1

Perinatal and neonatal death clustering in an Italian sharecropping community in the first

half of the nineteenth century

Francesco Scalone,

Patrizia Agati, Aurora Angeli, Annalisa Donno

Department of Statistical Sciences – University of Bologna

(DRAFT – PLEASE DO NOT QUOTE)

Introduction

Our object was to study the determinants of perinatal and neonatal mortality in the Italian village of

Granarolo from 1900 to 1939, focusing on the phenomenon of high risk mothers and clusters of

stillbirths and neonatal deaths at family level. We considered late fetal life and early neonatal

mortality, assuming stillbirths and deaths in the first weeks of life as perinatal death. We also took

into account late neonatal mortality between 7 and 30 days after childbirths. Causal structures of

neonatal mortality and stillbirths already emerged as broadly similar (Reid 2001), depending on

factors such as maternal age, parity, spacing of births and changes in birth weight distribution

(Waaler 1984). Endogenous factors, such as genetic make-up, congenital malformations,

prematurity and negative circumstances at childbirth determinate perinatal mortality risks.

Nevertheless, perinatal and neonatal variability pattern appears too sensitive to environmental

factors to be entirely related to purely endogenous causes (World Health Organization 2006)

A definition of infant death clustering can be based on the concentration of deaths within some

families or on the number of women who have lost more than one child, the so-called high-risk

mothers (Edvinsson and Janssens 2012). Among historical demographers, the notion that infant

deaths were unevenly distributed among families was first highlighted by Lynch and Greenhouse

(1994). After this seminal paper, other studies focused on this phenomenon demonstrating that

infant mortality was mainly clustered in a restricted number of high-risk families (Edvinsson and

Janssens 2012). Historical research on infant death clustering phenomenon can also be relevant to

2

understand the determinants of high-risk mothers in contemporary populations, better addressing

health interventions for reducing early mortality (Arulampalam and Bhalotra 2006; Zaba and David

1996).

Several previous studies already took into account genetic, bio-demographic and socioeconomic

determinants of infant death clustering. Despite a considerable interest, socio-economic

mechanisms of infant death clustering still need to be further investigated (Edvinsson and Janssens

2012) and less attention has been devoted to the effects of household structures and family

arrangements in determining infant mortality clustering.

From this perspective, sharecroppers’ families living in northern and central Italy in the past offer

some relevant features that make them interesting from the historical and theoretical point of view.

For centuries sharecroppers were pressed by their landowners to have large families in order to

increase the agricultural production (Barbagli 1984). Individuals living in these large, multiple and

multi-generation households could rely on the assistance of co-resident relatives, involving all

aspects of life (Laslett 1988). According to our view, co-residential kinship could also provide

security and support during pregnancy, delivery and neonatal life, mitigating the effects of parental

incompetence (Das Gupta 1997), genetic frailty and bio-demographics disadvantages. In multiple

and extended families, grandmothers, aunts or other older women could assist at childbirth and give

help in case of complications. Therefore, a less frequency of high risk mothers among sharecroppers

is expected.

In the same agricultural system, another group of rural proletarians coexisted. Daily wagers were

the more vulnerable segment of the rural population, since they could not rely on any agrarian

contract and were hired on daily and weekly bases. Given their precarious conditions, they

generally lived in smaller nuclear families. Consequently, we expected to find a higher

concentration of perinatal and neonatal deaths among their families, since in nuclear households

mothers were more isolated and could not rely on the helps of other women and relatives.

3

So our aim was to assess whether or not family context could mitigate the effects of adverse

genetics, endogenous factors and precarious female conditions.

Since birth weights and perinatal mortality were also determined by women’s living standards

(Ward 1993) and female physical labor was associated with a higher number of stillbirths (Reid

2001), high risk mothers are expected to have been more frequently involved in rural agricultural

works. Granarolo represents a relevant study case since women in rural families could frequently

take responsibilities in both house and field works (Ropa and Venturoli 2010). However, because

their privileged position, we hypothesized that sharecropper’s wives could be more protected than

daily wagers’ wives and got assigned less demanding physical tasks during pregnancy.

Traditional determinants often fail to explain why some families were more exposed to infant and

child mortality than others. Because of this unobserved heterogeneity, it is no longer useful for

infant mortality analysis to examine children only as unrelated individuals. Considering infant

deaths as correlated within the same family implies the adoption of multilevel models. So we used a

statistical multivariate method to properly measure amount, shape, and dispersion of death

clustering among mothers of sharecroppers, daily rural wagers and other socioeconomic groups,

controlling for several bio-demographic factors, seasonal effects and different historical periods.

In the next section of this paper, we provide our theoretical framework briefly reviewing the main

literature on both perinatal and neonatal mortality, infant death clustering and the nuclear hardship

hypothesis. Then we present the area under study, the data and the method used. We also list the

covariates in the multivariate analysis, discussing their expected effects. We then present and

discuss the empirical results.

Theoretical framework

Perinatal and neonatal determinants

As stated by World Health Organization (2006) causes and determinants of neonatal deaths and

stillbirths differ from those related to post neonatal and child deaths. Early neonatal deaths occur

4

during the perinatal period, and have obstetric origins, similar to those leading to stillbirths, whereas

infections are the main cause of neonatal death in many countries after the first week of life.

Neonatal deaths and stillbirths derive from poor maternal health, inadequate care and management

of complications during pregnancy and delivery, poor hygiene and lack of newborn care.

Complications during birth, such as obstructed labor and fetal malpresentation, are common causes

of perinatal death in the absence of obstetric care. Harmful practices such as inadequate cord care,

letting the baby stay wet and cold, discarding colostrum and feeding other food can further affect

neonatal survivorship.

Several maternal factors are related to perinatal and neonatal mortality such as women’s status in

society and nutritional state at the time of conception. In early or late childbearing, mother’s age

represents another risk factor. According to the maternal depletion hypothesis (Winkvist et al.

1992), short inter-birth intervals generally impoverish maternal physiological state, increasing

perinatal and neonatal mortality risks (Da Vanzo et al. 2008).

Considering neonatal conditions and characters, babies die immediately after birth since they are

severely malformed, are born very prematurely, suffer from obstetric complications before or

during birth, have difficulty adapting to extrauterine life or because of harmful practices after birth

that lead to infections (World Health Organization 2006).

Causes and mechanisms of infant death clustering

Variations in the distribution of mortality risks for children between families or mothers can be

explained by differentials in bio-demographic, socioeconomic and cultural features (Edvinsson and

Jansen 2012; Zaba and David 1996). Underlining the effects of physiological and demographic

factors, several authors have suggested that maternal depletion could play a main role in increasing

child mortality risks among high-parity mothers (Zaba and David 1996, Miller et al. 1992). It also

appears that the death clustering is related to the frequency with which families experienced

stillbirths (Edvinsson et al. 2005; Reid 2002). Beyond bio-demographic determinants, genetics may

5

also have a role in determining death clustering patterns. Genetic factors are almost impossible to be

measured in historical research, but their relevance emerges by the difficulty in explaining mortality

clustering adopting biological and socioeconomic predictors only (Edvinsson and Jansen 2012).

Even if primary causes of perinatal and neonatal death have an endogenous origin, individual socio-

economic characteristics together with environmental, hygienic and sanitary conditions could exert

an indirect effect, improving or worsening women’s health, maternal nutrition and obstetrical

quality. A negative combination of these factors could contribute in unevenly distributing perinatal

and neonatal deaths at mother level, creating mortality clusters and high risk mothers.

Socioeconomic parents’ characters such as occupation, social class, income, and education

represent other sources of infant death clustering, since they worsen health inequalities (Graham

and Kelly 2004). Wider environmental factors at community, neighborhood and family

environments have to be taken into account as well. In previous studies, Guo (1993) showed that

household income and mother’s educational attainment are important determinants of infant death

clustering at familial level in Guatemala, while Das Gupta (1997) demonstrated that in India

socioeconomic status has a fundamental role, even greater than household income. However, in

some historical studies, after controlling for several bio-demographic factors, household’s socio-

economic status and income do not significantly explain familial component in infant mortality

differences (Lynch and Greenhouse 1994; Janssens et al. 2010).

