 1496 – with their status in 1527-32, and doing
this we are able to conclude there was a clear demographic increase over the
intervening period52.
While this type of census-type information about population stocks is certainly useful,
it is also quite scarce – notice after 1532, we have no estimate at all for almost 200
years. Simply linear interpolation between these benchmarks would lead to utter lack
of identifying variation in the data, hence rendering it useless of econometric analysis.
Motivated by this difficulty, we use additional information to build a more complete
series. We complement the stock information with information about yearly flows
provided by parish-level records on births and deaths. Combined with appropriate
assumptions about child mortality and emigration flows, the result is information
about annual net population flows, which hence complement the stock information to
provide a continuous record. Our parish-level sample contains information about all
six major regions of the country. In table A1, we show how the weight of each region
has changed with time.
TABLE A1
The population stock at time of the 1527-32 numeramento leads to a land density of
between 12 and 18 souls per km2, which is roughly correspondent to Western
Europe’s average at this time. While the north of the country was more populous, the
south was more urbanized – 29 of the 39 cities with 500 or more hearths were situated
in the south (Marques 1997, p. 270). Lisbon had over 50,000 inhabitants as was hence
a large metropolis at the European scale and disproportionally huge relative to all
the lands subject to the jurisdiction of the king were surveyed in 1527, those of the house of Braganza in
1527-28, and those of the military orders in 1532. We have simply assumed all stocks apply to 1532.
50 In particular Pina Manique’s 1798 “census” had a primarily military purpose (Serrão 1970).
51 The least reliable of these is the Coreografia, which has been sometimes criticized for exaggerating
some of the estimates (see the discussion of Évora below.) Still, the Corografia is a useful, and
indispensable, benchmark because it is the first overall benchmark after the numeramento, and
importantly, it predates the large scale gold-rush related immigration to Brazil. It if does exaggerate the
estiamtes of 1706, then the true emigration will have been even larger than what we have estimated.
52 For Alenquer, see Godinho (19XX) and Ferro (19XX). For Castelo Branco, Guarda and Pinhel, see
Rau (19XX) and Dias (19XX).
34
other Portuguese urban centers. For a description of Portuguese demography up to
1527, see Ribeiro (1955) and Ribeiro (1963).
As for the terminal date for our demographic study, 1864, it corresponds to the first
modern and extensive census. The population stock was at this time 3,829,61853.
While at this time the 6 provinces were no longer formally described a such, we have
converted the district-level count and backed up the corresponding percentages for the
six traditional provinces; see Rodrigues (2004, p. 17-70) and Marques (19XX, p. 33).
A1.2. Methodological choices
While it is unavoidable that there is a certain degree of subjectivity in calculating some
of the quantities at stake here, we consider alternatives for the main methodological
choices in the robustness section, while lead to bounds to the quantities of interest.
We were careful to make sure that the parishes corresponded to fixed geographical
units over time. We stick to this rule even when it meant some data was unusable. For
sure this could be a form of sample selection, as older regions – those within city walls
for instance – are selected in favour or new or growing regions. This problem is
minimized by the fact that in Portugal, perhaps in part due to the long tradition of
political continuity and cultural uniformity, the parish-level denomination of places has
shown remarkable continuity over time (Hespanha 1992).
Another issue concerns the mapping of the number of baptisms and burials contained
in the parish registers into actual births and deaths. Wrigley and Schofield (1989)
spend no less than five chapters in describing their methodology about this issue. Here
we shall be a bit more parsimonious, however making our best to spell out all the
assumptions which underlie our estimates.
Unfortunately, the earliest complete flow data – that is, data on both births and deaths
– that to our knowledge exists, starts in 1570 (Alvito S. Pedro), although several other
regions follow shortly afterwards. These include Cardanha (1574) and such major
settlements such as Guimarães (1580), Viseu (1587) and Évora (1595). When an
individual series for a given region starts after 1570 or ends before 1864, we use linear
extrapolations, backwards or forwards as appropriate, for that series only, combined
with the actual data for the other series, so that the weighting procedure stays
unaltered through the construction of the final population vector54.
The number of deaths needs to be corrected by child mortality, which is not accounted
for in the official deaths records in the primary sources. The issue here is that while
recorded births correspond here to baptisms, the death of young children was usually
not recorded for children who had not reached the age of confession and anointing of
the sick, seven (Santos 2003, p. 164-5.) In the few cases where the parish registers do
include child mortality, we make no adjustment. Otherwise, we estimated corrected
deaths as “official” deaths plus one third of the average number of births between that
Note this refers to mainland only (the censo gives a population of mainland plus off-shore islands of
4,188,410). See also Rodrigues (2008: 331, 329), who gives the alternative number 3,927,932 for the
mainland.
54 The linear extrapolation is always based on the average growth rate of the 10 closest years for which
we do observe that series. For example, for 1570-1586, Viseu is extrapolated backwards at -0.00114
growth (the 1587-1595 growth rate). Then for 1840-1850, Viseu is extrapolated at .0133 growth (18301839 growth rate).
53
35
year and that which immediately preceded it55. This is a rough estimate based on
information about Guimarães during 1793-1812, as shown in the following table:
TABLE A2
Notice we are here assuming that this distribution is constant for all periods, although
a Malthusian logic would suggest the child mortality distribution varies with incomes
– both for “positive check” reasons, since higher incomes lead to more healthy mothers
and children, and occasionally “preventive checks” in the form of infanticide, either
active or by due to passive neglect. Here we unfortunately have no other choice than to
ignore these considerations and assume this distribution is stationary.
Assuming child mortality as about one third is may be too liberal as for other places
higher rates are ofen observed56. We hence also consider the alternative number of 0.4
which corresponds to a higher bound that to our knowledge is observed elsewhere.
A separate issue concerns the underestimation of births. We are implicitly assuming
baptism occurred immediately or shortly after birth since in accordance with Christian
beliefs this would save the child’s soul from original sin should death occur early on.
While this was the twentieth-century practice, for earlier periods transportation
difficulties were more of an issue, and we do know that at least for one region, 3% of
children died before baptism (Alves 1986, p. 135), and while for most other regions
quantitative data is lacking, we do know that newborns who died before baptism were
not registered (Santos 2003, p. 165.) On the other hand, Costa (1994) claims social
norms forced baptism to happen within a week of birth. We have no way of knowing
how representative each of these pieces of evidence is, and the first certainly seems a
bit high by comparison with what we know about widespread Christian practices.