Breastfeeding behavior, quality of maternal care or parental attitudes could affect children’s health

and might be responsible for the clustered outcomes. The much-debated “maternal incompetence”

is seen as a basic inability to manage domestic affairs, regardless education, income, and occupation

(Das Gupta 1997).

The “nuclear-hardship” hypothesis

In pre-industrial societies, individuals living in nuclear families were less protected than those

living in extended and multiple households. According to the “nuclear-hardship” hypothesis, before

6

the creation of a modern system of welfare and assistance, nuclear families were much more fragile

than today, since their members were more exposed to the consequences of negative events such as

the death of a spouse, unemployment, sickness or senility (Laslett 1988). Co-resident domestic

groups instead provided assistance and security for all members, ensuring the survivorships of

elderly people, widows and orphans. As Peter Laslett (1988) noted, this could be the case of the

Italian sharecroppers that used to live in large-scale, multiple and multi-generation households. In

addition, other studies already demonstrated that household structure could affect the women living

conditions in historical rural societies. Within non-nuclear rural households, women used to activate

both solidarity networks towards children and forms of horizontal solidarity among women

themselves (Palazzi 1990).

In our theoretical framework, material security and psychological support from co-residential

kinship protected newborns and new mothers during pregnancy, at childbirth and in the first periods

of life. They could rely on domestic food resources and consequently be in a better nutritional

status. Moreover, solidarity of relatives could protect pregnant women and new mothers from heavy

agricultural works, replacing them in the fields or not assigning them heavy tasks. Probably female

daily wagers were not in such conditions and could not have similar securities and protections. In

addition, material support from other family members could help mothers and newborns during

delivery. In a multi-generation sharecroppers’ household, older and more competent women could

successfully manage difficult childbirths and avoid harmful practices in childrearing. So a woman

with adverse biological and genetics background living in a sharecropper’s household had more

probability to give birth to a live-born and protect his surviving than a mother in a nuclear family.

Area

Granarolo is a rural village situated about ten kilometers far from Bologna, in Emilia-Romagna, a

northern Italian region. This region comprises the southern part of the Padan Plain and the first hills

of the Apennines Chain. Bologna is geographically located in the central area of Emilia-Romagna

7

and for centuries has been crossed by people travelling for economic, working, and cultural reasons.

The root of Granarolo word is “granary”, suggesting its ancient role as granary of Bologna. Because

of its proximity to the urban area, Granarolo always exchanged goods, agricultural products and

practical knowledge with Bologna.

During the period under analysis, the economy of Granarolo was prevalently rural. At the 1911

census, the majority of household’s heads was employed in agriculture with 47 per cent of farmers

and sharecroppers and 24 per cent of rural daily wagers (Istituto Centrale di Statistica del Regno

d’Italia, 1911). Twenty years later at the 1931 census, agriculture was still the most relevant sector,

counting 47 per cent of sharecroppers and farmers and 21 per cent of rural daily workers (Istituto

Centrale di Statistica del Regno d’Italia, 1937).

Before industrialization, in northern and central Italy, and especially in Emilia-Romagna, the

prevalent agrarian system was based on sharecropping contracts (Bellettini, 1971). Farmers, small

landowners and sharecroppers had quite stable economic conditions, relying on their own properties

or on their sharecropping contracts. Conversely, rural daily wagers could just count on seasonally or

temporary income, without any contractual protection.

For centuries, the economy played a strong role in household settlements, since the sharecropping

system required a constant supply of male workers and therefore a specific family organization.

Under pressure from their landowners and to avoid an imbalance between work force and farm size,

the safest way to guarantee the continuity of the domestic workforce was to live in large-scale

multiple families (Angeli 1983; Barbagli 1984; Poni 1978; Doveri 2000). In the sharecropping

system, household represented the basic unit of production, with a high level of self-sufficiency; all

family members had to work on farm, including women and children generally dealing with less

demanding tasks.

Since children represented the future workforce to be invested in the agricultural activities,

sharecroppers had generally higher fertility levels with respect to other rural workers (Breschi et al.

2014; Kertzer and Hogan 1989; Rettaroli and Scalone 2012). Moreover, higher infant and neonatal

8

survival levels were related to the more substantial family resources and better infant care from co-

resident women (Breschi et al. 2000).

In fact, family members could share material sources, emotional support and practical information

that could make the difference in critical life moments.

Conversely, daily wagers and rural labourers used to live in smaller nuclear families, since they had

no land to cultivate and scarce material sources. With low wages – even lower for women – and an

uncertain number of workdays during the year, wage labourers lived precariously, badly nourished

and clad, in poor housing conditions. High frequency of illness often reduced them to penury (Jacini

1884).

From the beginning of the twentieth century, a rapid development of a large-scale, capitalist

agriculture took place on the north-east plains of Bologna. Sharecroppers’ households contributed

to the creation of an economic system in which farming was combined with work in the industrial

sector (Villani 1989; Cazzola 1985). The new agrarian capitalism implied a progressive

proletarization of the rural workers, still making the daily wagers’ conditions more unstable and

precarious during the period under analysis (Cazzola 1996; Calanca 2004). Despite the substantial

economic, political, and social changes characterizing this period the proportion of sharecroppers

living in the area of Bologna did not significantly decrease and remained quite stable (Kertzer and

Hogan, 1989).

In the first decades of the twentieth century, the Bologna’s area progressively experienced the

impact of the urbanization process, developing manufacturing activities, transport infrastructures

and the sanitary system and attracting growing migration fluxes (Scalone and Del Panta, 2008). As

a consequence, the population of Granarolo increased from about 4.6 thousand in 1901 up to 5.1

thousand in 1921, then remaining stable until the middle of the twentieth century at around 5

thousand inhabitants.

During the first half of the twentieth century, Emilia-Romagna experienced progressive

improvements in survivor conditions: life expectancy at birth for both genders grew from 43 years

9

in 1901 to 68 years in 1951, while infant mortality steady declined because of the reduction in

neonatal mortality (Pozzi 2000). Infant mortality decreased in the region from about 170 per

thousand in 1901-1910 to about 81 per thousand in the 1930s. In the same period the neonatal

mortality rate declined from about 78 per thousand to about 42 per thousand (ISTAT 1975). Both

infant and neonatal mortality were higher in Emilia Romagna with respect to the Italian levels until

around 1920 (Bellettini, 1981); in the following decades the regional rates faster fell, reaching the

Italian levels.

In Granarolo, infant mortality rate declined from 168 per thousand in 1881-1900 to 96 per thousand

in 1921-1940, a lower level than either regional and national averages (Scalone et al. 2013).

In the province of Bologna perinatal mortality rates fluctuated between 50 per thousand and 150 per

thousand during the last decades of the nineteenth century and the beginning of the twentieth

century, to reach a level between 40 per thousand and 80 per thousand in the 1920s, and 20 per

thousand during most of the 1930s (Ward 2004). Perinatal mortality rates - as well as the infant

mortality levels - were lower in the province of Bologna with respect to those registered in the

whole Emilia-Romagna region. Differences were due to better geographic-environmental conditions

(Angeli, Del Panta and Samoggia 1995) and to improvements in obstetrical assistance and infant

care, already advanced in the early nineteenth century (Scalone et al. 2013). The first Italian chair in

obstetrics was instituted in 1804 at the University of Bologna, and obstetric and gynecological

clinics were established in 1860 in two hospitals (Ospedale Sant’Orsola and Ospizio Esposti), thus

favouring the reduction of stillbirths and perinatal risk, also among poor rural women (Ward 2004).

Historical sources referred to nineteenth century record both the attention of hospital pediatricians

towards rural children’s health and the presence of midwives in the area under analysis (Rosa,

Vegetti 2006; Lo Conte 2013). The rising interest towards maternal and child health could also have

protected rural women delivering at home and contributed to lower children early mortality.