Hence all considered, except where we have definite evidence to the contrary we have
decided to assume all births are registered and make a correction to deaths only. What
is important is to realize here is that unlike with the underestimation of deaths, here
the total flows are correct since the newborns that died before baptism were not
recorded at all, and hence do not appear in our measure of either births or deaths. The
few cases of known mortality of children under this age, as a percentage of total births
were as shown in table A3.
TABLE A3
The basic assumption is that we make is that the parish information that we have is
representative: we assume data is missing at random. This is both true with regards to
the choice of the particular parishes that we happen to have information about, and the
cases for which we have either missing births or deaths for a few years. So if deaths are
missing a few years for instance, the implicit assumption is that this was due to some
exogenous event such as a priest that happened to be or become lazy for exogenous
This seems to us a better option than using a seven-year average since that would give undue weight
to a year of unusually high births long after the occurrence, while as the table indicates, most deaths
occur in the first few years. It is also the case that the older the child, the most likely would a death
actually be recorded.
56 For period 1672 to 1787 in the parish of S. Pedro de Poiares the same quotient was 0.409 (we did not
collect this data because only moving averages are available), and in the mid-18th century, in the urban
parish of Oliveira it was 0.42 (p. 440). Other known cases are the city of Viseu during 1821-1833 with
0.432 and Vila de Resende for 1775-1792 with 0.376 (Oliveira 2002, p. 310). Finally, for Calvão in Trás
os Montes, between 1857 and 1855 the quotient was 0.354 (Faustino 1998, p. 211).
55
36
reasons, as opposed to a plague which lead to overstraining in resources which caused
registers to stop being updated57. Note, however, that the church keep a quite tight
monitoring system in place and this is reflected in the fact that missing data is quite
rare.
It is to be noticed that while Wrigley and Schoelfield (1989) notice their data includes
frequent omissions, such occurrences are rare, if occasional, in the Portuguese case.
The quality of the parish registers data is ensured by strict monitoring system known
as “visitations” (visitações). These were a form of church monitoring over the entire
religious life of each community, including the behavior of the priests. A delegate of
the bishop would visit each locality in person, and would make sure, among other
things, that the priest was keeping with his contractual obligations to his employer. A
particular vivid description is given by (Faustino 1998, p. 68-74). For our purposes the
important point is that the local priest was obliged to register baptisms and deaths, the
former immediately after occurrence, and in fact would be punished by the church
bureaucracy should he fail to do so. These punishments were enforced. There are cases
for which the frequency of these is known, and these were done randomly and on a
triennial, biannual or even annual basis (Faustino 1998, p. 69).
A final issue concerns emigration, about which we have no direct information. Census
and census-type information about stocks serve as checks which anchor the estimated
quantities based on flows. We estimate unobserved emigration flows residually as the
difference between the observed population stock at one period and the estimate which
would be suggested by sequentially adding the difference between births and corrected
deaths only. For instance, we know 10465 people lived in Guimarães in 1706. If we
then sequentially add corrected net population growth to this number, we eventually
reach 13755 people in 1758. However, the census-type data we have for that period
reveals only 8066 people lived there at that date. The difference corresponds to
outflows due to emigration, in particular during the period of the gold rush to Brazil
during the eighteenth century. When possible, we cross-check that the result is
broadly in line with the masculinity ratio – the ratio of men to women at a given
moment – since it is always disproportionality young men that emigrate.
In some cases, information about births is available much earlier than that about
deaths. In these cases, we have simply ignored this information and started the series
when both are available. However, earlier information about births still presents useful
information on the Malthusian preventive check – by far the most important one, at
least in an European context (see, for instance, Nicolini 2007) – even in the absence of
analogous information about deaths, but we leave the investigation of this for future
research.
We also need information on stocks, which serve as both initial conditions and
subsequent checks that the sequence of flows makes sense with the next available
information (small differences can be accounted for by data recording noise and
especially, migration flows). Information about stocks refers to beginning of period
quantities, and it is calculated as follows: if in a given year census-type information,
use this58; if not, recursively add or subtract net population growth flows since the
If births or deaths are missing in a given year, we extrapolate from an un-weighted average of the
corresponding 5 surrounding years.
58 An unavoidable bias is introduced here since for those few years for which the stock info is based on
censuses or censuses-type information, it necessarily refers to the number of people who lived at an
57
37
closest stock information. This necessarily implies that if the latest information about
flows we have is for year T, we are able to calculate the stock of T+1.
When a series starts later than 1570 – as they usually do – there are two reasonable
options at hand. We pursue both and compare the results. The first option is to drop
each early-ending or late-starting series altogether for the periods which it does not
cover, while correspondingly increasing the weight of the regions which are available
for those periods. The cost is an overestimation of the true aggregate variation, as the
aggregate estimate is based on thinner empirical support in which local stochastic
variation is implicitly being projected into the national estimates.
There is an alternative which is to extrapolate growth backwards, either using linear
interpolation between the latest available date for which the population stock can be
calculated and the 1527-32 numeramento (while stopping at 1570) or, when there is no
sufficiently detailed numeramento information available for that particular region,
instead use the annual growth rate of the closest say 10-year period. For instance, flow
information about Guimarães is only available after 1580, and it is also not possible to
know the stock during 1527-32. So for 1570-1579 we extrapolate backwards at 0.0204
annual growth (the 1580-1589 growth rate.) This often, though much less usually, also
sometime happens in the other end of our periodization. Continuing with the example
of Guimarães, the data also stops in 1819, so from then onwards to 1850 we extrapolate
forward at 0.00547 growth rate (that of 1810-1819).
In this second methodological alternative, the cost is instead an underestimate of the
actual variation in the data, since the data for which yearly data is missing is being
artificially smoothed. All things considered, the second option seems the most
reasonable, as we take it as out benchmark, but we also show the results for the second
method in the robustness section.
A few extra methodological comments are in order. When aggregating the regional
data, are implicitly assuming data is missing at random and hence sample selection is
not much of an issue. This seems sensible in this case. Further, to the extent that some
of the demographic increases may happen in new localities59, it could seem that we are
underestimating population growth. We do however make a residual correction to
flows based on observed population stocks at certain benchmarks. Hence, while
unfortunately we certainly are missing some yearly variation – we are underestimating
flow variance – by definition we do not underestimate total growth.
In no case does the flow data stretch as far back as the 1527-32 numeramento. For most
cases the next time we observe stocks is 1706, and it would be dangerous to simply
calculate stocks backwards using flows – in one case, Viseu, this would even lead to
negative population stocks by the early seventeenth century, and even for those for
which such clear nonsense does not occur, the values as usually too low compared with
those of the numeramento. In order to correct for this we simply linearly extrapolate
between the numeramento and the next available information about stocks, and we
assume that the stock at the moment each series start is that value. For instance, the
unknown, and possibly varying, point of that year – hence conflicting with the general rule that stocks
refer to beginning-of-period quantities.
59 Some people are born in the traditional areas but die elsewhere, sometimes in new locations, often
after having moved there flowing marriage. So emigration, seasonal or otherwise, was not just to the
empire, but also to other parts of the country.
38
series for Esmeriz starts in 1597. Since linear interpolation between 1532 and 1706
leads to a value of 170 individuals in 1597, this is the value we start with when
applying flows from that year onwards.
A1.3. Description of the regional data
Continental Portugal is traditionally divided in six provinces: Entre-Douro-e-Minho,
Trás-os-Montes, Beira (which is sometimes subdivided in Beira Interior and Beira
Litoral), Estremadura, Alentejo (also known as Entre-Tejo-e-Odiana) and Algarve. We
have data for all these regions. We are firmly focused on mainland Portugal; hence we
do not cover the demographic behavior of the Atlantic islands or any other parts of the
Portuguese Empire. We do consider emigration, including to regions of the empire, as
outlined below.
In total, we use information from 57 parishes.60 They vary in size from relatively
small, especially in the earlier periods, in which some have just a few hundred
individuals, to large according to the standards of the time – in particular, Lisbon. We
hence have information about both rural and urban areas. In some cases,
corresponding to the urban areas, the corresponding parishes are aggregated into a
single point estimate which we take as representative of that urban settlement. This is
the case of Guimarães (10 parishes) and Évora (13). In the case of Viseu, information
about six surrounding parishes to the city is used to complement missing data about
the city for certain periods, as described in detail below, but is not used
independently61.
While we use information about 57 parishes, unfortunately the vast majority do not
cover the entire 1570-1864 period, so as would be expected our information for the
later periods is the most solid. However, only slightly less than half the parishes do
start no later than during the final years of the sixteenth century. In several cases,
there is earlier incomplete information, for instance, we may know births but not
deaths. With some notable exceptions, as outlined below, for which a few imputations
on either births or deaths were made as detailed below, we have simply started at the
date when flow data begins. While we do not have data on Lisbon or Porto – just as
Wrigley and Schofield (1989) do not have London – we do have other major urban
centers such as Aveiro, Coimbra, Guarda and Vila do Conde. Finally, the sources for the
regional shares used in the aggregation are Rodrigues (2008, p.177 and 257, based on
Serrão 1993) and Matos and Marques (2002, p.17)
A1.3.1. Entre-Douro-e-Minho
Entre-Douro-e-Minho was the most populous part of the country for the entire period
under study. We use seven sources to cover this important region.
Alvito S. Pedro (1570-1864)
From a total of about 4,100 in 1801 (Silveira 1801). From the late sixteenth century onward our
sample already corresponds to a significative percentage of the total population; in 1590 we have 1.8%,
and this rises to
2.3% by 1700. By 1800 it was 1.6% and it was down to 0.7% by 1850.
61 For most of the parishes in our sample there is no missing data between the endpoints which apply to
that parish but Viseu is the case where the problem of missing data within the period is more severe.
60
39
This first source we discuss covers the region of Alvito S. Pedro and surrounding areas.
This is the most complete series we have, running for the entire period under study.
Alvito S. Pedro is also one of the regions that we know the approximate population of
1527-32 from the census, so there is less danger of retrospective cumulative error in
backwards calculation of population stocks from flows. In the 1864 censo info, the
original Alvito S. Pedro has been measured together with a locality called Gizo, which
is one of the original surrounding areas as well (it is included in the flow information
too.)
It is to be noticed that Alvito S. Pedro has an unusual demographic behavior in the
first half of the eighteenth century, as it shows net population growth during this
period, and of a considerable magnitude given the size of the locality. Notice, however,
that this would be even stronger without the emigration correction that we have made,
so there is no contradiction with the evidence of elsewhere of high levels of emigration
to Brazil during this period. In other words, the actual (gross) population growth is
even stronger than that suggested by the growth in the number of hearths from 40 in
1706 to 300 in 1758. What is comparatively unusual about this location is hence that
population growth was such that even high levels of immigration were not sufficient to
prevent net growth (see Palma and Reis 2004 for further details).
Guimarães (1580-1819)
Guimarães is a city, represented by ten surrounding parishes of Fermentões, Costa, Mesão
Frio, Urgeses, Oliveira, S. Sebastião, S. Paio, Castelo, Creixomil, Azurem.
From the 1527-32 numeramento, we know the population of four of these. We are
therefore missing six, which we calculate assuming regional proportionality with those
we observe, using the 1801 census, which again reveals the regional weights (Amorim,
Norberta 19XX, p.476.) It can be observed that the relative weights of those we
observe in 1527-32 have not changed much by 1801, which lends some credibility to
the exercise, though endogenous selection could be an issue which we have no way to
fully rule out. We hence arrive to a total of 1619 hearths, or 6480 inhabitants.
The earliest observed population stock information we have is 1706. We simply
calculate stocks backwards using flow information and the usual child mortality
correction for deaths. Flow information on births and deaths starts in 1580, but for the
first few years there are missing data problems for some of the underlying parishes,
especially regarding births. When this is the case, we use five-year moving averages,
as indicated in the data file62. Most series have started by the early seventeenth
century, however, and after 1625 all but one, Castelo, are complete without
interruptions until the late eighteenth century, when a few additional years of missing
data occur for three of the ten parishes.