Stillbirths rate in 1931 was 29.9 per thousand in Emilia Romagna (versus 34.3 per thousand in

Italy), while the perinatal mortality rate was 53.4 per thousand, almost equal to the Italian rate

10

(ISTAT 1975). In Italy, late-fetal mortality rates only reached credible levels during the early

decades of the twentieth century, after a long process of registration improvements (Del Panta

1997).

Data

Data used for the analysis came from civil births, deaths and marriages registers of the Granarolo

Municipality, available from 1866. The traditional method of family reconstitution (Fleury and

Henry 1956) was applied by preliminary linking births registers to deaths and marriages registers.

For each newborn, death, birth and maternal marriage dates were used to calculate the individuals’

life duration, parity and mother’s age at childbirth. Mortality estimates based on this kind of family

reconstitution are generally biased by unobserved migrations, because of the lack of death dates

when individuals moved and died in another place. However, this bias has generally a limited

impact on perinatal or neonatal mortality measures, since it was not so frequent that a new mother

and her newborn could migrate to another place within a week or a month of delivery.

Nevertheless, for mothers who were born and married outside Granarolo, birth and marriage dates

could remain unknown. Therefore it has been necessary to further search for those mothers in the

1911 and 1931 micro census data to extract their birth date. Since 1931 census data also reported

marriage duration for each married women, other mothers’ dates of marriage were found. However

in a certain quote of childbirths, it was not possible to know mother’s birth and marriage date. In

these cases, maternal age and parity remained unknown.

Stillbirths and immediate neonatal death after childbirth were noted in the birth register, instead of

being reported in the death register. In all these cases, the Italian municipality officers wrote a note

reporting that the newborn was not alive at the moment of the registration without specifying

whether or not he or she was stillbirth or live-born (Breschi et al. 2012). In Granarolo, stillbirths

were frequently reported as “Nato Morto” or “N. M.” in a handwritten note on the border of the

document. As this information was not systematically reported, in some periods the calculated

11

stillbirth rates were too low or suspiciously equal to zero. Considering the date (and hours) of

childbirth reported in the birth registration, it was possible to deduct that these cases could only

concern stillbirths and early neonatal deaths occurred within two days of delivery. Because of that,

we decided to refer this analysis only to perinatal deaths, including stillbirths and early neonatal

deaths, and late neonatal deaths identified with a fair degree of certainty.

The births register also contained name and surname of the newborn, birth’s date and place,

maternity, paternity, parents’ occupations, legitimacy and multiple births. The signature at the end

of the birth registration, made also possible to deduct both the presence and literacy of the father.

We totally considered 4918 childbirths from 1900 to 1939, counting 4827 live-born, 91 stillbirths,

150 deaths within 7 days of delivery and 92 between 7 and 30 days. The perinatal mortality rate

(stillbirths plus death in the first week of life) was equal to 49 per thousand and late neonatal

mortality rates equal to 19.7 per thousand.

Method

Data consisted of n newborns grouped into k families, where a family is defined as a mother and her

children. Each newborn had a vector of covariates, x, whose (fixed) effects could be estimated and

were represented in a vector of coefficients β. The k mutually independent cluster variables, u1, u2,

… uk were not to be estimated, since they were not of interest per se; rather, it was the heterogeneity

between clusters that needed to be estimated. To this aim, a normal distribution was assumed for the

u’s, and parameters characterizing this distribution were used to assess the magnitude of the

unexplained interfamily variation.

Therefore, a generalized linear mixed model (GLMM) – that is, an ordinary logistic regression with

a cluster random effect – was used to estimate both the unexplained interfamily variation

(Holmberg and Broström, 2012) and the effects of the selected predictor variables on the

perinatal/neonatal mortality. In symbols:

12

( ) ( )

( ) i = 1, 2, …, k ; j = 1, 2, …, ni

where Yij is the (dichotomous) response variable associated to the infant j in family i, and the ui’s

are the k random cluster effects, assumed to be independent and identically distributed as Gaussian

with a mean of zero and variance σ2: in symbols, ui N (0, σ

2) i.

Such a kind of hierarchical (or multilevel) model has a number of advantages over the conventional

(fixed-effects) regression technique:

a) it allows to take into account the clustered nature of data (newborns grouped by mothers):

by treating the groups as a random sample from a population of groups, inferences can be

made beyond the groups in the sample;

b) it recognizes the multilevel structure of data and prevent the standard errors of regression

coefficients to be wrongly estimated, leading to an overstatement or understatement of

statistical significance for the coefficients of the covariates.

Traditional measures of variation between clusters in a random-effects logistic regression are:

- the intraclass correlation coefficient computed as proportion of the total variation attributable to

variation between clusters. It does not convey information about heterogeneity between clusters and

it is not a very useful measure when determining whether or not clustering is a relevant factor.

Besides, is not directly comparable with fixed effects measures, usually expressed in terms of

odds ratios;

- the variance (or the standard deviation) of u itself, : but it is quite difficult to interpret, since it is

on the log odds ratio scale.

Due to the interpretational drawbacks just described, results in this paper have been showed also in

terms of median odds ratios (MOR), a new measure of heterogeneity between clusters suggested by

Larsen and Merlo (2005) and directly comparable with fixed-effects odds ratios. The MOR is based

13

on the comparison between two individuals with the same covariate values from two different,

randomly chosen, clusters. The MOR is the median of the odds ratios between the individual of

higher propensity and the individual with a lower propensity across repeated samples. The MOR

was computed as a function of the cluster variance:

MOR = [√ ]

where is the 75th

percentile of the standard normal distribution.

This measure is always greater than or equal to 1: if it is 1, then there is no variation between

clusters; if it is large, then the heterogeneity between clusters is considerable.

For all of models in the analyses Stata 12 was used (Stata Statistical Software: Release12. College

Station, TX: Stata Corp LP).

Variables

Outcome variables

We have examined three different early mortality risk outcomes: perinatal mortality risk (still births

and deaths in the first week of life); late neonatal mortality risk (deaths 7-30 days); risk in the first

month of life (still births and deaths in the first 30 days of life).

As in many historical studies, considering perinatal mortality sidesteps the problems of

distinguishing stillbirth from live births (Ward 2004).

Explicative variables

Sex of the newborn

Several studies on neonatal and infant mortality (Drevenstedt et al. 2008; Pinnelli, Mancini 1997)

highlight that child’s sex contributes in determining the risk of early death: due to biological factors,

male infants have a higher risk of mortality than female.

14

Multiple births

Among historical populations, newborns of a multiple delivery showed higher levels of perinatal

and neonatal mortality (Reid 2001; Wrigley et al. 1997), mainly associated with lower birth-weight

of twins or triplets. Since twins tend to be delivered earlier, they have a smaller size and are

consequently more vulnerable than other children, as birth-weight is one of the most important

factors affecting neonatal survival (Conde-Agudelo, et al 2012; Matteson et al. 1998; Ward 1993;

Reid A., 2001).

Age of the mother

The age of the mother plays a relevant role in perinatal and neonatal mortality and has been widely

discussed in the literature. In young mothers, mother-fetus competition for nutrients and/or maternal

incomplete physical growth might contribute to adverse neonatal outcomes (Kramer and Lancaster,

2010). An advanced mother’s age can be related with other risk factors such as maternal

morbidities, congenital abnormalities and neonatal inability to withstand bouts of infectious disease

(Pozzi, 2002; Carolan and Frankowska, 2011). To take into account higher risks of perinatal and

neonatal mortality at younger and older maternal ages, a five-category variable was included. As

shown in table 1, there was a proportion of mothers whose age at delivery was unknown, because

they were born far from Granarolo.