S. João Baptista de Canelas (1589-1808)
62
We realize our assumptions do artificially reduce the true variation in the data. However, this is made
less serious by the fact that ultimately Guimarães only has so much weight in the aggregate, that
missing data is more frequent in deaths while in pre-modern economies the key Maltusian check is the
preventive one (Nicolini 2007) and most importantly, that if we apply the same procedure for the
periods in which we do observe the variable of interest (births or deaths), the results do not change
much, though it certainly is the case that the fit shows variation depending on the region, period, and
distance to the nearest observed data.
40
This is a small parish which today belongs to the Vila Nova de Gaia municipality, but
until the eighteenth century belonged to the Porto municipality. The source which we
use is Costa (1994), who also indicates population stock numbers in page 318.
S. Martinho de Ávidos (1623-1864)
This is a rural region, which had a hearth count of 60 (240 individuals) in 1527-32 and,
according to our estimates, had about 350 individuals at the time the parish-level
source starts, 1623. The secondary source we used for the flows is Paiva (2001). There
is missing data on deaths 1848-60, which we have filled recursively using five year
moving averages over the closest available five year periods.
S. Pedro de Esmeriz (1597-1864)
The source for both flows and stocks is Soares (1987). Some deaths (and once, births)
are missing, and for these we have simply assumed a zero. This is quite likely the truth
as during the entire 268 series not once a zero is observed for either births or deaths,
despite the fact that 1’s and 2’s are frequent.
S. Tiago de Bougado (Trofa) (1650-1849)
This is a rural region, which had a hearth count of 521 in 1620 (Alves 1986, p. 65).
The secondary source we used for the flows is Alves (1986).
Emigration, calculated by the residual method, yields an average of 8 emigrants per
year between 1706 and 1744, 7 per year between 1744 and 1758, and 6 per year
between 1758 and 1794. This is broadly in line with the known masculinity ratios of
0.72 for 1744, 0.84 for 1765, and 0.88 for 1780 (Alves 1986, p. 88).
S. Tiago de Lordelo (1624-1864)
To be completed.
S. Tiago de Ronfe (1651-1864)
The source for this region is Scott (1999).
S. João Batista de Vila do Conde (1595-1640)
The source for this region is Polónia (1999)63. This is an area which had about 3600
individuals during the 1527-32 numeramento and showed moderate growth during the
first half of the sixteenth century.
A.1.3.2. Trás-os-Montes
This region is represented by Cardanha and Rebordãos. Both are rural parishes, but this
is appropriate as this is a predominantly rural region.
This data is avaliable from the Vila do Conde archive (Arquivo Municipal de Vila do Conde),
under the CEDOPORMAR group.
63
41
Cardanha (1574-1801)
Deaths for 1633-1651 are missing. These are filled by recursively applying five-year
moving averages starting at the closest observation. Since estimated population
numbers are close to the stocks at benchmark periods in which they are observed,
there was calculate emigration flows.
Rebordãos (1610-1800)
To be completed.
A.1.3.3. Beira
Here we have Viseu as representative of Beira Interior plus Tocha and Eixo e Oliveirinha
as representative of Beira Litoral.
Aveiro (1695-1815)
Aveiro starts with just one parish in 1527-32, but this is later divided into four, and by
1835 reduced to two only. Our data, both about stocks and flows comes from Amorim
(1996), who in addition to the flows provides fiscal data which provides the stocks at
several benchmarks. In addition to the usual benchmarks and sources we have used
these benchmarks for 1575, 1685, 1721, 1732.
In the baseline estimates we did not use the 1706 benchmark from Carvalho da Costa’s
Coreografia of 2700 hearths due to the proximity with the 1685 and 1721 benchmarks
from which they differ in an economically significant way, being almost half that value.
It is well known that the Coreografia’s estimates are often exaggerated (see Santos
2003, p. 173, and the critical literature cited there.) Still, and also for consistency with
most of the other region in which we have no alternative sensible choice other than to
use the Coreografia’s estimates, we additionally provide alternative estimates for Aveiro
in which this 1706 benchmark is used.
Viseu (1587-1840)
In order to obtain flows, we use parish records for the city of Viseu and surrounding
areas. Because the information for the surrounding areas exhibits severe missing data
problems, we only use the parish of Viseu Ocidental and Viseu Oriental after 1700 and Sé
de Viseu before this date. Despite the change in nomenclature, in 1700 the two parishes
display the exact same figures for births and deaths, which strongly suggest they did
coincide. The full series also shows clear signs of continuity. The source for Viseu prior
to 1700 is Oliveira (1990) and after 1700, Oliveira (2002). This definition of intra-walls
Viseu with the Sé de Viseu parish is confirmed by the 1864 census, by which time these
denominations had not changed.
We also need information on stocks, and for 1527-32, as well as thereafter, we use the
correspondent amounts only – it is important to notice that this corresponds not just
to the whole of intra-walls Viseu, 354 hearths, but also people who lived outside the
city walls but were registered within that parish for the purposes of church registers.
This raises the number to 459.
42
In 1758, the town of Viseu had 6.256 inhabitants according to the memorias paroquiais
(Capela, 2010). They belonged to 1744 households, as shown by manuscript tax
records and by the MP. Of these households, 469 were in the urban centre and 1275
were in surrounding areas (arrabaldes), and were therefore rural in nature. This 25/75
distribution is somewhat different from that implicit in the urbanization rate for the
entire country at the time, i.e. 18/82, but not immensely so. The county (concelho) of
Viseu, with an area of 504 km2, had, at the same date, a population of about 22,000. In
addition to the town of Viseu, it had a “rural” component (termo) which comprised 32
other parishes, with a total population of 15,744. Among them were Santos Evos,
Fragosela, Lourosa, Vila Chã de Sá and Mundão, the total population of which came to
2369 in the same year, i.e. 10,77 percent of that of the county.
As for the issue of under registration in parish registers, Oliveira (1990) informs us
that new born which died without being baptized were nor registered and children
under the age of 7 were also not registered (p. 68). More worryingly, we are told that
at certain times of epidemics, there was under-registration of deaths because priests
could not cope with this function (69). This seems not to have been the case for the
Napoleonic wars, however.64 We have made no attempt to correct for this, other than
residually so that the next stock matches, in a way similar and not distinguishable to
the emigration correction.
It is to be noticed that Oliveira (2002) reiterates the point that deaths of children
under 7 were only very sporadically considered (p. 309). Even in the 19th century,
registration of deaths of children under 7 was only detected in some parishes.
Tocha (1812-1864)
While we have flow data on Tocha exists since the late seventeenth century, it does not
appear in the Memórias Paroquiais and it seems Tocha did not exist as such since the
early nineteenth century. The evidence indicates that until the mid-seventeenth
century, Tocha may have been an inhabited or wild place. This means we can only use
information about Tocha for the nineteenth century.
Eixo e Oliveirinha (1666-1864)
For Eixo e Oliveirinha, we have specific information about child mortality. Birth
records include everyone but children who died under 7 were only included in deaths
after 1850. So after that date we make no correction. For 1850-1900 the proportion of
these relative to total deaths is 26.8% (Ferreira 2001, p. 55, 74, 180-2), and for this
region we project this number backwards – this is the correction to deaths that we
make, instead of the usual formula as outlined above.
Coimbra (1527-1708)
While this data goes far back in time, fluctuations need to be interpreted with care due
to the special academic nature of the city. We have the numeramento stock, which
applies to 1527 (excluding the anomalous concentration of clergy; see Oliveira 1971, p.
147).
The anomalously high number of deaths during 1810-11 is due to the wars associated with
the Napoleonic invasions.
64
43
We have 1567, 1605 and 1617 as stocks, but it should be primary origin is of fiscal
nature, and hence different from the outer counts which are based on counting the
number or hearths. There are no disaggregated values for 1706 but we do have most
of these for 1721. There is here a large increase relative to 1616, but this happens
again between 1758 and 1798. We accept this as historically accurate due to the special
status of Coimbra which leads to fluctuations related to the fortunes of the university.
This may be especially true in the earlier part of the sixteenth century, but may apply
to other periods as well.
Sometimes, for brief periods of time, flow data is missing. The assumptions which we
made about unobserved data we are follows. For baptisms, in the initial periods, we
assumed proportionality of the unobserved relative to 5-year average of the nearest
observations. This happens in the beginning of the sample for some parishes and
otherwise only occurs three times. The exception is the parish of S. Cristóvão, which
after 1683 totally disappears, being that after that date it is simply extrapolated at the
nearest 5-year average rule. As for deaths, we have calculated the ratio of deaths to
births as observed in the first 10 years for which they are observed. This number
varies between 30 and 70% depending on the parish. Then we assumed the same ratio
for the unobserved periods, for the corresponding parish.
Almalaguês (1560-1864)
The parish of Almalaguês includes surrounding locations in addition to the location
called Almalaguês proper. The numeramento of 1527-32 discriminates the population of
these in detail. At this time, Almalaguês is 47.8% of the total of the parish.
Our source for flows is Oliveira (19XX, p. 197). As far as stocks are concerned, our
source for 1706 only gives the population of Almalaguês proper (136 heathrs) but not
all the surrounding locations correspondent to those of 1527-3265. We hence assume
its relative weitgh stayed unchanged and reach the number of 284 hearths, that is 1136
individuals. This implies a growth of 58% between 1706 and 1758, which seems
reasonable. Another source (Bandos 1968) additionally provides a stock for 1647, 250
hearths, which we accept despite being suspiciously round; instead ignoring this
additional benchmark and proceeding as usual only changes the result marginally (see
Palma and Reis 2014 for the graph).
A.1.3.4. Estremadura
Ventosa (1558-1835)
To be completed.
Barcarena (1620-1864)
Carvalho da Costa attributes 136 vizinhos the parish correspondent to Almalaguês, Bera, Torre de Bera,
Monte de Bera, Rio de Galinhas, Monforte, Ribeira de Flor da Rosa, Ribeira de Bera, and other surrounding
farms. In 1527-32, we can find Almalaguês (54 vizinhos), but also Rio de Galinhas (25 vizinhos) and Bera
(34 vizinhos). It seems Bera may have broken up into Torre, Monte and Ribeira and the remaining
locations Carvalho da Costa could also reflect the split which occurred in the intervening period of
almost two centuries.
65
44
While flows are avaliable since 1588, the absence of any information about stocks in
the numeramento means that we are only able to start in 1620, at which point we do
have such information, from taken from d’Oliveira (1620). For 1801, the source for the
stock is Silveira (2001).
A.1.3.5. Alentejo
Évora (1568-1851)
Alentejo is represented by 13 parishes around Évora. It is one of our regions with the
largest population at the beginning of the sample but it is also one of the slowest
growing throughout our period. We have yearly flow data for 1595-1850 taken from
Santos (2003), which we further complement with an extension based on one of those
parishes, Divor, only66. (For the period after 1595 Divor has a roughly similar
behaviour when compared with the average of the other parishes).
For the region of Évora we often know deaths for children under 7 years old. We know
this for 71% of the sample, but unfortunately, missing data is largely concentrated in
the early parts of the sample67.
In aggregating the information presented by Santos (2003) for different parishes
around Évora into a single point estimate for this area, we used the following
methodology. First, we took great care to ensure we are comparing the same
geographical units over time. This meant we could only use the 13 parishes for which
Santos (2003) presents flow data and had to make sure the population stocks at the
several benchmark periods corresponded to these units68.
For 1527-32, the source only gives us the entire Évora region, which included many
counties and parishes in addition to those for which we have flow information from
parish registers. We hence calculate the percentage of our 13 parishes using the next
available and usable, that is sufficiently disaggregated, information, 1720. We then
calculate the 1527-32 stock under the assumption of proportionality. Based on the
We thank Carlota Santos for this suggestion. The primary data is available at www.ghp.ics.uminho.pt
For the parish where this information starts sooner, Nossa Senhora da Boa Fé, it is given since the mid17th century, and for some parishes only about a century later. We do often have deaths for people over
the age of 7 (we have this for about 90% of the sample), so when only the deaths <7 years old are
missing we simply use the same methodology as for the other regions: we assume these represent 0.32%
of the total births of that year, in accordance with the distribution of Amorim (1987). For the 10% of the
sample which remains, we always have info for some other parish, so we simply extrapolate from those.