Parity

Birth order may affect the risk of neonatal mortality. Several studies show a J-shaped effect of

parity, with the probability of infant mortality declining after the first child and increasing again for

four and higher orders (Knodel 1988). During perinatal and neonatal life, the greater vulnerability

of first-born has already been demonstrated (Reid 2001). At higher parities, a large number of living

children may affect parental investment in child rearing and the ability of the mother to care for her

15

newborns may decrease as her energy level declines (Matteson, Burr and Marshall 1998). To

account for the parity effect and to control for the effect of current family size (Lynch and

Greenhouse 1994), a four level categorical variable was introduced: first newborns, second and

third birth order, four and higher birth order. A last category was introduced for unknown parity

childbirths, when mothers arrived in Granarolo after marriage and it was impossible to fully

reconstruct their reproductive histories. This covariate was constructed by considering any previous

childbirth, therefore including previous stillbirths in the parity count.

Birth interval

The length of interpregnancy intervals has been associated with the risk of adverse perinatal

outcomes (Conde-Agudelo et al. 2006).

According to the maternal depletion hypothesis, a short birth interval may negatively affect

maternal and neonatal nutritional status (Erickson 1978; King 2003) and may give mothers

insufficient time to recover from the nutritional burden of pregnancy, compromising their ability to

support fetal growth (Hobcraft et al 1985; Federick and Adekstein 1973; Winkvist et al. 1992).

Moreover, when births are closely spaced the breastfeeding of the newborn is affected by the

overlap of breastfeeding with pregnancy. We specified a three-category variable to take into

account birth intervals lower than 18 months, between18 to 30 months and greater than 30 months

(see table 1).

Maternal History

As suggested by Reid (2001), to investigate the role of the mother’s features in determining the risk

of perinatal and neonatal mortality, a variable representing the maternal reproductive history has

been taken into account.

Basing on the nominative family reconstitution, for each newborn in a family we preliminary

computed the number of mother’s deliveries, the number of previous infant deaths within one year

16

of childbirth as well as the number of previous stillbirth. Then the measure was obtained dividing

the sum of previous infant deaths and stillbirths by the number of children previously born.

This variable could reflect the ability of the mother as the main care-giver, through her health and

child-care competences. It also could represent a proxy of the hygienic level of the household and

of maternal ability in managing household facilities (Reid 2001).

Season

Infant and neonatal mortality in Italy, at least until the beginning of the nineteenth century was

strongly affected by climatic conditions. Several studies on infant mortality in northern and central

Italy (Breschi et al. 2000; Breschi and Livi Bacci 1994) showed a U-profile of the neonatal

mortality by month (or season of birth) and an evident relationship with lowest winter temperatures

(Dalla Zuanna and Rosina 2011; Derosas 2009; Ferrari and Livi Bacci 1985).Therefore we have

also controlled for a seasonal effect, expecting that winter born children had the greatest risk of

neonatal mortality, whereas those born in summer experienced the lowest levels.

Socio-economic Status

We have also taken into account the socio-economic status considering the father’s occupation at

birth. A close connection between socio-economic status and neonatal mortality was expected

(Breschi et al. 2000; Reid 2002; Derosas 2003). The classification scheme distinguished among:

professionals, clericals and sales workers; skilled and low skilled workers; farmers and

sharecroppers; rural daily wagers (including unskilled persons whose job opportunities and earnings

were uncertain and might change daily). A category for unknown socio-economic status was

introduced for illegitimate newborns without father’s indication in the birth registration.

Mother rural status

17

Civil birth registers also reported the mother’s occupation, helping in understanding women’s

conditions during pregnancy. In a rural context, because of the need to contribute to the family

sustenance, it is plausible that working women in agriculture did not stop to work in the fields

throughout pregnancy, thus making the likelihood of adverse perinatal outcomes increase (Reid

2001). To take into account this aspect, we introduced a covariate on mothers’ working status. This

dummy variable distinguished newborns from mothers involved in rural work (daily wagers or

farmers) from those born from mothers not involved in rural works (housekeepers, other women not

involved in rural work). Sharecroppers’ wives registered as housekeepers have been included

among not rural working women.

Father's presence and ability to sign

A categorical variable refers to the father presence at birth’s registration and his ability to sign the

document, further specifying the social status of the father. This information also allowed

identifying newborns whose father was far from home at the delivery moment and probably in the

following days (because of work or war reasons). These newborns were generally declared by the

obstetrician.

A higher mortality risk was expected for newborns whose fathers were illiterate or not present.

Period

Social and economic changes of the first half of the twentieth century improved the living

conditions of the rural population. Relevant progresses in obstetrical techniques and sanitary

organization contributed to reduce infant mortality levels in this rural area (Scalone et al. 2013).

Considering five historical periods 1900-1909, 1910-1914, 1915-1919, 1920-1929, 1930-1939 (see

table 1), we expected a steady continuous decline in mortality risks in the subsequent periods with

the exception of World War I (1915-1918) and the epidemic of Spanish influence (1918-1919).

18

Table 1 - Here

Results

Results of logistic regression analysis are presented in three different models in table 2. Model 1

assumes as dependent variable the risk of perinatal mortality (stillbirths and early deaths within 6

days of delivery); model 2 takes into account the risk of late neonatal mortality (deaths between 7

and 30 days) and model 3 focuses on perinatal and neonatal mortality risk (stillbirths and deaths

within 30 days of delivery).

In model 3, related to the perinatal and neonatal mortality in the first month of life, bio-

demographic effects as multiple birth, sex and maternal age are significant. Parity effect is not

significant, probably because of the high proportion of unknown order childbirths. Considering

model 1 and 2 in more detail, multiple births appear extremely dangerous, since twins register

higher and significant odds ratio (9.3 for perinatal mortality in the first week, decreasing to 6.7 for

late neonatal death). Lower significant female risks are observed for the late neonatal period,

whereas newborns of older mothers significantly experience an increase in perinatal mortality risk

within 6 days after delivery.

As expected, periods affect perinatal mortality in both first week and first month after delivery

(model 1 and 3), with significantly declining risks. Confirming the expected results, higher perinatal

mortality risk is found on winter. Considering late neonatal life (model 2), season effects are not

significant and only the two decades after the first world war show a significant declining odds

ratio.

Turning to the socio-economic determinants, newborns of mother involved in agriculture

experience a 44 per cent higher perinatal mortality risk in the first month of life. With respect to the

socio-economic status of the father, well off and higher status group has a 65 per cent lower

probability of perinatal mortality within 6 days of birth than rural daily wagers (model 1), whereas

sharecroppers and farmers have a 46 per cent significant reduction only in late neonatal mortality

19

risk (model 2). It is likely that well off could rely on better antenatal and obstetrical care in early

perinatal life, whereas multiple sharecroppers’ household could provide better protection during the

late neonatal life. No significant effect results for father’s literacy, whereas “father not present”

category significantly increases the late neonatal mortality risk. The presence of the father could

make the difference only after the first week of life, since it appears less important during and

immediately after childbirth, when bio-demographic determinants prevail.

Looking at the death clustering measures, the median odds ratio of late neonatal model (2) are twice

lower than those calculated for the perinatal model (1). In these terms, the existence of “higher

mortality risk mothers” appears more related to the perinatal period that includes stillbirths and

deaths within 6 days of delivery.

Table 2 - Here

In order to show how perinatal deaths clustering could vary between different social groups, table 3

includes death clustering measures from three separated logistic regression models of perinatal and

neonatal mortality risk in the first month of life, referred to the father profession: sharecroppers and

farmers (model 4), rural daily wagers (model 5) and other workers (model 6).

Table 3 - Here

Sharecroppers and farmers’ median odds ratio (MOR) is clearly lower than that referring to rural

daily wagers and to other workers. This means that mothers in sharecroppers’ and farmers’ families

had more homogenous perinatal mortality risks than mothers of other social groups. According to

these results, it was more likely to find higher risks mothers among daily wagers and other workers

groups, since the median odds ratio between high risk mothers and low risk ones in the

20

sharecroppers and farmers group is almost half of the values calculated for the other groups (1.9

against respectively 3.9 and 3.2). These findings suggest that the multiple sharecroppers’ household

could better help and protect the newborn children of high risk women, confirming the protecting

role of multiple households.

In table 4, the effects of the length of the interval from previous birth and maternal history are

presented including only newborns of second and higher parities and therefore excluding first and

unknown order childbirths.

Table 4 - Here

In late neonatal life from 7 to 30 days after delivery, the length of the interval from previous birth

and maternal history effects are not significant (model 8), whereas they are statistically significant

for mortality risks in the first week (model 7) and in the first month of life (model 9). During the

perinatal period (within 6 days of life), the intervals from previous birth from 18 to 30 months and

longer than 30 months induce a risk of dying lower than the reference interval (46 and 57 per cent

respectively). In addition, the birth interval effect on perinatal and neonatal mortality in the first

month follows a similar pattern.

The median odds ratio almost equal to 1 in model 8, suggests that late neonatal mortality risk for

second and higher order births is homogenous at the mother level. Conversely, when perinatal

mortality in the first week of life (model 7) is considered, clustering of perinatal deaths is 90 per

cent higher than late neonatal mortality.

In table 5 death clustering measures, maternal mortality and previous birth-interval effects are

estimated distinguishing by rural and non-rural female workers (models 10 and 11).

The length of previous birth-interval and maternal history effects follow the same patterns in both

models. However, the effect of maternal history on perinatal death risk is higher (2.2) and

significant among rural female workers, whereas only a birth interval longer than 30 months

21

appears significant among non-rural women. As in model 10 the median odds ratio is almost equal

to 1, there are no evidences of high risk mothers among non-rural working women. Conversely,

higher death clustering at the mother level is observed among women working in agriculture.

Table 5 - Here

In table 6, separated model are estimated by taking into account only childbirths of working

mothers in rural sector and distinguishing respectively between sharecroppers’ and daily wagers

wives. Only second and higher order childbirths have been here considered. As expected, the

highest measures of death clustering are found for daily wagers’ wives that worked in agriculture.

In sharecroppers’ families, measures of perinatal and neonatal mortality clustering are equal to

almost 1 and remain not relevant even for mothers involved in rural works.

Table 5 - Here

Conclusions

In our analysis, the effects of bio-demographic factors on both early perinatal and late neonatal

mortality are confirmed. Significantly higher risks for multiple-births and male newborns have been

found. Moreover maternal age older than 34 years is significantly associated with a higher risk of

perinatal mortality. Parity effect results to be statistically non-significant in both early perinatal

(stillbirths and deaths within 6 days of delivery) and late neonatal ages (deaths between 7 and 30

days of life), probably because of the high proportion of unknown birth orders.

As expected, significant higher perinatal risk has been found in winter. The historical period effect

on perinatal and neonatal mortality in the first month of life significantly declines in the two

decades after World War I.

22

A significant lower risk of death emerges for the well-off group in the early perinatal period,

probably because they could rely on better resources and could also access to better antenatal and

obstetrical care. Sharecroppers instead register the lowest risk during the late neonatal period, when

causes of death related to exogenous factors prevail. It is possible that, in sharecroppers’

households, assistance from other co-resident women could protect newborns by these causes of

deaths related to infectious diseases, maternal competence, and childrearing practices.

Maternal working conditions have significant effects only when stillbirths and neonatal death within

one month of delivery are jointly considered. In this case, newborns of working women in

agriculture show a significant higher mortality risk than newborns from non-working mothers,

confirming the detrimental effects of heavy and physical maternal work. It is possible that those

mothers could not stay far from the work fields for a period before or after delivery.

Father’s ability to sign has no statistically significant effect, probably because signature ability was

not a good proxy for education attainment in the first decades of the nineteenth century, when some

very poor people could just be able to make a simple signature, without possessing more knowledge

and information than illiterate individuals. Nevertheless, late neonatal mortality risk significantly

rises when father was not present, suggesting that the lack of the father support could significantly

affect newborns late neonatal mortality, more frequently related to exogenous causes.

In order to measure the level of perinatal and neonatal death clustering and detect the presence of

high risk women, we included in the logistic regression models a cluster random effect at mother

level and then calculated the median odds ratios. Death clustering is found to be more pronounced

for early perinatal mortality than for late neonatal mortality. In these terms, it appears that death

clustering phenomenon tends to be more related to events linked to delivery. Results obtained by

analyzing a subsample of childbirths with complete maternal reproductive history, show that birth

intervals shorter than 18 months significantly reduce the risk of stillbirth and neonatal death within

6 days of childbirths. We also find that maternal history of previous stillbirths and infant deaths is

significantly associated with higher perinatal mortality risks.

23

Looking at the death clustering measures at the mother level, it is interesting to note that a certain

level of unexplained heterogeneity still remains in all the presented models. According to our

theoretical framework, this unexplained variability should be related to some other unobserved

family characteristics, such as household structures and female conditions. Considering stillbirths

and neonatal deaths within 30 days of delivery, we estimated separated logistic regression models

by father’s profession. Concentration of perinatal deaths at the mother level results lower for

women living in sharecroppers’ families than for those living in rural daily wagers’ families. Since

rural proletarians used to live in nuclear families and sharecroppers in complex households, these

findings highlight the effects of the different household structures on perinatal and neonatal deaths’

clustering. Multiple households were able to reduce risk factors related to specific mother

characteristics, relying on a more stable domestic economy, material resources and reciprocal

supports between family members.

A higher level of perinatal and neonatal clustering has been found among women working in

agriculture. More fragile women in terms of genetics and physiology could be severely harmed by

heavy physical activity with direct consequences on fetal and neonatal health. More interestingly,

maternal activity in agriculture is associated to a higher level of death clustering only for working

women married to rural daily wagers. The combination of poor conditions, worse nutritional state

and necessity to carry out heavy physical work made some women more fragile than others. In case

of complications at childbirth or precarious health conditions of newborns, high risk women could

rely on limited institutional, collective, and familial protections.

Because of their precarious conditions, rural proletarians had to constantly move searching for a

new job, far away from their families. So daily wagers’ wives could count on a limited network of

female solidarity, since their relatives probably lived in other villages. In these terms, our findings

confirm and are coherent with the “nuclear hardship” theoretical paradigm (Laslett 1988).

In the same perspective, no mortality cluster is found among sharecroppers’ wives working in rural

activities, since fragile mothers could more frequently receive help from co-resident women thus

24

reducing the impact of their disadvantage. This help was directly given in terms of obstetrical

assistance at delivery and childrearing by other competent women that already had experienced or

assisted previous childbirths, involving the transmission of knowledge and skills in childcare.

Relatives’ support was also material, related to better nurturing, warm clothes availability and work

organization. It is highly possible that sharecroppers’ wives could avoid more demanding physical

task during pregnancy since their privileged position in the hierarchy of rural activities.

Our results suggest that intergenerational and horizontal solidarity active in large families played a

significant role in explaining perinatal and neonatal mortality patterns and clustering.

25

References

Angeli, Aurora. 1983. Strutture familiari nella pianura e nella montagna bolognese a metà del XIX secolo:

Confronti territoriali [Households structure in the plains and mountains of Bologna in the middle of the XIX

century: Territorial comparisons]. Statistica, 43:727-740.

Angeli, Aurora., Del Panta Lorenzo and Alessandra Samoggia. 1995. Aspetti del regime demografico in

Emilia-Romagna tra XVIII e XIX secolo [Features of the demographic regime in Emilia-Romagna between

XVII and XIX century], in L. Del Panta (a cura di), Le Italie demografiche. Saggi di demografia storica

[Demographic Italies. Historical demography essays], Università degli Studi di Udine, pp. 123-150.

Arulampalam, Wiji and Sonia Bhalotra. 2006. Sibling death clustering in India: State dependence vs

unobserved heterogeneity, Journal of the Royal Statistical Society, Series A 169(4): 829-848.

Barbagli, Marzio. 1984. Sotto lo stesso tetto: Mutamenti della famiglia in Italia dal XV al XX secolo [Under

the same roof: family changes in Italy from XV to XX century]. Bologna: Il Mulino.