While this certainly leads to some loss of representativeness, we are able to show that if we didn’t know
this info for later periods (when we do), the hypothesis which we are using for the earlier periods – and
in fact for other regions – that deaths of children under 7 years old represent 0.32% of the number of
births that year, holds well. For instance for the Nossa Senhora da Boa Fé parish, which is that for which
we have the most complete sample, the actual number of dead which fulfill that condition for the entire
period is 872, while the estimate based on 0.32% of births would predict 780; the true average is 4.5 per
year while the estimate predicts 3.8, and the median is 4 while the estimate predicts 3.84. Only standard
deviation is significantly higher in the estimate case (3.8 for the sample versus predicted 1.36); this was
to be expected since as is well due due to families’ conscious fertility decisions births are less volatile
than deaths. All of this is suggestive evidence that this rule is also working well for the earlier periods
when we do not observe deaths of children under 7.
68 The units are the following. From the termo of Arraiolos: Igrejinha. From the termo de Évora (proper):
Abóbada, Boa Fé, Divor, S. Miguel de Machede, Matias, Pigeiro, Regedouro, Torre de Coelheiros. From the
Termo of Évora Monte: S. Maria, S.Pedro. Finally, from the termo of Portel: Monte do Trigo.
66
67
45
information of Santos (2003, p. 188), the weight our parishes in the 1720 total was
.115, and under our assumption this implies a population of 869 for 1527-32.
Notice we have elected not to use the 1700/1706 Corografia stocks in this case. Santos
(2003) argues they are unreliable – all figures are suspiciously presented as rounded
numbers, are non-credibly high in comparison with surrounding benchmarks which
provide an independent checks. But in any case using them would require a method
similar to that of 1527-32, as they too are not presented in sufficiently disaggregated
for our purposes.
Second, because for Évora we have several benchmarks during the eighteenth century,
it is worth to take a close look at estimated emigration numbers. These numbers are
well squared with the narrative evidence regarding the timing of the Brazilian gold
rush, and are suggestive that while most emigration there happened from the EntreDouro-e-Minho region, other parts of Portugal also experienced this phenomenon in a
reasonable scale. There were an average of 170 emigrants per year during 1593-1700,
out of a population of about 17,600 and 22,800, respectively, while the emigration
numbers shoot up to 487 per year between that date and 1720, when population had
fell to about 18,100. Emigration then slows down to an average of 234 individuals per
year until 1732, when we again observe a benchmark on the population stock, about
18,500 individuals. It further stabilizes in absolute terms, but falls in relative terms, to
236 individuals per year between that date and 1798, when population was 21,092.
During the ninetieth century, it rises slightly in absolute terms but keeps falling,
though not by much, in relative terms.
For the 1706 stock, Santos (2003, p. 188) does not discriminate the stocks by parish,
and he also attributes the true date to c. 1700. We hence have looked at the original
Corografia document and to arrive to our number. For 1820-8 we took the Santo’s
stock for out parishes as representative of the 1824 value; similarly for 1838-40 we
used as 1839. For 1587-1594, Évora is extrapolated backwards at the 1595-1604
growth rate (.0051). It is to be noticed unlike for most other parts of the country the
demographic growth starts immediately after 1713, the year the war of Spanish
succession ends.
A.1.4.6. Algarve
Conceição de Tavira (1766-1864)
The Algarve is here represented by Conceição de Tavira. Unfortunately, information
about deaths is only available starting 1766, despite the fact that births are available
since 1687. The stock of 1837 comes from Lopes (1841).
Summary of regional population
In figure 6, we show the resulting population estimates for each region.
A1.4. Annual estimates of the Portuguese population, 1527-1864
In table A1, we show the benchmarks several historians have proposed, using
information such as the 1527-32 contagem, the eighteenth century contagens, and the
1801 census.
46
TABLE A3 HERE
In figure A1, we present the result of our aggregation of the regional data, as well as
the benchmark estimates of table 1. Since we have no parish information prior to 1570,
we cannot observe population flows before this period. We do show in the graph the
population stock which we know from the 1527-32 hearth count, as well as a few
estimates put forward by historians, but we chose not to use data prior to 1570 in our
econometric work since the required interpolations to do so would eliminate the
variation in the data leading to an identification problem. We then can compare the
result of our estimation with the previous guess-estimates of historians. We have
selected those which seem more credible based on the amount and firmness of primary
source information incorporated.
FIGURE A1 HERE
A1.6. Concluding remarks on Portugal’s population figures
While there is some degree of regional variation, as we have documented, this is the
picture which best represents the evolution of Portuguese demography during this
period. Some regional studies have concluded there was a strong demographic increase
in the first half of the eighteenth century followed by reduced growth, stagnation or
even absolute fall during the second half (see Santos 2003 p. 206 and the references
presented there.) We do not find support for this assessment in this study.
A.2. Construction of other variables
A2.1. Climate variables
We consider rainfall in the different stations and temperatures, both relative an
average and in absolute terms. We also build indicator variables for extreme
temperatures. In the development economics literature, extreme weather conditions
are often defined as those above or below a certain percentile of the distribution in
either direction. For instance, Kaur (2009 p.52) writes “a positive shock is defined as
rainfall in the first month of the monsoon above the 80th percentile of the district’s
usual distribution and a drought is defined as rainfall below the 20th percentile of the
district’s usual distribution”. This seems too broad as 40% of the distribution is defined
as extreme weather. We instead take the 15th of either side in the “extreme weather”
variable.
A2.2. The calamity index
In this index we consider calamities other than those that are climate related (at least
directly). Examples include earthquakes and wars. The criteria for index II is less
inclusive and more credibly exogenous (for instance, wars are not included as they
may well have an endogenous component, even in the short run). We construct this
variable based on the narrative of Prices, Wages and Rents (2013).
47
A.3. Econometrics appendix
A.3.1. Specification tests
A.3.1.1. ADF tests
Visual inspection of each of the univariate series suggests that the wage rent ratio and
manufacturing TFP may have trends, and that the land-labor ratio almost certainly
has a trend. Notice, however, than in the model it is the natural logs of the variables
that are used, and as is well known under such conditions trends become attenuated.
The trends may be polynomial and possibly, stochastic (drift). The other variables do
not seem to exhibit any trend behavior. We have performed Augmented Dickey-Fuller
(ADF) tests on all variables (raw data) both for the full sample and for the 1530-1755
and 1756-1850 sub-periods and this led to rejection of the null hypothesis of unit root
both for the trend and the drift (that is, stochastic trend) cases, at the 1% level of
significance, in practically all cases.69
These are firm rejections, further keeping in mind that the ADF test is well known to
have low power to reject the null. Notice ADFT with trend test does not mean we
should necessarily include a trend in the main regressions. Despite the fact that the
null hypothesis of unit root is rejected relative to the alternative of weak stationary
with a (possible) trend, we include a linear trend in the main regressions. For all cases
it is not statistically significant at the standard levels.