Bellettini, Athos. 1971. La popolazione delle campagne bolognesi alla metà del secolo XIX [Bolognese

countryside population in the middle of the XIX century]. Bologna: Zanichelli.

Bellettini, Athos. 1981. Aspetti della transizione demografica italiana nel primo periodo postunitario

[Features of the Italian demographic transition during the first post-unification period], in Studi in onore di

Luigi Del Pane. Bologna: CLUEB, pp. 769-805.

Breschi, Marco and Massimo Livi Bacci. 1994. Le mois de naissance comme facteur de survie des enfants,

Annales de Démographie Historique pp. 169-185.

26

Breschi, Marco, Renzo Derosas and Matteo Manfredini. 2000. Infant mortality in Nineteenth-century Italy:

Interactions between ecology and society, in Tommy Bengtsson, Osamu Saito (eds.), Population and

economy. From hunger to modern economic growth. Oxford: University Press.

Breschi, Marco, Renzo Derosas and Matteo Manfredini. 2004. Mortality and enviroment in three emilian,

tuscan and venetian communities, 1800-1883, in Tommy Bengtsson, Cameron Campbell, James Z. Lee

(eds.), Life under pressure: mortality and living standards in Europe and Asia, 1700-1900. Cambridge: The

Mit Press, pp. 209-251.

Breschi Marco, Massimo Esposito, Stanislao Mazzoni, Lucia Pozzi. 2012. The sardinian experience of the

lowest Italian infant mortality at the turn of the twentieth century. True or false empirical evidence? Annales

de Démographie Historique. 1, 63:94.

Breschi, Marco, Alessio Fornasin, Matteo Manfredini, Lucia Pozzi, Rosella Rettaroli and Francesco Scalone.

2014. Social and economic determinants of reproductive behavior before the fertility decline. The case of six

Italian communities during the nineteenth century, European Journal of Population.

Calanca, Daniela. 2004. Famiglia e famiglie [Family and families], in Sorcinelli Paolo (ed.), Identikit del

Novecento: conflitti, trasformazioni sociali, stili di vita [Identikit of the 20th century: Conflicts, social

changes, lifestyles]. Roma, Donzelli Editore, pp.97-179.

Carolan M., and D. Frankowska. 2011. Advanced maternal age and adverse perinatal outcome: a Review of

the evidence. Midwifery, 27(6):793-801.

Cazzola, Franco. 1985. Le campagne emiliane dall’unità alla prima guerra mondiale. Note storiografiche

[The Italian country sides from the Unification period until the First World War. Historiographical notes],

«Annali dell’Istituto Alcide Cervi», 7/1985. Milano: Bruno Mondadori Editore.

27

Cazzola, Franco. 1996. Storia delle campagne padane dall’Ottocento a oggi [History of the Po country sides

from the 1800s until today]. Milano: Bruno Mondadori Editore.

Conde-Agudelo, Agustin, Anyeli Rosas-Bermúdez, Ana Cecilia Kafury-Goeta. 2006. Birth spacing and risk

of adverse perinatal outcomes. A meta-analysis, JAMA, 95(15):1809-1823.

Conde-Agudelo, Agustín, Anyeli Rosas-Bermudez, Fabio Castaño and Maureen H. Norton. 2012. Effects of

birth spacing on maternal, perinatal, infant, and child health: A systematic review of causal mechanisms.

Studies in Family Planning, 43(2): 93-114.

Dalla Zuanna, Gianpiero and Alessandro Rosina. 2011. An analysis of extremely high 19th

century winter

neonatal mortality in a local context of northeastern Italy, European Journal of Population / Revue

Européenne de Démographie, 27: 33-55.

Das Gupta, M. 1997. Socio-economic status and clustering of child deaths in rural Punjab. Population

Studies. 51(2): 191–202.

DaVanzo, Julie, Lauren Hale, Abdur Razzaque, and Mizanur Rahman. 2008. The effects of pregnancy

spacing on infant and child mortality in Matlab, Bangladesh: How they vary by the type of pregnancy

outcome that began the interval, Population Studies, 62(2): 131-154.

Del Panta Lorenzo. 1997. Infant and child mortality in Italy, eighteenth to twentieth century: long-term

trends and territorial differences, in Alain Bideau, Bertrand Desjardins, and Héctor Pérez Brignoli (eds.),

Infant and Child Mortality in the Past. Oxford: Claredon, pp. 7-21.

Derosas, Renzo. 2003. Watch out for the children! Differential infant mortality of Jews and Catholics in

nineteenth-century Venice. Historical Methods, 36(3): 109-130.

28

Derosas, Renzo. 2009. The joint effect of maternal malnutrition and cold weather on neonatal mortality in

nineteenth-century Venice: An assessment of the hypothermia hypothesis, Population Studies, 63(3): 233-

251.

Doveri, Andrea. 2000. Land, fertility, and family: a selected review of the literature in historical

demography, Genus, LVI (3/4): 19-59.

Drevenstedt, Greg. L., Eileen Crimmins, Sarinnapha Vasunilashorn, and Caleb E. Finch. 2008. The rise and

fall of excess male infant mortality, Proceedings of the national academy of sciences of the United States of

America, 105(13): 5016-5021.

Edvinsson, S., A. Brändström, J. Rogers, and G. Broström. 2005. High-risk families: the unequal distribution

of infant mortality in nineteenth-century Sweden, Population Studies, 59(3): 321–337.

Edvinsson , S., and Janssens , A . 2012. Clustering of deaths in families – infant and child mortality in

historical perspective. Biodemography and Social Biology, 58(2): 75- 86.

Erickson, J. David, and Tor Bjerkedal. 1978. Interpregnancy interval. Association with birth weight,

stillbirth, and neonatal death. Journal of Epidemiology and Community Health, 32: 124-130.

Federick, Jean, and Philippa Adelstein. 1973. Influence of pregnancy spacing on outcome of pregnancy, Br

Med J., 4(5895): 753-756.

Fleury, M., and Henry L. H. 1956. Des registres paroissiaux à l'histoire de la population : Manuel de

dépouillement et d'exploitation de l'état-civil ancien. Paris: INED.

Graham, H., and Kelly M. 2004. Health inequalities: concepts, frameworks & policy. London: Health

Development Agency.

29

Guo, Guang. 1993. Use of sibling data to estimate family mortality effects in Guatemala, Demography 30(1):

15-32.

Hobcraft, J.N., J.W. McDonald, and S.O. Rutstein. 1985. Demographic determinants of infant and early child

mortality. A comparative analysis, Population Studies, 39(3): 363-385.

Holmberg H., and G. Broström. 2012. On statistical methods for clustering: a case study on Infant mortality,

Northern Sweden, 1831-1890. Biodemography and Social Biology, 58: 173-184.

Istituto Centrale di Statistica. 1975. Tendenze evolutive della mortalità infantile in Italia [Infant mortality

evolving trends in Italy], «Annali di statistica», VIII, 29, Roma.

Istituto Centrale di Statistica del Regno d’Italia. 1937. VIII Censimento generale della popolazione. 21 aprile

1936. Provincia di Bologna, 2, 36. Roma: Tipografia Ippolito Failli.

Jacini, Stefano. 1884. I risultati della Inchiesta Agraria (1884). La situazione dell’agricoltura e dei

contadini italiani dopo l’Unità [Results from the agrarian survey (1884) [The situation of agriculture and the

Italian farmers’ conditions after Unification]. Torino: Giulio Einaudi Editore.

Janssens, A., M. Messelink, and A. Need. 2010. Faulty genes or faulty parents? Gender, family and survival

in early and late childhood in the Netherlands, 1860–1900. History of Family, 15:91–108.

Kertzer, David I., and Dennis P. Hogan. 1989. Family, political economy, and demographic change: The

transformation of life in Casalecchio, Italy, 1861–1921. Madison: The University of Wisconsin Press.

King, Janet C. 2003. The risk of maternal nutritional depletion and poor outcomes increases in early or

closely spaced pregnancies. Journal of Nutrition, 133(5):1732S-1736S.

30

Knodel, John E. 1988. Demographic behavior in the past: A study of fourteen German village populations in

the Eighteenth and Nineteenth centuries. Cambridge: Cambridge University Press.

Kramer K.L., and J.B. Lancaster. 2010. Teen motherhood in cross-cultural perspective. Annals of Human

Biology, 37(5):613-628.

Larsen K., and J. Merlo. 2005. Appropriate assessment of neighborhood effects on Individual Health:

Integrating random and fixed effects in Multilevel Logistic Regression, American Journal of Epidemiology,

161(1): 81-88.

Laslett Peter. 1988. Family, kinship and collective as systems of support in pre-industrial Europe:

A consideration of the ‘Nuclear-Hardship’ hypothesis. Continuity and Change, 2, 153–175.

Lynch, Katherine A., and Joel B. Greenhouse. 1994. Risk factors for infant mortality in nineteenth-century

Sweden. Population Studies, 48(1): 117–33.

Lo Conte, Daniela. 2013. Le donne di Granarolo nel Risorgimento [Women in Granarolo during the

Risorgimento period], (http://www.comune.granarolo-dellemilia.bo.it).

Matteson, Donald W., Jeffrey A. Burr, and James R. Marshall. 1998. Infant mortality: A multi-level

analysis of individual and community risk factors. Soc. Sci. Med., 47(11): 1841-1854.

Miller, J. E., J. Trussell, A. R. Pebley, and B. Vaughan. 1992. Birth spacing and child mortality in

Bangladesh and the Philippines. Demography, 29:305–318.

31

Palazzi, Maura. 1990. Famiglia, lavoro e proprietà: le donne nella società contadina fra continuità e

trasformazione [Family, labor and property: women in rural society between continuity and change],Annali

dell’Istituto Alcide Cervi, 12: 25-80.

Pinnelli, Antonella, and Paola Mancini. 1997. Gender Mortality Differences from Birth to Puberty in Italy,

1887-1940, in Carlo A. Corsini, Pier P. Viazzo (eds), The decline of infant and child mortality. The

European experience: 1750-1990, Leiden: Martinus Njhoff Publishers, pp. 73-93.

Poni, Carlo. 1978. Family and “podere” in Emilia Romagna, The Journal of Italian History,1(2): 201-234.

Pozzi, Lucia. 2000. La lotta per la vita. Evoluzione e geografia della sopravvivenza in Italia fra ’800 e ’900

[The fight for life. Evolution and geography of surviving in Italy between 1800 and 1900]. Udine: Forum.

Pozzi, Lucia. 2002. The determinants of infant and childhood mortality: a complex tangle in the historical

research, in Società Italiana di Statistica, Atti della XLI Riunione Scientifica, Milano 5-7 giugno. Padova:

CLEUP, pp. 77-86.

Reid, Alice. 2001. Neonatal mortality and stillbirths in Derbyshire in the early twentieth century. Population

Studies, 55(3): 213-232.

Reid, Alice. 2002. Infant feeding and post-neonatal mortality in Derbyshire, England, in the early twentieth

century. Population Studies, 56(2):151-166.

Rettaroli, Rosella, and Francesco Scalone. 2012. Reproductive behavior during the pre-transitional rural

Bologna. Journal of Interdisciplinary History, 42(4), 615–643.

32

Ropa, Rossella, and Cinzia Venturoli. 2010. Donne e lavoro: Un’identità difficile. Lavoratrici in Emilia-

Romagna (1860-1960) [Women and work: A difficult identity. Female workers in Emilia-Romagna (1860-

1960)]. Bologna: Editrice Compositori.

Rosa, Edoardo, and Alba Vegetti. 2006. Floriano Brazzola (1859-1921): Un accademico a servizio della

sanità pubblica [Floriano Brazzola (1859-1921): an academic at the service of public health], Annali di

Storia delle Università italiane, 10.

Scalone, Francesco and Lorenzo Del Panta. 2008. Urbanizzazione, spopolamento, migrazioni: I comuni della

provincia di Bologna dall'Unità agli ultimi decenni del XX secolo [Urbanization, depopulation, migrations:

The communes in the province of Bologna from the Unification to the last decades of the XX century].

Udine: Forum.

Scalone, Francesco, Patrizia Agati, Aurora Angeli, and Annalisa Donno. 2013. Microanalisi delle tendenze

nella mortalità infantile a Granarolo dell’Emilia tra il XIX e il XX secolo [Micro Analysis in Infant Mortality

Trends in Granarolo between XIX and XX Century] in Marco Breschi and Lucia Pozzi (eds) Mortalità e

stato di salute dalla nascita alla prima adolescenza. Indagini micro in Italia. Secoli XIX-XX. Udine: Forum,

51-79.

Villani, Pasquale. 1989. La pluriattività negli spazi rurali: ricerche a confronto. Introduzione [Pluriactivity in

rural areas: researches comparisons. Introduction]. Annali dell’Istituto Alcide Cervi, 11/1989: 11-22.

Waaler, Hans T., and Göran Starky. 1984. What is the best indicator of health care?, in World Haerlth

Organisation, World Health Forum, 5: 276−279.

Ward, Peter. 1993. Birth weight and economic growth: Women's living standards in the industrializing West.

Chicago: University of Chicago Press.

33

Ward, Peter. 2004. Perinatal mortality in Bologna, 1880-1940, in Marco Breschi and Lucia Pozzi (eds.), The

Determinants of Infant and Child Mortality in Past European Populations, Udine-Sassari: Forum, pp. 213-

230.

Winkvist, A., K.M. Rasmussen, and J.P. Habicht. 1992. A new definition of maternal depletion syndrome.

American Journal of Public Health, 13(5):691–694.

World Health Organization (WHO). 2006. Neonatal and perinatal mortality. Country, regional and global

estimates. France.

Wrigley, E.A., R.S. Davis, J.E. Oeppen, and R.S. Schofield. 1997. English population history from family

reconstitution 1580-1837. Cambridge: Cambridge University Press.

Zaba, Basia, and Patricia H. David. 1996. Fertility and the Distribution of Child Mortality Risk among

Women: An Illustrative Analysis, Population Studies: A Journal of Demography, 50:2, 263-278.

Zenger, Elizabeth. 1993. Siblings’ neonatal mortality risks and birth spacing in Bangladesh. Demography,

30(3): 477- 88.

34

Table 1 Descriptive statistics for variables included in the analysis: percentage distribution and

mean values

Sources: Births, Marriages and Deaths Registers of the Municipality of Granarolo

All parities Second and higher parities

Sex of newborn child (%)

Male (Ref.) 51.00 49.19

Female 49.00 50.81

Multiple births (%)

Single (Ref.) 97.09 97.55

Twins 2.91 2.45

Age of mother (%)

<25 26.25 18.14

25-29 (Ref.) 24.75 33.55

30-34 18.06 25.84

> 34 16.31 22.47

Unknown 14.64 -

Parity (%)

1 30.40 -

2-3 (Ref.) 30.20 68.07

4+ 14.13 31.93

Unknown 25.27 -

Interval since previuos birth (%)

< 18 months (Ref.) - 17.17

18-30 months - 41.30

>30 months - 41.53

Maternal history (mean) - 0.18

Season (%)

Winter (Ref.) 25.58 24.13

Spring 25.93 26.95

Summer 23.49 23.31

Autumn 25.01 25.70

Socio-economic Status (%)

Professionals, clerical and sales 5.49 4.75

Skilled and lower skilled workers 23.02 22.98

Farmers 47.64 50.99

Rural daily wagers (Ref.) 21.00 21.27

Unknown 2.85 -

Mother rural status (%)

Mother not in rural work (Ref.) 40.28 34.24

Mother in rural work 59.72 65.76

Father's ability to sign (%)

Sign (Ref.) 81.76 82.19

No sign 12.10 11.86

Father not present 6.14 5.95

Period (%)

1900-1909 (Ref.) 30.30 26.58

1910-1914 15.43 17.26

1915-1919 10.23 12.55

1920-1929 25.44 26.77

1930-1939 18.61 16.84

No. of childbirths (twins=1) 4,918 2,167

35

Table 2 Logistic regression analysis of the risk of perinatal and neonatal mortality in Granarolo, 1900-1939

Sources: As table 1.