A.3.1. Structural break tests
The tests from the previous section may be invalid for any given individual series if a
structural break occurs under the period in question. However, it is known that under
the presence of structural breaks, the ADF test statistics are biased towards not
rejecting unit root nulls; since we have actually rejected these, there is less to worry.
One possibility would is to perform a Chow test at one or more dates chosen a priori.
Choosing 1755 as the break date, we cannot reject a break, as expected from visual
inspection and from the breakdown of columns (3)-(4) versus (5)-(6) of table 3. Since
this choice implies picking out a single year – while the null of might not be rejected
for multiple years – we alternatively use the Quandt likelihood ratio statistic70 in order
to test for parameter stability. This test is designed to detect single and multiple
discrete breaks, as well as a slow evolution of the (population) regression function
(Stock and Watson 2007, p. 567, Hansen 2001). We additionally consider tests which
allow for multiple unknown breakpoints (Bai and Perron 1998). We implement these
sequentially against the null of 0, 1 and 2 breaks, and we allow error distributions to
differ across breaks checkbox to allow for error heterogeneity. The conclusions remain
unchanged.
As visual inspection suggests, the main exception is the land-labor ratio. For this variable it is
possible to reject a drift at 10% significance, but, as for a trend its existence cannot be rejected even at
1% significance. In the case of (the natural log of) per capita GDP and the real wage, if these tests are
separately performed to the data pertaining to 1530-1750 period only, then the existence of a trend
cannot be rejected at the 10% significance level. As discussed in the main text (see the section on an
error-correction model) neither a trend nor a drift can be rejected even at 1% for both per capita GDP
and the population, even at 1%.
70 This is also known as the sup-Wald statistic and corresponds to the maximum of the all the Chow Fstatistics over a rage of inner periods. Here this does not seem very restrictive as the likely break points
do not occur near the end of the sample, and we follow the conventional choice of the truncating 15% of
the sample (48 years) on each side. We hence perform the test for to 1578 to 1802.
69
48
References (of the appendix)
Alves, Jorge Fernandes (1986). S. Tiago do Bougado, 1650-1849, dissertação de
mestrado, Faculdade de Letras Universidade do Porto.
Amorim, Inês(1996). Aveiro e a sua provedoria no séc. XVIII : 1690-1814: Estudo
económico de um espaço histórico. Ph.D. dissertation,Universidade do Porto Amorim,
Amorim, Maria Norberta (1987). Guimarães 1580-1819: Estudo demográfico (Lisboa:
Instituto Nacional de Investigação Científica.
Bai, J. and P. Perron (1998). Estimating and testing linear models with multiple
structural changes. Econometrica 66 (1): pp. 47-48
Bandos, Antonio do Rosário (1968). A freguesia de Almalaguês na 2ª metade do século XVI
(1560-1599). Estudo demográfico. Graduation dissertation, Universidade de Coimbra.
Capela, José Viriato (2010). As freguesias do distrito de Viseu nas memórias paroquiais de
1758 : memórias, história e património (Braga: Minhografe).
Costa, António Carvalho da (1706-12). Corografia portuguesa e descrição topográfica do
famoso reino de Portugal. (Lisboa: Valentim Deslandes, 3 vols).
Costa, Francisco Barbosa (1994) S. João Baptista de Canelas. Uma comunidade rural da
Terra de Santa Maria : Estudo demográfico, 1588-1808. (Vila Nova de Gaia: Afons’eiro
Edições).
Faustino, José A. (1998). Calvão: Uma paróquia rural do Alto Tâmega (1670-1870)
Chaves: NEPS).
Ferreira, Francisco Messias (2001). A antiga freguesia de Eixo e Oliveirinha (1666-1900)
(Aveiro: Câmara Municipal de Aveiro).
Ferreira, Olegário (1989). As crises de mortalidade nas antigas vilas de Alenquer, Aldeia
Galega da Merceana e seus termos nos séculos XVI-XVII. Dissertação de Mestrado ,
dactilografado, F. C. S. H. / U. N. L, Lisboa.
Faustino, José A. (1998). Calvão: Uma paróquia rural do Alto Tâmega (1670-1870)
Chaves: NEPS).
Ferreira, Francisco Messias (2001). A antiga freguesia de Eixo e Oliveirinha (1666-1900)
(Aveiro: Câmara Municipal de Aveiro).
Guiot, Joel and Christophe Corona (2010). Growing season temperatures in Europe
and climate forcings over the past 1400 years. PLoS One 1 5(4). Data avaliable at the
World Climate Centre for Paleoclimatology of NOAA:
www.ncdc.noaa.gov/paleo/recons.html
Greene (2005). Econometric Analysis. Pearson Education, fifth edition.
Hansen, Bruce (2001). The new econometrics of structural change: dating breaks in
the U.S. labor productivity. Journal of Economic Perspectives, 15(4): 117-128.
49
Hespanha, António M. (1992). História de Portugal, IV Volume: O Antigo Regime (16201807). In José Mattoso (ed.) História de Portugal (Lisboa: Círculo de Leitores, vol V).
Hohenberg, Paul M. (1977). Maize in French agriculture. Journal of European Economic
History 6
Janeiro, José Adriano Soares Guerra (1997). Gerações sacrificadas: a população e a
sociedade de S. Tiago de Lordelo, séculos XVII a XX. Master’s dissertation. Universidade
do Minho.
Leite, Odete T. Paiva Silva (1999), S. Martinho de Ávidos: Comunidade rural do Vale
do Ave (demografia e sociedade, 1599-1995). Master’s dissertation, Universidade do
Minho.
Lopes, João Baptista da Silva (1841). Corografia ou memória económica, estatística e
topográfica do Algarve (Lisboa: Academia das Ciências de Lisboa).
Miranda, Fernando António da Silva (1993), Estudo Demográfico de Alvito S. Pedro e
Anexa, 1567-1989. Barcelos: Junta de Freguesia de Alvito S. Pedro.