Odds Ratio SE p Odds Ratio SE p Odds Ratio SE p

Sex of newborn child

Male (Ref.) 1.000 - - 1.000 - - 1.000 - -

Female 0.875 0.129 0.367 0.384 0.093 0.000 0.678 0.087 0.003

Multiple births

Single (Ref.) 1.000 - - 1.000 - - 1.000 - -

Twins 9.258 2.660 0.000 6.691 2.441 0.000 9.976 2.575 0.000

Age of mother

<25 1.420 0.324 0.124 1.456 0.521 0.294 1.446 0.287 0.064

25-29 (Ref.) 1.000 - - 1.000 - - 1.000 - -

30-34 1.059 0.266 0.818 1.881 0.677 0.079 1.308 0.277 0.205

> 34 1.769 0.446 0.024 1.298 0.537 0.528 1.634 0.363 0.027

Unknown 1.593 0.571 0.194 1.797 0.851 0.216 1.628 0.493 0.107

Parity

1 1.096 0.218 0.643 1.244 0.382 0.478 1.143 0.197 0.439

2-3 (Ref.) 1.000 - - 1.000 - - 1.000 - -

4+ 1.017 0.260 0.948 0.989 0.397 0.978 1.023 0.228 0.920

Unknown 0.967 0.280 0.907 1.294 0.493 0.498 1.098 0.270 0.703

Season

Winter (Ref.) 1.000 - - 1.000 - - 1.000 - -

Spring 0.641 0.129 0.027 0.570 0.174 0.066 0.595 0.104 0.003

Summer 0.663 0.137 0.046 0.666 0.202 0.180 0.628 0.112 0.009

Autumn 0.601 0.124 0.013 0.651 0.192 0.145 0.591 0.104 0.003

Socio-economic Status

Professionals, clerical and sales 0.341 0.184 0.047 0.886 0.497 0.830 0.498 0.202 0.086

Skilled and lower skilled workers 0.837 0.236 0.529 1.084 0.416 0.833 0.864 0.207 0.542

Sharecroppers and Farmers 0.901 0.181 0.603 0.539 0.150 0.026 0.750 0.130 0.096

Rural daily wagers (Ref.) 1.000 - - 1.000 - - 1.000 - -

Unknown 0.557 0.336 0.333 1.227 0.712 0.725 0.765 0.338 0.545

Mother rural status

Mother not in rural work (Ref.) 1.000 - - 1.000 - - 1.000 - -

Mother in rural work 1.372 0.289 0.133 1.734 0.540 0.077 1.442 0.262 0.044

Father's literacy

Sign (Ref.) 1.000 - - 1.000 - - 1.000 - -

No sign 0.857 0.208 0.527 0.928 0.321 0.830 0.880 0.185 0.542

Father not present 1.282 0.453 0.482 2.216 0.904 0.051 1.619 0.454 0.086

Period

1900-1909 (Ref.) 1.000 - - 1.000 - - 1.000 - -

1910-1914 0.825 0.182 0.383 0.873 0.289 0.681 0.845 0.162 0.381

1915-1919 0.588 0.170 0.067 0.661 0.272 0.315 0.615 0.152 0.049

1920-1929 0.523 0.115 0.003 0.705 0.209 0.237 0.559 0.104 0.002

1930-1939 0.398 0.104 0.000 0.426 0.163 0.026 0.391 0.088 0.000

Sigma u 1.169 0.445 1.047

Rho 0.294 0.057 0.250

MOR 3.053 1.529 2.717

Number of events 241 92 333

Number of childbirths 4,918 4,676 4,918

Number of mothers 2,164 2,127 2,164

Log likelihood -888.1 -416.1 -1122.8

Stillbirths + Deaths 0-6 days Deaths 7-30 days Stillbirths + Deaths 0-30 days

Model 1 Model 2 Model 3

36

Table 3 Sigma u, Rho and MOR from logistic regression analysis of the risk of perinatal and neonatal

mortality by fathers’ professions in Granarolo, 1900-1939. Stillbirths and deaths within 30 days of delivery

are considered

Note: The models also include the following control variables: SES, Mother rural status, Father's literacy,

Age of mother, Parity, Multiple births, Sex of newborn child, Seasonality, and Period.

Sources: As table 1.

Table 4 Logistic regression analysis of the risk of perinatal and neonatal mortality in Granarolo including

interval births, parity and maternal history, 1900-1939. Second and higher parities are considered

Note: The models also include the following control variables: SES, Mother rural status, Father's literacy,

Age of mother, Parity, Multiple births, Sex of newborn child, Seasonality, and Period.

Sources: As table 1.

Model 4 Model 5 Model 6

Sharecroppers and Farmers Rural daily wagers Other workers

Sigma u 0.665 1.450 1.255

Rho 0.118 0.390 0.324

MOR 1.886 3.992 3.312

Number of events 155 93 76

Number of childbirths 2,343 1,033 1,402

Number of mothers 977 506 746

Log likelihood -523.4 -276.6 -265.7

Odds Ratio SE p Odds Ratio SE p Odds Ratio SE p

Interval since previuos birth

< 18 months (Ref.) 1.000 - - 1.000 - - 1.000 - -

18-30 months 0.537 0.150 0.026 0.916 0.435 0.853 0.596 0.142 0.030

>30 months 0.431 0.132 0.006 0.659 0.357 0.442 0.465 0.123 0.004

Maternal history 1.879 0.462 0.010 1.866 0.715 0.104 1.990 0.400 0.001

Sigma u 0.645 0.005 0.007

Rho 0.112 0.000 0.000

MOR 1.851 1.005 1.006

Number of events 101 33 134

Number of childbirths 2,167 2,066 2,167

Number of mothers 950 933 950

Log likelihood -373.8 -152.7 -464.7

Stillbirths + Deaths 0-6 Deaths 7-30 Stillbirths + Deaths 0-30

Model 7 Model 8 Model 9

37

Table 5 Logistic regression analysis of the risk of perinatal and neonatal mortality by mothers' rural status in

Granarolo, 1900-1939. Stillbirths and deaths within 30 days of delivery of second and higher parities are

considered

Note: The models also include the following control variables: SES, Mother rural status, Father's literacy,

Age of mother, Parity, Multiple births, Sex of newborn child, Seasonality, and Period.

Sources: As table 1.

Table 6 Sigma u, Rho and MOR from logistic regression analysis of the risk of perinatal and neonatal

mortality for only childbirths of mothers in rural sector by father’s rural professions. Granarolo, 1900-1939.

Stillbirths and deaths within 30 days of delivery are considered

Note: The models also include the following control variables: SES, Mother rural status, Father's literacy,

Age of mother, Parity, Multiple births, Sex of newborn child, Interval since previous birth, Maternal History,

Seasonality, and Period.

Sources: As table 1.

Odds Ratio SE p Odds Ratio SE p

Interval since previuos birth

< 18 months (Ref.) 1.000 - - 1.000 - -

18-30 months 0.496 0.240 0.148 0.656 0.190 0.146

>30 months 0.243 0.132 0.009 0.560 0.180 0.071

Maternal history 1.360 0.661 0.527 2.188 0.530 0.001

Sigma u 0.003 0.516

Rho 0.000 0.075

MOR 1.003 1.637

Number of events 33 101

Number of childbirths 742 1,425

Number of mothers 437 651

Log likelihood -118.5 -332.4

Model 10 Model 11

Non rural sector Rural sector

Model 12 Model 13

Sharecroppers and Farmers Rural daily wagers

Sigma u 0.160 1.120

Rho 0.008 0.276

MOR 1.165 2.912

Number of events 62 31

Number of childbirths 980 376

Number of mothers 437 177

Log likelihood -211.8 -89.6