Nicolini, E. (2007). Was Malthus right? A VAR analysis of economic and demographic
interactions in pre-industrial England. European Review of Economic History 1, pp 99121
Oliveira, António de (1972). A vida económica e social de Coimbra de 1537 a 1640
(Coimbra: Instituto de Alta Cultura).
Oliveira, João Nunes de (1990). A Produção agrícola de Viseu entre 1550 e 1700 (Viseu:
Câmara Municipal de Viseu).
Oliveira, João Nunes de (2002). A Beira Alta de 1700 a 1840: gentes e subsistências (Viseu:
Palimage).
Oliveira, Nicolau de (1620). Livro das Grandezas de Lisboa (Lisboa: Jorge Rodriguez).
Paiva, Odete (2001). S. Martinho de Ávidos. Comunidade rural do Vale do Ave. (Vila Nova
de Famalicão: Câmara Municipal de Vila Nova de Famalião e N.E.P.S.).
Palma, Nuno and Jaime Reis (2014). Portugal’s population 1527-1864: a regional
analysis. Unpublished manuscript.
Pauling, Andreas, Jürg Luterbacher, Carlo Casty, and Heinz Wanner (2006). Five
hundred years of gridded high-resolution precipitation reconstructions over Europe
and the connection to large-scale circulation. Climate Dynamics 26(4): pp. 387-405. Data
avaliable at the World Climate Centre for Paleoclimatology of the U.S. National
Climatic Data Center: www.ncdc.noaa.gov/paleo/recons.html
Pinto, José Antonio de Faria (1996) Estudo Demografico de uma Paroquia Algarvia:
Conceição de Tavira (séculos XVIII-XIX), Master’s dissertation, ISCTE (Lisbon).
50
Polónia, Amélia (1999). Vila do Conde, um porto nortenho na expansão quinhentista.
Ph. D. Dissertation, Faculdade de Letras da Universidade do Porto, 2 vols.
Prices, Wages and Rents (2013). Chronology of Calamities. This source is avaliable at
http://pwr-portugal.ics.ul.pt/?page_id=20
Ribeiro, Orlando (1955). Portugal, in Geografia de Espanã y Portugal, ed. Manuel
Téran, Barcelona: Teide.
Ribeiro, Orlando (1963). Cidade, in Joel Serrão (ed.), Dicionário de História de Portugal,
(Lisboa: Iniciativas Editoriais, vol. 1), 574-580.
Rodrigues, Teresa Ferreira (2008). História da população portuguesa: Das longas
permanências à conquista da modernidade (Porto: Afrontamento).
Santos, Rui (2003). Sociogénese do Latifundismo Moderno: Mercados, Crises e Mudança
Social na Região de Évora, Séculos XVII a XIX. Lisboa: Banco de Portugal
Scott, Ana Sílvia Volpi (1999). Famílias, formas de união e reprodução social no Noroeste
português (Séculos XVIII e XIX). (Guimarães: NEPS- Universidade do Minho).
Serrão, Joaquim V. (1970). A população de Portugal em 1798: O censo de Pina Manique.
Paris, Fundação Calouste Gulbenkian, Centro Cultural Português.
Silveira, Luís Nuno Espinha da (ed.) (2001). Os recenseamentos da população portuguesa de
1801 e 1849. (Lisboa: INE).
Soares, Franquelim Neiva (1987). Monografia de S. Pedro de Esmeriz (Vila Nova de
Famalicão: Câmara Municipal de S. Pedro de Esmeriz).
Stock, James (2007). Introduction to econometrics. Pearson education. Second Editon.
Supreet, Kaur (2009). Nominal Wage Rigidity in Village Labor Markets. Unpublished
manuscript, Columbia University. Avaliable at http://www.supreetkaur.com/research
Wrigley, E. A. and R. S. Schofield [1989 (1981)]. The population history of England
1541-1871 (Cambridge: Cambridge University Press).
51
Tables and figures (of the appendix)
Year
1500
Population (m)
0.906
Source
Castro Henriques
1550
1.35
José V. Serrão, Teresa Rodrigues
1600
1650
1.857
2
José V. Serrão
A. Oliveira, Godinho, Marques
1700
2.3
Rodrigues, R. Magalhães,
Pinto/Madeira
1750
1800
1850
2.359
2.912
3.411
Serrão, Pinto/Madeira;
Serrão, Pinto/Madeira;
M. Costa Leite
Table A1. Benchmark population stock estimates
Total born
221
of which
<1 year
1 to 4 years
years 4 to 6
died <7 total
%
0.16
0.14
0.02
0.33
36
31
5
72
Table A2. Mortality distribution, Guimarães 1793-1812. Source: Amorim (1987)
Child deaths as a
percentage of births
43.2%
Viseu Oriental plus
Viseu Ocidental
Resende
Almeida
Guimarães: Urgeses
Guimarães: Oliveira
Guimarães: urban area
Guimarães: urban area
37.6%
40.5%
32.7%
39.3%
46.4%
46.6%
Period
Source
1821-33
1775-92
1700-1828
1793-1819
(Amorim, 1987, p. 278)
(Amorim, 1987, p. 279)
1650-1719
1710-1760
Table A3. Known child mortality rates
(Newey and West
1989)
0.75T1/3
(Stock and
Watson
2007, p. 607)
T1/4
(Greene 2003, p.200).
5
5
4
4(T/100)2/9
Main
specification,
T=321
any T
Wooldridge (2009, p.
429)
1 or 2 (for annual
data)
Table A4. Choice of the number of autocorrelation truncation parameters to be used in
the Newey-West estimation of the variance matrix, where T is the number of
observations.
52
Figure A1. Population flows of Guimarães
Figure A2. Portugal’s population, national estimates
53
Figure A3. Average rainfall data. Negative numbers reflect dry years and conversely
positive numbers reflect wet years. Source: Pauling et al (2006).
Figure A4. Average temperature data. The data is defined as degrees of difference
relative to 20th century data. Source: Guiot and Corona (2010).
Figure A5a. Calamities I (for a definition, see the text). Source: our calculation, based
on Prices, Wages and Rents (2013).
54
Figure A5b. Calamity II (for a definition, see the text). Source: our calculation, based
on Prices, Wages and Rents (2013).
Figure A6. GDP per capita, adjusted and unadjusted for labor supply. Source: see text.
55